In this landscape, traditional segmentation and manual firewall rule management can’t keep up; autonomous microsegmentation is vital for enabling real-time breach containment.  

Gartner’s 2026 Emerging Tech Impact Radar: Global Attack Surface Grid report by analysts Luis Castillo, Charanpal Bhogal, Isy Bangurah, Murat Önder, Kanishka Chauhan, Esha Bhatia, David Senf, Bryan Haley, Evan Zeng, Stacey Yin, and Tom Powledge validates autonomous microsegmentation as one of the most impactful technologies for reducing attack surfaces, with early majority (more than 15% of the target market) adoption anticipated in 3-6 years.

"The impact [of autonomous microsegmentation] will be transformational ... Adopters can streamline policy life cycles, automate draft rule generation, and significantly reduce breach response times." 

- Gartner, Emerging Tech Impact Radar: Global Attack Surface Grid 

What Is Autonomous Microsegmentation?  

Autonomous microsegmentation divides a network into isolated segments that function as individual security zones; these solutions “discover network assets, model traffic telemetry, and dynamically enforce access policies,” according to Gartner.  

While legacy microsegmentation approaches place a heavy manual burden on security teams, autonomous microsegmentation shifts from a “human-in-the-loop” approach to a “human-on-the-loop” system where policy enforcement is automated with staff oversight as needed. 

Autonomous Microsegmentation vs. Legacy Microsegmentation  

Strategies to prevent lateral movement have evolved significantly in the last 30 years. As a new, autonomous generation of microsegmentation takes center stage, it’s important to understand how solutions have progressed from legacy approaches to modern microsegmentation.  

The earliest segmentation attempts divided networks into VLANs and utilized NACs to enforce who could connect. Next came agent-based microsegmentation solutions, which delivered more granular control and flexible enforcement but still faced scalability challenges and security gaps with a heavy reliance on manual work. Finally, next-gen microsegmentation introduced automated, agentless management of native controls – this evolution paved the way for Gartner’s recognition of “autonomous microsegmentation” as an emerging technology. 

Why Security Teams Need Autonomous Microsegmentation Now: Industry Trend Review 

Gartner’s 2025 Hype Cycle for Workload and Network Security placed microsegmentation on the “Slope of Enlightenment” – a rebound from the “Trough of Disillusionment” (where interest wanes and only the most effective solutions survive), signaling early mainstream maturity in the microsegmentation market.  

Meanwhile, CISA’s latest guidance confirms microsegmentation is foundational for Zero Trust, and recent research from Enterprise Management Associates (EMA) reveals that 96% of IT and security leaders consider microsegmentation very or extremely important for cyber defense.  

Still, it’s clear that traditional approaches to microsegmentation have fallen short. Gartner estimates microsegmentation market penetration hovers between 5% and 20%, echoing  a ViB Tech survey that found only 5% of organizations are microsegmenting their networks today.  

In other words, industry insights send a clear message that microsegmentation is now a foundational cybersecurity practice rather than a nice-to-have optimization; Gartner’s Emerging Tech Impact Radar reinforces this takeaway with a critical clarification: automation is central to segmentation success.  

This is further underscored by Gartner’s 2026 report Reimagining Network Microsegmentation: Beyond the IP – Identity, Context, and Agentless Innovation, which identifies three critical insights shaping the future of network microsegmentation: the shift from IP-based rules to identity-first enforcement, the need for autonomous policy governance that moves beyond manual management, and the requirement for agentless, multimodal enforcement architectures that can deliver comprehensive coverage across heterogeneous enterprise environments. 

Gartner attributes the urgency around autonomous microsegmentation to a bevy of interconnected factors, including the accelerating pace of AI-driven threats, rising IoT breaches, and a new wave of Zero Trust prioritization. Attackers now begin moving laterally in as little as 27 seconds, meaning defenders can’t afford to rely on delayed detection or manual response, yet implementation complexity remains the #1 barrier stopping security teams from adopting microsegmentation.  

How Autonomous Microsegmentation Works: Simplifying the Path to Self-Defending Networks 

As Gartner points out, the next generation of microsegmentation requires a shift from manual management to “advanced, self-governing Zero Trust.” The report clarifies that the current state of the art approach for autonomous microsegmentation exists as human-on-the-loop architectures where solutions automatically handle asset discovery, zero-trust policy creation, and simulation of policy impact against life traffic telemetry. In practice, this means a modern microsegmentation solution should:  

• Automatically discover assets and learn network behavior 
• Create and enforce deterministic firewall rules and policies with a human-on-the-loop 
• Provide comprehensive real-time visibility  
• Dynamically adapt policies to network changes  

Perhaps unsurprisingly, these capabilities mirror security leaders’ priorities, validating the assertion that autonomous microsegmentation will prove vital in the coming years.  

What Security Leaders Want from a Microsegmentation Solution 

When EMA’s research asked about the key differentiators between leading microsegmentation solutions and legacy approaches in the next 1-2 years, an overwhelming majority of respondents cited “fast, automated asset discovery and tagging that scales with growing environments” and “automated policy creation and lifecycle management that reduces manual effort and ensures consistent enforcement” as important. 

Similarly, ViB Tech survey findings show that automated policy creation ranks among the most important capabilities for a microsegmentation solution, while CISA’s latest Microsegmentation in Zero Trust guidance highlights the need for segmentation policies that evolve dynamically using contextual data – a takeaway echoed in Gartner’s 2026 Reimagining Network Microsegmentation report:  

“Relying on static, IP-based microsegmentation guarantees catastrophic vulnerability to AI-driven attacks. Vendors clinging to manual policies face rapid obsolescence, as these methods are completely incapable of securing dynamic hybrid networks against lateral movement.”  

Gartner, Reimagining Network Microsegmentation: Beyond the IP – Identity, Context, and Agentless Innovation   

Instant Threat Containment and Lateral Movement Prevention  

Research shows that security leaders perceive microsegmentation’s top benefits as: 

• The ability to instantly contain and quarantine threats  
• Halting lateral movement and thwarting ransomware 

Autonomous microsegmentation unlocks a simple, scalable path to realizing these benefits. While traditional segmentation waits for someone to notice a threat and apply a rule, autonomous microsegmentation learns continuously, adapting quickly to constantly changing environments. This shift empowers defenders, allowing them to focus on meaningful projects while knowing they can rely on a self-defending network that blocks unauthorized lateral movement. 

Why Gartner Named Zero Networks a Sample Vendor for Autonomous Microsegmentation  

Purpose-built from day one to overcome the deficiencies of legacy segmentation, Zero Networks was named a sample vendor in Gartner’s Emerging Tech Radar. Zero’s automated, identity-aligned solution delivers the core capabilities defining the next generation of microsegmentation:  

• Adaptive segmentation based on learned network connections: Zero Networks deploys in a click, pinpointing every network asset before monitoring and learning all connections to create deterministic, highly accurate firewall rules and policies. 
• Human-on-the-loop enforcement and real-time visibility: Thanks to Zero’s robust automation engine and comprehensive visibility, security teams can oversee segmentation and network activity without needing to manually implement changes.  
• Comprehensive coverage and scalability across environments: Zero’s dynamic policy creation continuously refines and adapts policies to ever-growing and changing networks so organizations can automatically secure every asset (IT, and OT/IoT) across all environment types: on-premises, cloud, hybrid, and Kubernetes. 

Zero Networks’ dynamic approach delivers quantifiable business value. Zero pinpoints every asset and identity within minutes, automatically learning all network activities in just 30 days and greatly reducing the manual burden associated with managing network segmentation long-term. As a result, Zero Networks saves the average enterprise 87% compared with traditional segmentation solutions.   

By leveraging native controls at the host level and automating the most notoriously complex microsegmentation tasks, Zero Networks delivers comprehensive network protection without the manual burden of legacy solutions.  

See for yourself how Zero Networks is ushering in a new era of microsegmentation – request a demo.   

To understand where VLANs fit into modern cybersecurity strategies, we’ll outline everything you need to know about how VLANs work and when to combine them with more advanced solutions.  

Key Answers  

• What is a VLAN? A VLAN (virtual local area network) is a logical segment within a physical network that groups devices into a shared broadcast domain, regardless of where they sit physically. 
• How does a VLAN work? VLANs operate at Layer 2 of the OSI model, using switch port assignments and traffic tagging to control which devices can communicate directly with each other. 
• Is a VLAN the same as network segmentation? No, VLANs only separate broadcast domains, not traffic or access between devices. Even with a VLAN in place, a compromised device can move laterally to others in the same Layer 3 network.  
• Do VLANs stop lateral movement? VLANs cannot effectively prevent all unauthorized lateral movement on their own. In theory, communication between different VLANs is restricted unless explicitly permitted, but misconfigurations like excessive allow-rules or poorly managed inter-VLAN traffic make it easy for attackers to bypass those restrictions and move laterally. 
• What is the top VLAN alternative? Modern microsegmentation solutions extend VLAN-level isolation with identity-aware, granular access controls to close the gaps VLANs leave open without requiring a network redesign.  

Virtual LAN (VLAN) Definition 

A virtual local area network (VLAN) is a logical segment within a physical network. A VLAN can ensure network traffic on a single physical infrastructure behaves as if it is split between separate networks.  

VLANs are often used to isolate traffic for specific departments within an organization, segment VoIP systems, or create separate guest networks.   

How Do VLANs Work?  

VLANs function at Layer 2 of the OSI model, enabling devices within the same VLAN to communicate directly while restricting communication with other VLANs unless explicitly permitted. This is achieved by assigning devices to specific logical groups, then ensuring traffic is only forwarded within those groups.   

VLAN Misconception: Why VLANs Don’t Equal Segmentation

VLANs were designed in an earlier era of networking. As today’s distributed infrastructures face AI-driven attacks and sophisticated cyber threats, the inherent limitations of VLANs are increasingly clear: A VLAN is a very simple but ineffective way to separate networks. 

VLANs are often mistaken for true network segmentation, but in reality, they only separate broadcast domains – not traffic or access between devices. So, while a VLAN can reduce noise and improve organization, it doesn’t inherently stop one compromised endpoint from reaching another within the same Layer 3 network. This false sense of segmentation can leave organizations exposed to lateral movement and internal spread of attacks. True segmentation requires enforcing access controls and isolation policies that go beyond simple VLAN boundaries. 

VLANs vs. Subnets 

VLANs and Subnets are two of the most common approaches to logical segmentation. VLANs operate at Layer 2 and work by:   

• Creating multiple virtual networks within a physical infrastructure   
• Assigning specific ports, switches, or users  
• Isolating traffic between VLANs  

Subnets operate at Layer 3; they allow organizations to create segments with distinct subnet address ranges, which enables:  

• Efficient IP address management  
• Access control and security policy enforcement at the subnet level  
• Network addressing schemes for automated provisioning and configuration processes  

VLAN Configuration and Considerations: Key Components

To function properly, VLANs rely on a handful of technical building blocks, all of which play a role in configuration.  

VLAN Ports and Switches  

Switches are the backbone of VLANs. Once a VLAN is created, ports on a switch can be assigned to it so any device plugged into that port automatically becomes part of the VLAN. 

VLAN Membership 

Membership determines which devices are assigned to a VLAN; some of the most common approaches for assigning membership to VLANs are:  

• Port-based membership: The most common method, where a switch port is tied to a VLAN ID. 
• MAC-based membership: VLAN assignment follows the device’s unique MAC address. 

VLAN Tagging

Tagging helps distinguish VLAN traffic from other traffic, ensuring that traffic is associated with the right VLAN as it traverses the network. VLAN tagging is typically achieved by marking Ethernet frames with a VLAN ID.  

VLAN Trunking 

Usually completed using a protocol like IEEE 802.1Q, VLAN trunking allows a single physical link to carry traffic for multiple VLANs. 

VLAN Router 

Because VLANs operate at Layer 2, devices in different VLANs can’t communicate directly. A router (or Layer 3 switch) is needed for inter-VLAN routing, which allows traffic to pass between VLANs in a controlled way.

VLAN Segmentation: Benefits and Disadvantages  

By dividing a single physical network into multiple logical segments, VLANs make it easier for organizations to segment networks via firewalls between gateways, reduce the scope for auditing, and improve identification of assets by network addresses.  

Because VLANs offer a straightforward and cost-effective way to logically separate networks without requiring additional physical infrastructure, they have long been a foundational tool in network segmentation. In fact, VLANs remain one of the most popular network segmentation methods, leveraged by 30% of organizations today. 

Still, it’s important to recognize that VLANs were never built for today's challenges. Understanding both the advantages and pitfalls of VLAN segmentation helps clarify where the technology fits into a modern security strategy. 

VLAN Benefits: Foundational Network Security 

VLANs have earned their reputation as a tried-and-true network security strategy thanks to benefits like:  

• Performance optimization: VLANs help to keep traffic local and reduce congestion. In large enterprises with thousands of connected assets, this can meaningfully improve network efficiency.  
• Baseline security isolation: By keeping devices in one VLAN from directly communicating with devices in another, organizations can prevent accidental exposure of sensitive data and minimize the attack surface.  
• Operational flexibility: VLANs allow administrators to reorganize networks without touching physical infrastructure. For example, a device that moves to a new location can still remain in the same VLAN since it’s a logical rather than physical construct. 
• Cost efficiency: Instead of buying and deploying additional physical switches, security teams can achieve security gains more cost-effectively with VLANs.  

So, VLANs deliver foundational security improvements without excessive cost or operational complexity, but they also come with tradeoffs.

VLAN Disadvantages: Misconfigurations, Blind Spots, and Manual Burden 

VLANs are a straightforward, well-established approach to network segmentation; still, they bring distinct drawbacks, such as:  

• Administrative overhead: Each VLAN must be configured and maintained consistently across switches. In sprawling environments, this can create operational drag, excessive manual burden, and a high risk of misconfiguration. 
• Misconfigurations: VLANs are only as effective as their configurations – improper setups can introduce hidden vulnerabilities. VLANs often end up looking like Swiss cheese as allow-rules accumulate, so they don’t really increase protection on either side. Misconfigurations like excessive allow-rules or poorly managed inter-VLAN traffic make it easy for attackers to bypass restrictions and move laterally. Misconfigurations can also interrupt network operations, causing outages and poor performance.   
• Limited adaptability: VLANs work well in relatively static environments but struggle to keep pace with hybrid clouds, multisite networks, and other dynamic environments that demand constant flexibility. Large, constantly changing infrastructures make it particularly difficult to maintain and scale brittle VLAN configurations without error.  
• Security blind spots: While VLANs provide isolation, they don’t enforce granular access controls. In other words, a VLAN may visually segment a network, but true traffic isolation is only achieved with stringent rule enforcement. VLANs often give a false sense of security by appearing to segment assets when in reality they aren't properly configured for segmentation. 
• Limited granularity: VLANs divide networks into broad segments but lack fine-grained control. To combat threats like ransomware, organizations need segmentation strategies capable of preventing lateral movement and quarantining security breaches. VLANs operate primarily at Layer 2 and lack the granularity and adaptive capabilities needed to counter these sophisticated attacks.  

In practice, VLANs traditionally strike a balance between efficiency and control, but they should be understood for what they are: a legacy tool that provides valuable isolation but falls short of delivering the fine-grained, adaptive security controls modern networks require as organizations pursue Zero Trust. VLANs can be a steppingstone, but they need to be complemented by other tools to truly secure a network.

VLAN Alternatives: Network Segmentation Strategies  

While VLANs remain a staple of enterprise networking, alternative segmentation approaches may better serve an organization’s security objectives. In some cases, these strategies can (and should) be layered with VLANs to bolster cyber defenses.  

Traditional Firewall Segmentation 

Using firewalls to enforce policies between network segments can help organizations extend VLANs with more granular traffic rules. Firewall segmentation often provides familiar tooling and centralized control, but it comes with a familiar limitation: scalability. 

Every new application requires policy updates, and sprawling rule sets are prone to misconfiguration. Firewall segmentation approaches also tend to enforce coarse controls rather than the fine-grained, dynamic access policies needed today. 

Software-Defined Networking  

SDN decouples the control plane from the data plane, enabling administrators to centrally manage traffic flows. Organizations with large, virtualized, or cloud-heavy environments may turn to SDN for greater agility. However, SDN requires significant upfront investment and often necessitates a complete network overhaul with reconfiguration of the existing infrastructure. On top of that, the developing nature of SDN technology means it requires specialized knowledge to manage, and while SDN centralizes control, it doesn’t necessarily alleviate operational complexity. 

Application Ringfencing   

Application ringfencing tightly controls how applications communicate with one another and what resources they can access. Ringfencing limits the blast radius by ensuring applications operate only within defined boundaries, making it a particularly appealing approach for environments where certain applications are especially sensitive or heavily regulated.  

Importantly, application ringfencing is best leveraged as part of a layered strategy as it’s not a comprehensive segmentation solution. Like other approaches, ringfencing also brings operational complexity and administrative burdens as implementing and managing policies requires significant manual effort.  

Physical Segmentation  

At face value, physical segmentation – the practice of deploying separate physical infrastructures for different parts of an organization – may seem like a step in the wrong direction as it’s expensive, rigid, and difficult to scale. However, this method provides the highest level of isolation since segments are physically disconnected, making lateral movement extremely difficult.  

Still, even for organizations with extremely high security or regulatory requirements (like those in government, defense, or other mission-critical industries), physical segmentation is falling out of step with modern trends such as cloud adoption and hybrid workforces.  

Microsegmentation  

Unlike traditional network segmentation approaches – which generally divide a large network into broad subnetworks, or segments – microsegmentation is a much more granular and robust process of isolating all clients, workloads, applications, virtual machines, and operating systems into individual segments with unique security perimeters.  

Microsegmentation aligns with Zero Trust principles by enforcing least privilege across the entire network; it gives organizations the ability to proactively contain breaches at the source, block lateral movement, and shrink the attack surface. Legacy microsegmentation has historically been complex to deploy and manage, leading to relatively low adoption despite widespread recognition that microsegmentation is the gold standard in network security. However, modern microsegmentation solutions can combine deterministic automation and identity-aware access controls to accelerate implementation and enable hands-free adaptability, unlocking granular segmentation at scale – without added complexity. 

Real-World Example: Replacing VLANS with Microsegmentation   

Reference Laboratory delivers high-quality clinical analysis services in collaboration with 400+ hospitals and 1,000 clinical analysis laboratories worldwide, so downtime can spell diagnostic delays and critical disruptions in patient care. Although Reference Laboratory had achieved basic segregation with VLANs, the organization struggled with a lack of visibility into VLAN traffic – and in turn, a lack of control.  

Reference Laboratory turned to Zero Networks’ automated, identity-based microsegmentation for the granular control, comprehensive visibility, and operational simplicity they needed, uncovering previously unknown service accounts and automatically generating fine-grained access policies to constrain them. 

Modernizing Network Security: Evolving Beyond VLANs  

VLANs remain an important network security building block, providing an initial level of internal segmentation that improves performance and basic security. But as environments grow more complex and threats more advanced, VLANs alone are not enough. Instead, VLANs should be part of a layered defense strategy that includes firewalls, Zero Trust Network Access (ZTNA), and automated microsegmentation.  

Zero Networks makes it easy to address the security vulnerabilities left by VLANs and achieve comprehensive granular segmentation with an automated, three-step approach: 

• Comprehensive Discovery: Zero deploys in a click, automatically pinpointing assets before conducting a detailed analysis of all network interactions during an automated learning period.   
• Automated Tagging and Policy Creation: Using the data gathered in learning, Zero Networks generates deterministic, fine-grained policies – no manual labeling, guessing on grouping, or never-ending policy creation required.  
• Effortless Enforcement: Once policies are in place, Zero automatically segments assets, rapidly enhancing security without disrupting day-to-day operations.   

Learn more about how you can build from the foundational network security established by VLANs and achieve comprehensive, identity-aware microsegmentation without adding operational complexity – request a demo.  

A containment-focused approach to cyber risk quantification overcomes the language barrier between CISOs and business leaders by delivering an objective measurement of what’s at stake, how easily an attacker could reach critical assets, and where security investments can minimize cyber risk exposure the fastest. We’ll walk through a step-by-step approach for connecting security gaps to real-world impact, then leverage those insights into a business-validated cyber resilience roadmap.  

Key Answers  

• What is cyber risk quantification? The practice of measuring cybersecurity risks in objective, business-aligned terms, so security leaders can communicate the cost of a security gap and the value of proposed security investments in a language every executive and board member grasps.
• How can enterprises measure cyber resilience? To calculate an enterprise containment score (the most defensible measure of proactive cyber resilience), security teams need a validated inventory of critical assets, an estimate of the business impact if any of those assets were compromised, and an attack path analysis that scores each asset’s current level of exposure within common compromise scenarios. Composite containment scores for each critical asset can be averaged out to an enterprise-wide score.
• How can security leaders track the value of cyber resilience investments? By regularly calculating containment metrics in the context of business exposure, security leaders can measure cyber risk exposure before and after implementing targeted controls designed to address the costliest risks. Reduced exposure of business-critical assets translates to measurable cyber resilience ROI.  

What Is Cyber Risk Quantification?   

Cyber risk quantification (CRQ) is the process of assigning objective, business-aligned measurements to cybersecurity threats and vulnerabilities. For example, rather than surfacing a gap as “critical,” a properly executed CRQ enables security leaders to communicate the cost of that gap in business terms and quantify the impact of suggested security investments.  

While many CRQ strategies focus on the estimated financial repercussions of a breach, it’s equally important to measure the true scope of exposure by uncovering how accessible critical assets are and what security controls are currently in place to mitigate that accessibility.  

Containment Metrics: Measuring Proactive Resilience Strategies  

Containment metrics are critical to comprehensive cyber risk quantification. Rather than scoring how well a security team detects or responds to an incident, containment metrics score the structural properties of the environment – the factors that determine how far an attacker can move and how much damage they can do from an initial point of compromise.  

Containment metrics help translate a critical asset inventory into a high-level exposure score that’s digestible at the board level.  

The 3 Dimensions of an Enterprise Cyber Resilience Score  

To calculate the full scope of cyber risk exposure, security leaders need three connected inputs:  

1. Critical asset inventory: Identify which systems are essential to business operations.   
2. The business impact of critical asset compromise: Collaborate with cross-functional leaders to quantify the impact of a critical asset breach, in both financial and operational terms.  
3. Attack path analysis and breach containment controls: Map the attack paths adversaries could use to reach critical assets and measure the existing security controls along those paths. Note: We understand mapping attack paths is a lot of work. That's why we built a free tool that will do it for you – get it here.  

The goal isn’t just to inventory critical assets and establish their current exposure, but to translate those insights into an overall containment score, standardizing cyber resilience measurement and clarifying the value of security investments.  

How to Quantify Business Exposure: Mapping Cyber Risk to Business Impact  

With an inventory of critical assets and a baseline estimation of the financial or operational fallout if a compromise occurred, security teams can proceed to measuring how exposed critical assets are today and uncovering which security interventions would most meaningfully shrink that exposure.  

Track Path Distance, Privilege Escalation, and Data-Layer Controls  

For each critical asset, security teams should analyze attack paths using common compromise scenarios and likely ingress points. Each of these paths should be scored on dimensions of structural containment:  

• Path Distance: How many structural barriers and controls – such as authentication boundaries and network segments – exist between the ingress point for a breach scenario and the critical asset.  
• Privilege Requirements: How difficult it is for an attacker to gain the privileges needed to access the critical asset, defined by factors like escalation levels required and service account density.  
• Data-Layer Controls: How much damage an attacker could do once they reach the critical asset, indicated by encryption at rest and identity-based access controls.  

Calculated together, these measurements turn a list of individual controls into a unified resilience metric.  

Critical Asset Containment Score: Detailed Exposure Breakdown  

After scoring every compromise scenario against structural containment dimensions, security teams can calculate each critical asset’s overall containment score by: 

• Counting the controls within the first two containment categories to reach a composite (average) score for both path distance and privilege requirements  
• Calculating the overall containment score for each path using the formula: Containment Score = (Path Distance composite x 0.54) + (Privilege Requirements composite x 0.36) + (0.5 for each Data-Layer Control)  
• Mapping overall containment scores for each compromise scenario to understand where the greatest weaknesses lie, then averaging path-level containment scores to reach the critical asset’s composite containment score.  

This approach leaves security leaders with two important insights: a high-level view of each critical asset’s resilience posture, and a granular view of the riskiest exposures across the business, indicated by each asset’s lowest containment score.  

Highest Cost Path Indicator: Enterprise-Wide Cyber Risk Insights  

The final step in quantifying cyber risk and resilience is to consolidate containment scores into one business-wide score and to surface priority security gaps by documenting worst-case containment scores in a single enterprise view.  

First, calculate the average of every critical asset’s composite containment score; the result is the organization’s overall containment score. Next, look back at the lowest containment scores for each critical asset – these will be used to calculate the highest cost path indicator (HCPI), which assigns a single blended score for cyber exposure and business exposure.  

To calculate HCPI, divide the median estimated damage from a critical asset breach by the asset’s worst-case containment score. A higher result (relative to the HCPI for other critical assets) indicates that an asset is both costly to lose and easy to reach; a lower score indicates that the asset carries relatively low business impact or is fairly well protected.  

Tracking the HCPI for every critical asset gives security leaders a single, comprehensive view of where enterprise risk is concentrated – and the controls that would most significantly improve business exposure.  

Building a Cyber Resilience Plan with Business Exposure Measurements  

Calculating enterprise-wide containment scores by using granular per-scenario and per-asset scores as building blocks gives security leaders both the high-level reporting output they need to communicate at the board level and the detailed exposure insights that tell what to do about it.  

When a CISO surfaces HCPI scores across the enterprise, they can use the individual containment metric scores within each of those worst-case compromise scenarios as a diagnostic tool to uncover priority interventions:  

• A low path distance composite score indicates the need for more comprehensive network segmentation and additional authentication boundaries. Path distance is the most heavily weighted dimension of containment scoring because adding one of these controls can eliminate entire compromise scenarios; as a result, actions here typically have the largest impact per dollar invested.  
• A low privilege requirements composite score indicates that eliminating standing privileges, governing service accounts, or enforcing just-in-time access controls would most meaningfully improve business exposure.  
• Missing data-layer controls within a worst-case compromise scenario can be addressed by adding granular identity-based access controls or encryption at rest with separated key management.  

Building a cyber resilience roadmap this way gives security leaders a data-driven and defensible strategy for investment asks and provides a baseline for measuring the impact of those investments over time. Tailoring cyber resilience strategies to business exposure delivers the evidence boards expect in a language they can easily grasp.  

Strengthen Cyber Resilience and Maintain Business Continuity with Zero Networks 

Zero Networks gives security teams a direct path for addressing business-critical cyber risk exposure. With automated, identity-based microsegmentation, Zero provides immediate visibility into every identity and asset on the network, then automatically enforces adaptive, identity-aligned policies that prevent lateral movement to critical assets to safeguard business resilience.   

By strengthening every containment dimension from a unified platform, Zero Networks unlocks a cyber resilient architecture with measurable business value. Request a demo to learn how.  

So, security leaders need a way to strengthen protection without adding headcount. By building a Zero Trust architecture, enterprises can achieve both the comprehensive coverage and the cost savings they’re after, reducing operational overhead so security teams have more bandwidth to focus on strategic priorities.  

We’ll outline the operational complexity security teams face today and map how a Zero Trust architecture addresses those challenges, shrinking security’s operational burden – and in turn, cyber costs.  

Key Answers  

• How can Zero Trust reduce operational overhead for security teams? Zero Trust architecture closes access by default and enforces least privilege continuously, which means fewer open paths to monitor, fewer manual exceptions to maintain, and a smaller blast radius when breaches occur. Automation makes sustaining that posture operationally feasible at scale.  
• What are the primary drivers of higher security OpEx? Expanding attack surfaces with broad default trust, rising alert volume, manual firewall policy maintenance, constant rule tuning, and labor-intensive incident response workflows are some of the factors that ultimately contribute to higher security overhead.  
• How much time and effort can enterprises save by implementing Zero Trust? Organizations can recapture hundreds of hours per year with a Zero Trust architecture. For example, Zero Networks customers save 10-15 hours per week, per engineer on policy maintenance; that ultimately saves enterprises 87% TCO.  

What Is Zero Trust Architecture?  

Zero Trust is a cybersecurity strategy that removes implicit trust, treating all traffic as potentially risky – even if it’s already inside the network. Zero Trust architecture (ZTA) refers to how the philosophy is implemented across infrastructure, workflows, controls, and policies.   

NIST’s special publication on Zero Trust architecture outlines logical components of a Zero Trust architecture, which include:  

• Policy Engine: Responsible for granting, denying, or revoking access to a resource.   
• Policy Administrator: Establishes or shuts down communication, generating any session-specific authentication steps as necessary.  
• Policy Enforcement Point: Communicates with the policy administrator to forward requests or receive policy updates to enable, monitor, and eventually terminate connections.  

The same NIST special publication surfaces approaches for building a Zero Trust architecture, such as:  

• Enhanced Identity Governance: This approach to developing a ZTA uses identity as the key component of policy creation.  
• Microsegmentation: ZTA can be developed by isolating assets in unique network segments to effectively secure every resource and dynamically grant access to individual requests.   

As new threats emerge and board expectations rise, a Zero Trust architecture that reduces the operational burden on security teams is more urgently needed than ever.  

Rising Security Operations Overhead: Top Drivers  

The gap between what security teams can manually sustain and what modern environments require keeps widening. The long-standing demands of traditional security strategies are compounded by the evolving, AI-era threat landscape, leaving security teams to contend with an ever-growing to-do list.   

Expanding Attack Surfaces and Alert Volume 

Cloud and hybrid workload sprawl, enterprise AI adoption, and quietly expanding machine identities have multiplied the assets and connections most teams are responsible for securing. More than 80% of analysts already report feeling overwhelmed by alert volume, false positives, and insufficient context; more complex and dynamic environments only add to the backlog, adding another barrier to effective incident response and breach containment.  

Manual Policy and Firewall Rule Maintenance  

Traditional network segmentation requires constant manual tuning – someone has to define what’s allowed, test the change, and revisit the rule when the environment shifts. That labor scales with network complexity; as environments become increasingly dynamic and interconnected, securing them requires more time and effort than teams can spare.  

Labor-Intensive Detect-and-Respond Workflows  

Detection and response is a chain of steps: alert, prioritize, investigate, respond, and contain. Each of those steps requires manual effort and orchestration – any delay results in slower breach containment, but as alert volumes grow, security teams are forced to sacrifice either speed or precision. Either way, operational complexity keeps climbing and new gaps emerge.  

Zero Trust Architecture ROI: Shrinking Security Operations Overhead

Adding more tools or headcount won’t resolve underlying factors that cause security operations overhead to climb.  

A properly implemented Zero Trust architecture enforces least privilege access across every identity and communication path, requires explicit verification for every connection, and closes internal access by default. This proactive approach removes the burden of post-breach manual effort, driving security teams’ operational overhead lower while improving the outcomes that matter most for business continuity.  

Closing Access by Default to Reduce Alert Volume 

A single compromised system can give attackers access to 85% of the environment – and adversaries begin moving laterally in as little as 27 seconds. When enterprises over-rely on detection and response workflows, they’re betting that analysts will have the time and skills to investigate critical alerts before breaches escalate into business crises. But 70% of alerts are simply distracting noise – they don’t lead to real risk reduction.   

A Zero Trust architecture proactively closes lateral movement pathways and constrains every identity to least privilege; adaptive, context-aware access policies reduce the volume of generic perimeter alerts, ultimately freeing security teams from the endless backlog and allowing organizations to recapture those costs.  

Centralizing Policy Management and Shrinking Tool Sprawl 

The average organization manages 83 different security tools from 29 different vendors – and most CISOs say this complexity is the greatest impediment to security operations. In fact, security complexity can cost organizations more than 5% of their annual revenue.  

By implementing a Zero Trust architecture, security teams can eliminate the need for a patchwork of point solutions. Addressing the root cause of most disruptive security breaches – unchecked lateral movement and excessive internal trust – means organizations no longer need a separate security appliance to cover every distinct gap. By centralizing policy management through a single, unified platform, security leaders unlock consistent control across the entire environment while eliminating unnecessary deployment and maintenance requirements.  

Streamlining Breach Containment with Proactive Controls  

It takes 247 days to identify and contain the average breach. In an era of AI-accelerated exploits, the gap between lateral movement and containment has never been wider – so security teams tasked with incident response workflows have never been busier. 

 Zero Trust architecture makes containment a property of the network itself rather than a race against the clock. When every asset is proactively isolated via microsegmentation, always-on access is eliminated with identity-based controls, and privileged pathways are protected by just-in-time MFA, proactive containment replaces around-the-clock response.  

Operational Outcomes: Measuring the Impact of Zero Trust on Workloads and Cybersecurity Budgets 

Closing access by default, enforcing least privilege everywhere, and building containment into the network architecture delivers a measurable reduction in day-to-day workloads. For example, with a closed-by-default architecture, Zero Networks customers achieve:  

• 10 to 15 hours saved per week, per engineer, on policy maintenance that previously required manual writing and tuning  
• 2-3x more assets and identities managed per team, without a proportional increase in headcount  
• 87% lower cost of ownership at enterprise scale, driven primarily by acquisition, maintenance, and management savings  

These real-world results clarify the impact of Zero Trust architecture: fewer hours spent maintaining rules or chasing alerts, more capacity for strategic initiatives.  

Build a Zero Trust Architecture That Reduces OpEx with Zero Networks  

Zero Networks replaces manual processes, endless tool sprawl, and fragile rules with automated, identity-based controls that adapt in real time, fast-tracking Zero Trust initiatives and removing the operational complexity that drives overhead higher.  

Zero helps security teams build closed-by-default architectures that satisfy Zero Trust requirements with:  

• Agentless, automated microsegmentation that proactively closes lateral movement pathways.  
• Just-in-time network layer MFA that verifies every privileged access request and protects ports by default.   
• Identity segmentation that maps the necessary connections for every user and service, ensuring comprehensive least privilege enforcement.    
• Automated policy creation and enforcement that adapts to network changes thanks to a deterministic automation engine.  
• Integrated ZTNA capabilities that combine speed and security to overcome secure remote access challenges.   

By orchestrating native firewalls and integrating with existing identity providers, Zero Networks strengthens cyber resilience without operational disruptions or never-ending implementation cycles. Learn how to build a Zero Trust architecture that reduces blast radius and operational overhead – request a demo. 

In other words, while attackers’ use of AI is alarming, rising risk exposure driven by enterprise AI adoption poses an equally urgent threat to business resilience. In fact, security breaches involving AI tools are surging even faster than attacks involving adversarial AI, rising 61% year-over-year.  

As boards continue to push for accelerated AI innovation, organizations need a way to confidently adopt AI without absorbing new AI risks. We’ll lay out the AI vulnerabilities every modern enterprise should be aware of, and AI security best practices that enable security teams to govern tools, agents, and workflows by design.  

Key Answers  

• What are the cybersecurity risks of AI adoption? Every new AI tool, agent, and model connection expands an organization’s internal attack surface, and AI adoption has largely outpaced security governance. AI-driven lateral movement via excessive AI agent access and autonomy, shadow AI, and LLM exposure are some of the key risks enterprises face due to accelerated AI innovation.  
• Which solutions protect AI tools from exploitation? AI segmentation combines microsegmentation, identity-based access controls, always-current network visibility, and deterministic policy creation and enforcement to effectively shield an organization’s internal AI from attackers.  
• What are best practices for strengthening AI security? Implement microsegmentation to proactively isolate systems, enforce least agency for all AI agents with identity-based access controls, apply deterministic guardrails to AI adoption, and maintain real-time network visibility and control.  

AI Security Risks: How Is Enterprise AI Adoption Increasing Cyber Risk Exposure?  

Nearly 90% of organizations use AI in at least one business function, with AI agent adoption expected to grow 85% within the next year. Amid this rapid acceleration, it’s no wonder that 87% of security leaders rate AI vulnerabilities as their organization’s fastest growing cyber risk. 

As AI becomes more embedded across business functions, it introduces risk through a set of related vectors: the AI agents acting autonomously inside the network, the AI tools that employees adopt outside of IT oversight, and the model that infrastructure organizations increasingly build on.  

AI-Driven Lateral Movement (AILM) and Excessive Agency  

By design, AI agents connect to multiple systems – email, CRM, databases, cloud APIs, code repositories, and more – and act autonomously across those connections. But unlike traditional identity classes (like human users), AI agents are largely provisioned without robust governance frameworks. In fact, among organizations that experienced an AI-related breach in the last year, 92% lack proper AI access controls; across all organizations, less than a third have extended Zero Trust architecture to include non-human identities (NHIs).   

This uncontrolled agentic attack surface leaves organizations vulnerable to AI-driven lateral movement enabled by excessive AI agent permissions. At its core, this risk is familiar: an identity with broad internal access moving through the network in unintended ways. In other words, AI vulnerabilities aren’t novel – they’re shining a light on the structural gaps long embedded in enterprise environments.   

What to do about it: Identity-based microsegmentation  

The solution to AILM and overprivileged AI agent risks is to make Zero Trust the default rather than the exception. Comprehensive identity-based microsegmentation blocks communication paths unless explicitly necessary, keeps systems invisible to unauthorized users and processes, and constrains access based on identity and business need. When AI agents operate within identity-based, least privilege boundaries governed at the network layer, agentic systems cannot be leveraged into lateral movement vehicles.  

Shadow AI and Ungoverned SaaS AI Usage 

The number of employees that regularly use AI on their corporate devices has increased 3x year over year, but just 22% of individuals rely exclusively on tools provided by their employers – that gap between adoption and oversight is where shadow AI lives.  

Security incidents involving shadow AI more than doubled in the last year, costing an average of $5.39 million, yet 68% of organizations still lack the necessary governance to manage AI or detect shadow AI. So, the challenge is twofold: uncovering unsanctioned AI usage in the first place, then implementing controls to effectively prevent shadow AI.  

The core problem is that unsanctioned AI creates connectivity that nobody approved, reviewed, or is watching. If enterprises don’t know which cloud AI services users are accessing, how developers are deploying AI agents outside of the security team’s visibility, or which third-party software has embedded autonomous workflows, they’re stuck blindly chasing attackers when breaches involving shadow AI occur.  

What to do about it: End-to-end network visibility and control 

To mitigate shadow AI risks, security teams must first establish and maintain a real-time inventory of AI in their environment to answer three questions:  

1. Which SaaS AI destinations are users and devices reaching?  
2. Which AI agents are running and what are they connecting to?  
3. Which of these AI deployments fall outside of IT oversight, or where does AI access exceed operational need?  

An always-current network map will deliver visibility into every AI tool, agent, and integration in the environment, delivering the insights security teams need to block unsanctioned AI by default; for example, organizations can apply network-layer controls to automatically block connections to SaaS AI destinations outside of an approved list.  

LLM Exposure  

Model infrastructures are code, and like any code, internal models can be reached, tampered with, and poisoned. Among AI-related data breaches, model inversion and prompt injections attacks are the most expensive, costing an average of $6.07 million and $5.89 million respectively.  

While the rise in these incidents underscores the growing difficulty of protecting training data, controlling model behavior, and safeguarding sensitive outputs, the uptick doesn’t necessarily relate to model selection – the root cause is most often structural. In other words, LLM exposure is fundamentally a network access problem.  

Organizations continue to prioritize innovation over security for AI models and applications, which can leave them vulnerable to AI-related breaches ... The most common causes weren’t model failures themselves, but weaknesses in surrounding systems.”  

IBM Cost of a Data Breach Report, 2026 

What to do about it: Proactively isolate model infrastructure  

Model infrastructure should be segmented so it’s only reachable for explicitly authorized systems and identities. Granular network segmentation closes the access paths attackers rely on to tamper with or extract data from a model, without requiring that security teams fully understand the model’s internal behavior in order to protect it.  

4 AI Security Best Practices: How to Govern AI Tools, Agents, and Workflows  

Eighty percent of organizations say AI has increased productivity; security leaders need a way to protect AI vulnerabilities without hampering that business value. The key is building an AI-ready network architecture with a deterministic enforcement layer governing what AI can reach and do, so new capabilities adopted by the business don’t quietly expand the attack surface.

Implement Microsegmentation to Proactively Constrain Blast Radius  

Microsegmentation eliminates the broad internal reachability that makes AI exploitation so risky, closing lateral movement pathways by default so if an AI tool is compromised or misconfigured, the impact is automatically contained. This way, new AI is deployed inside a network that’s already hardened – a containment architecture is designed to neutralize risk, so AI vulnerabilities aren’t inherited. 

Enforce AI Agent Authorization Governance with Identity-Governed Access for Every Connection  

The OWASP Agentic Applications Top 10 formalized the Least Agency principle: contain an agent’s autonomy, tool access, and decision-making authority to reduce the impact of compromised AI agents. In practice, this means combining identity-based access controls and network-layer MFA for just-in-time privileged access, ensuring AI agents communicate only with explicitly authorized systems, only access approved resources, and require human approval before performing sensitive administrative actions.  

Scale AI Adoption Safely with Deterministic Guardrails  

Security policy enforcement must be deterministic, based on real network behavior rather than likely guesses. By leveraging a deterministic, human-on-the-loop automation engine for policy creation and enforcement, organizations can ensure AI access controls stay up to date as the network evolves, enabling peace of mind that rapid AI adoption won’t create hidden security gaps.  

Maintain Comprehensive Real-Time Network Visibility  

Enterprises need continuous, real-time visibility into which AI agents are running, what cloud AI services users are accessing, and how model infrastructure is being accessed, mapped against actual business need. This network map both surfaces risks and enables real-time enforcement, giving security teams the power to generate and simulate segmentation policies as AI risks emerge.  

AI Segmentation: Contain AI Cyber Risk Exposure with Zero Networks  

AI innovation doesn’t have to create AI risk exposure. Zero Networks’ AI Segmentation capabilities give enterprises the visibility and control they need to confidently embrace AI across the business:  

• AI agent control and least agency enforcement constrain AI access to the systems and services required for their assigned task  
• AI lateral movement control removes the connectivity that allows attackers to pivot from AI access points, ensuring unauthorized users, systems, or agents can’t reach critical resources  
• SaaS AI control governs which cloud AI services users and devices can access at the network layer  
• LLM protection granularly segments model infrastructure so only authorized systems can reach it, closing the structural security gaps that allow access and tampering  

Learn how you can accelerate AI innovation while containing risk by design with Zero Networks – request a demo.  

For enterprises focused on strengthening cyber resilience, the answer isn’t faster detection or more automated response. The only reliable advantage for defenders is to rethink incident response, stepping out of the endless cycle of chasing attacks to instead prioritize proactive containment.  

We’ll walk through how incident response is changing in the AI era, why a containment architecture is key to accelerating cyber incident recovery and strengthening resilience, and share a practical framework for making automated breach containment a built-in feature of the network. 

Key Answers  

• How can security teams quickly isolate infected systems without shutting down the whole network? Implement identity-based microsegmentation, so the blast radius of a breach is proactively constrained, and threats remain isolated to the initial point of compromise. When lateral movement and privilege escalation are blocked by the network architecture, breaches hit an immediate dead end.  
• What's the difference between incident response and breach containment? Incident response (IR) is the structured process teams follow once a cyber incident is identified; breach containment refers specifically to limiting the spread of an attack across the network. In traditional IR plans, containment is typically treated as a sequential step in the response chain, but when containment is enforced architecturally (for example, through microsegmentation and identity-based access controls), it operates independently of other steps, removing the dependency on detection and response.  
• How can enterprises strengthen cyber resilience with an incident response plan? A cyber incident response plan strengthens cyber resilience when it treats containment as a precondition rather than a reactive step in a response chain. Rather than relying solely on a workflow that is only initiated once a breach is detected, proactive containment makes cyber resilience the default posture.  

What Is Cyber Incident Response?  

Incident response (IR) in cybersecurity is the structured process organizations use to detect, contain, and recover from cyber incidents. Traditionally, incident response unfolds in phases:  

• Preparation: Establish policies, roles, training, and technical capabilities  
• Detection & Analysis: Identify the presence and scope of malicious activity  
• Containment: Stop the spread of the threat across the network  
• Eradication & Recovery: Remove the threat and restore normal operations  
• Post-Incident Activity: Analyze and learn from the incident; improve processes and security measures   

This sequence has been central to enterprise security operations for decades, all the while carrying a built-in assumption: that incident detection is a necessary prerequisite for containment and recovery.

Breach Containment vs Cyber Recovery vs IR  

Incident response, breach containment, and cyber recovery are distinct concepts that connect to a central theme:  

In other words, incident response describes the collective sequence an enterprise kicks off after identifying a security breach; breach containment and cyber recovery are typically viewed as steps within the broader IR umbrella. But viewed this way, the traditional IR model is a relay: detection has to work before containment can start, and containment has to be executed before recovery can begin. Each phase waits on the one before it. In the Frontier AI era, that approach is too brittle and too slow to reliably strengthen cyber resilience.

Incident Response Challenges in the AI Era  

Standard incident response is a chain of steps – each of which carries its own risk of failure. For the chain to work, every step has to go right; in the real world, that rarely happens.  

In an era of AI-driven attacks, the failure points that have always existed in traditional IR playbooks are more apparent – and more risky – than ever. A few uniquely modern threat realities are driving the shift away from traditional incident response:  

• Attack speed outpaces coordinated response workflows: Attackers begin moving laterally in as little as 27 seconds, with average breakout time now sitting at 29 minutes. Yet it still takes defenders an average of 183 days to identify a breach – and another 64 to contain it. Meanwhile, attackers now move from initial access to data exfiltration in as little as 72 minutes. The mismatch between AI-enabled attack speed and traditional threat response plans leaves enterprises vulnerable.  
• Attackers blend in with normal activity to evade detection: More than 80% of cyber incidents are malware-free attacks as adversaries increasingly abuse legitimate tools, systems, files, or applications to blend in with legitimate traffic while carrying out a compromise. In fact, more than 70% of enterprise threat activity flows through just four admin protocols. As widespread AI adoption continues to climb and attackers weaponize agentic AI security gaps, organizations that still rely heavily on detection to kick off IR face an uphill battle in identifying exploits at all – let alone stopping them before they impact business operations.  
• Threat actors are targeting backup infrastructure and other core systems to drive business disruption: The average cost of downtime triggered by cyber incidents jumped to $15,000 per minute in 2026, according to Oxford Economics – a price tag attackers know enterprises don’t have the appetite to absorb. Because of that, adversaries are specifically targeting back up technologies, virtualization management planes, and other parts of trusted service infrastructure to intentionally disrupt operations and make it harder for organizations to recover from a cyber incident.  

The rising speed, stealth, and persistence of AI-driven attacks forces defenders relying on inherently reactive incident response plans to intentionally trigger downtime in order to isolate breaches. According to Mandiant’s 2026 M-Trends Report, security teams often have to preemptively disconnect critical systems to halt the spread of an attack, effectively triggering a self-induced outage to avoid total compromise.  

Organizational survivability predicated upon Endpoint Detection and Response (EDR) or traditional backup restoration at the endpoint layer are no longer sufficient recovery models. Instead, a model focused on resilience, which seeks to address the primary objectives attackers pursue, represents the best chance for organizations to keep pace with the rapid evolution of ransomware operators.”  

- Mandiant M-Trends Report, 2026 

Cyber Resilience Strategy for Real-Time Threat Containment: A Modern Framework 

Reactive IR playbooks are a losing strategy against modern attackers that weaponize AI to disrupt every stage of the traditional breach response flow. To strengthen cyber resilience – ensuring the organization is prepared to absorb a breach and constrain its impact before an incident occurs – defenders need to prioritize built-in containment over reactive response.  

Proactively Isolate Every Asset with Microsegmentation  

Comprehensive microsegmentation isolates every network asset inside its own security zone, keeping the blast radius of a breach constrained to the initial point of compromise automatically. This means security teams don’t have to race to disconnect critical systems when a breach is identified, triggering downtime in the process – instead, threats are contained in real time even if no alert fires, making detection speed irrelevant and recovery processes far simpler. 

Eliminate Always-On Access with Granular Identity-Based Controls  

Identity weaknesses play a material role in 90% of cyberattacks, with standing access and excessive internal trust effectively handing attackers lateral movement fast-lanes. Granular access controls should be tied to the identity of users, devices, or applications and restricted to pre-approved assets and logon types. When identity governs access inside a segmented network, security teams can effectively prevent unauthorized lateral movement that would otherwise blend in with legitimate activity.  

Enforce Just-in-Time (JIT) MFA on Privileged Pathways  

Even an admin account that requires privileged access to complete regular operations shouldn’t hold elevated permissions indefinitely. Instead, implement network-layer MFA on sensitive protocols, systems, and activities, granting just-in-time access only after an identity with a confirmed business need completes verification – and only for as long as necessary. After the necessary window closes, automatically revoke elevated permissions to eliminate the persistent privileged access that allows attacks to escalate quickly without triggering alarms. 

Dynamically Adapt Protection with a Deterministic Policy Engine  

Just as modern environments – and the threats targeting them – are too dynamic for rigid IR playbooks, they’re too fluid for static policies. To ensure real-time containment happens automatically, policies must adapt as the network changes. By maintaining always-current network visibility and feeding up-to-date insights into a deterministic automation engine, enterprises can keep enforcement current and tied to observed business need without taking on constant rule maintenance and operational debt.  

Strengthen Cyber Resilience and Automate Containment with Zero Networks  

Cyber resilience isn’t just about bouncing back from an attack – it’s about staying operational when an incident occurs. By building a closed-by-default architecture, organizations can regain a defensive advantage, proactively containing security breaches regardless of how quickly detection and response workflows are initiated.  

Zero Networks’ automated, identity-driven microsegmentation gives enterprises a modern solution for cyber incident recovery and resilience by combining comprehensive network segmentation, deterministic automation, identity-based access controls, and network-layer MFA for just-in-time access.  

Decoupling breach containment from detection rewrites the traditional IR playbook. When containment happens automatically, cyber incident recovery no longer sits at the finish line of a reactive relay race; with Zero, containment is a built-in architectural feature, so resilience is the default posture.  

See for yourself how Zero Networks stops breaches automatically, revolutionizing incident response to strengthen cyber resilience – request a demo.   

The issue isn’t that third parties can get in – the gig economy and SaaS integrations are only becoming more central to enterprise operations. But when threat actors can abuse third-party credentials to log in and effectively access 100% of your environment within two hops, third-party access often introduces an untenable level of business risk in the name of operational ease, and it can only be addressed at the architectural level. 

We’ll explore how excessive third-party access can lead to supply chain attacks, the governance challenges that typically stand in the way of cyber resilience, and five best practices to limit third-party access and keep every attack contained automatically.  

Key Answers  

• Why is third-party access risky? Third-party identities (whether human or machine) are typically provisioned once and rarely re-evaluated. A vendor login, a contractor’s VPN account, or an OAuth token connecting SaaS platforms often carries far more reach than is operationally necessary; if that identity is compromised, threat actors inherit the existing access. 
• How does a Zero Trust architecture help contain supply chain attacks once an attacker is inside? A Zero Trust architecture treats every connection as potentially risky; foundational Zero Trust strategies like microsegmentation and identity-based access controls ensure that third-party identities are constrained to a pre-defined scope and unable to move laterally. This means that attacks are automatically contained to the initial point of compromise, regardless of how they start.  
• What are best practices for preventing supply chain attacks? Supply chain attacks are inherently difficult to detect or block at the perimeter, so the strongest defense is limiting the potential impact of a breach to prevent enterprise-wide compromise: implement microsegmentation to block lateral movement; enforce identity-based access controls for human, machine, and AI identities; modernize secure remote access by replacing VPNs with ZTNA; apply just-in-time MFA for all privileged access; maintain real-time visibility into what every third-party connection can reach.  

What Is a Supply Chain Attack?  

A supply chain attack is a cyberattack that targets a trusted third party – such as a software vendor, service provider, SaaS integration, or contractor – and exploits existing access to breach a target environment. 

Third-Party Breach vs. Supply Chain Attack: What’s the Difference?  

While third-party breaches and supply chain attacks are related, they describe incidents with different levels of impact:  

• A third-party breach refers to any cyber incident where a vendor, contractor, or partner is compromised via a third-party attack vector – even if that compromise never reaches an organization’s environment.  
• A supply chain attack is where threat actors leverage a third-party breach as a steppingstone to impact downstream users or clients.  

Practically speaking, the third-party breaches that make headlines are usually supply chain attacks, but not every third-party breach escalates into a supply chain attack.  

How Third-Party Compromise Threatens Business Continuity  

When a vendor, contractor, partner, or third-party app connects to an enterprise environment, they inherit some level of trust – in most networks, that trust is broad by default. Third-party connections are often granted access that matches the broadest operational need the relationship might ever require, and those permissions tend to accumulate over time.  

Standing privileges, persistent connections, and broadly reachable internal systems create conditions where a single compromise upstream can rapidly expose an entire environment downstream, and attackers know it. Almost half of the breaches investigated for Verizon’s 2026 DBIR Report involved a third party – a 60% increase year-over-year.  

Real-World Examples: Supply Chain Attacks via Third-Party Compromise  

When a third party is compromised, attackers can move laterally to target environments directly via stolen credentials or inject malicious code into a trusted system to execute software supply chain attacks. Some of the most well-known examples of supply chain attacks illustrate how a third-party breach can ultimately threaten business resilience:  

• Target (2013): Target experienced a massive data breach, impacting roughly 110 million customers, when a third-party HVAC vendor’s credentials were compromised. Although Target’s systems did issue multiple alerts regarding malicious activity, Target’s SOC disregarded the alerts amid a sea of noisy warnings. Alert fatigue contributed directly to the breach’s scale, which ultimately shone a light on the risk of supply chain attacks for the industry.  
• SolarWinds (2020): In one of the largest supply chain attacks to date, adversaries injected a backdoor into one DLL in SolarWinds’ Orion product, which was then delivered through software update to roughly 18,000 organizations. From there, attackers chose specific organizations to compromise further, typically leveraging credentials that allowed them to use legitimate remote access (such as VPNs and RDP servers), which largely hid the attack from detection and response tools.   

In both cases, implicit third-party trust materially enabled the supply chain attack’s ultimate impact. 

Third-Party Vulnerabilities: Governance Challenges  

Most organizations address third-party access risks by layering on familiar hurdles: security assessments are completed before onboarding, access requires authentication, sessions are logged, and contracts may even include security requirements. But traditional governance models weren’t built to keep pace with how many vendors, contractors, and integrations now touch the average enterprise environment, creating an evolving set of challenges for security teams:  

• Exposure accumulates faster than security gaps are remediated: According to the 2026 Verizon DBIR, it takes almost eight months to resolve just 50% of weak passwords and permission misconfigurations for third parties, and 37% of third parties have MFA disabled on at least one admin account, despite a policy requiring it. With new vendors and integrations added constantly in dynamic enterprise environments, those gaps compound into a complex web of third-party risk.  
• Third-party identities lack ongoing oversight: Research shows that many organizations lack a unified view of SaaS connections and vendor integrations, meaning they can't quickly answer where a given vendor has access, what that access actually enables, or how to revoke it cleanly if needed. Since most third-party governance is front-loaded, a lack of ongoing visibility leaves security teams blind to the full scope of risk exposure.  
• Integration ownership erodes over time: Vendor relationships and third-party integrations often outlast the tenure of the person or team that set them up, but third-party access is rarely revisited after it’s provisioned. In one Unit 42 investigation into a single SaaS integration, researchers uncovered nearly 100 additional third-party integrations connected to the same platform – many dormant, unmonitored, or tied to employees that had already left the organization.  

Governing third-party access at the entry point without determining what happens inside the network – after initial access – leaves a structural gap between enforcement and real risk exposure.   

Tightening the perimeter without constraining internal reachability leaves the most consequential part of the attack chain largely unaddressed. Closing that gap requires shifting governance from a point-in-time access decision to a continuous architectural constraint. 

5 Best Practices for Third-Party Access Governance and Supply Chain Attack Containment  

Enterprises must govern what any third-party identity can reach, keep that governance current as the network evolves, and proactively limit internal access paths. Applied consistently across vendors, contractors, and integrations, a closed-by-default architecture drastically reduces the reach of any compromised identity, regardless of how the initial breach occurs.  

1. Implement Microsegmentation for Proactive Breach Containment  

Microsegmentation isolates every asset inside its own secure zone. When internal access paths are closed by default and only available if there’s a specific business need, unauthorized lateral movement is prevented structurally, and the blast radius of any breach is automatically constrained. A granularly segmented architecture removes the dependency on detection to contain supply chain attacks by proactively cutting off the internal trust and reachability that attackers rely on. 

2. Enforce Identity-Based Access Controls  

Third-party access should be narrowly scoped to what’s operationally necessary with enforcement tied to identity. This is a fundamentally different posture than logging and monitoring broad access; instead, a compromised vendor credential doesn't open up access to the entire network – it opens a narrow, policy-defined slice of it. When layered on top of a granularly segmented network architecture, identity-based controls effectively mitigate the identity weaknesses that play a material role in nearly 90% of cyber incidents.  

3. Replace VPN with a Modern Secure Remote Access Solution 

VPN and RDP access remain the top connection types sold by initial access brokers, accounting for the majority of pre-compromised access available on underground markets. This is the same pattern that made SolarWinds so difficult to detect – because third-party remote access is treated as an already-trusted route, it’s a dependable path into target environments for attackers. A modern secure remote access solution should replace standing VPN tunnels with access granted based on identity and context, combining the security of ZTNA with the speed of VPN. This way, remote third parties get the access they need but the rest of the network stays invisible by default.  

4. Add Just-in-Time (JIT) MFA for Privileged Access  

Persistent privileged access to sensitive systems leaves organizations exposed via compromised credentials, even if identity-based access controls are enforced – and traditional MFA doesn’t always close that gap. Mandiant’s 2026 M-Trends Report found that stolen OAuth and refresh tokens often remain valid after logout, allowing attackers to hijack sessions without triggering an MFA alert at all. By implementing network-layer MFA on all privileged pathways, enterprises can enforce just-in-time verification that opens privileged access temporarily for pre-approved identities.  

5. Maintain Real-Time Network Visibility That Informs Dynamic Access Policies  

Periodic reviews can’t keep pace with how quickly vendor relationships and integrations change; OAuth tokens accumulate across SaaS integrations, API keys get issued and forgotten, and vendor accounts outlive the contracts that authorized them – before security teams can effectively govern third-party access, they need to fully understand it. Enterprises should maintain a real-time network map that shows all access paths and identity activity, enabling adaptive policy creation and enforcement grounded in observed network behavior.  

Strengthen Supply Chain Resilience: Build a Closed-by-Default Architecture with Zero Networks 

Modern business operations require third-party connections, but that doesn’t mean enterprises have to accept an untenable – and often hidden – level of third-party risk exposure. Zero Networks enables security teams to build a network architecture that defines what any third party can reach, how long elevated access persists, and how quickly it can be revoked, shifting third-party and supply chain vulnerabilities from a top resilience hurdle to an easily surmountable one.   

With automated, identity-based microsegmentation, Zero governs reachability based on the identity of every third party, closing access paths by default without slowing the pace of business. Patented network-layer MFA extends just-in-time verification to the privileged protocols and admin pathways that traditional MFA leaves exposed, while our modern secure remote access solution combines the speed of VPN with the security of ZTNA, delivering third-party access to pre-approved apps and services only.  

The result is a self-defending architecture where a compromised vendor credential, hijacked integration token, or overprivileged service account has nowhere to go, ensuring third-party compromise doesn’t escalate into disruptive supply chain attacks. See for yourself how Zero Networks narrows third-party trust to what’s actually needed and proactively contains any attack – request a demo.  

The collaboration gives Exclusive Networks' Hong Kong resellers a fast path to automated, identity-driven microsegmentation that verifies every connection and stops lateral movement, human or AI, within seconds. The goal is to help local enterprises strengthen resilience without launching complex, multi-year security projects, combining Zero Networks' automation and agentless-first architecture with Exclusive Networks' regional expertise and partner ecosystem.

Adam Hofeler, VP Sales & GTM at Zero Networks, commented:

As AI reshapes how organizations detect, manage, and respond to risk, Exclusive Networks partnered with Zero Networks to meet that reality head-on with preventative, identity-based control across different layers where AI introduces risk: governing which cloud AI services can be accessed, enforcing least-privilege boundaries on AI agents, segmenting model infrastructure so it can't be tampered with, cutting off the connectivity that enables lateral movement, and surfacing compliance gaps in real time.

A Zero Networks technical workshop hosted by Exclusive Networks last month, attended by their Hong Kong resellers, showed that the local community is ready to move beyond detection-focused security and toward prevention-focused models. Attendees focused on how far an attack,  human or AI, could spread inside their networks, and how to contain it without launching a tedious and lengthy project.

Empowering Reseller Partners

This partnership is designed with reseller partners in mind. By combining Zero Networks’ rapid deployment model and Exclusive Networks’ value-added services, partners will benefit from:

• Access to cutting-edge Zero Trust technology that meets the growing demand for scalable, identity-based security, helping organizations defend against AI-speed attacks, protect uptime, and contain breaches before they spread, so a breach never has to mean business disruption
• Accelerated deployment timelines, enabling faster customer onboarding and quicker ROI.
• Enhanced margin opportunities through deal registration, marketing support, and training incentives.
• Comprehensive technical enablement, including certifications, pre-sales support, and co-branded campaigns.
• Strategic alignment with a trusted distributor, helping partners build credibility and close deals more effectively.

About Exclusive Networks

Exclusive Networks (EXN) is a global cybersecurity specialist that provides partners and end-customers with a wide range of services and product portfolios via proven routes to market. With offices in over 45 countries and the ability to serve customers in over 170 countries, we combine a local perspective with the scale and delivery of a single global organisation.

Our best-in-class vendor portfolio is carefully curated with all leading industry players. Our services range from managed security to specialist technical accreditation and training and capitalize on rapidly evolving technologies and changing business models. For more information visit www.exclusive-networks.com.

Almost everyone has picked up a Rubik's Cube at some point. 

The first few moves feel productive. You line up a row of colours, complete part of a side, and convince yourself you're making progress. Then one more twist undoes everything you just achieved. 

The challenge isn't that the cube is impossible to solve. The challenge is that every move affects something else. 

Kubernetes environments often evolve in exactly the same way. 

A cluster starts simple. A few workloads. A handful of namespaces. A small platform team that understands every application running inside it. Developers know what their services communicate with. Security teams understand the environment. Documentation is current. Dependencies are well known. 

Then the business grows. 

New applications are deployed. Additional teams adopt the platform. New services are integrated. More namespaces appear. More clusters are introduced. Temporary projects become permanent services. Acquisitions bring in new workloads. Migrations create new dependencies. 

As the environment grows, responsibilities also begin to spread across application, platform and security teams. Each team makes decisions within its own part of the environment, but those decisions inevitably affect the others. 

Before long, what started as a neatly organised environment begins to resemble a Rubik's Cube that has been twisted thousands of times. 

Every team understands part of the environment. Few understand all of it. 

Kubernetes Was Never Designed to Stay Small 

One of Kubernetes' greatest strengths is its ability to scale rapidly. 

Development teams can deploy applications faster than ever. New environments can be created in minutes. Infrastructure becomes highly dynamic and workloads are constantly changing. 

But this flexibility introduces new challenges. 

Unlike traditional environments where servers may exist for years, Kubernetes workloads can appear and disappear in seconds. Pods are ephemeral. Services communicate across namespaces. Applications are distributed across dozens or even hundreds of microservices. Workloads often communicate not only within a cluster, but across clusters and with external systems. 

And it isn't just the technology that becomes distributed. Responsibility does too. 

Application teams design the application, deploy it and increasingly define aspects of its networking and security. Platform and IT teams operate the underlying Kubernetes environment. Security teams remain responsible for governance, risk and ensuring that appropriate controls are in place. 

The boundaries between these responsibilities are not always clear. 

If a privileged pod introduces unnecessary risk, who owns the problem? Is it an application issue, a platform configuration issue or a gap in security policy? If a new service introduces an unexpected communication path, who is responsible for identifying it and deciding whether that access should exist? 

As environments grow and development teams move faster, these questions become increasingly difficult to answer. Security teams may be accountable for protecting the environment without having complete visibility into how applications are changing or communicating. Platform teams understand the infrastructure, while application teams often have the deepest understanding of individual workload dependencies. 

Each team understands a different part of the cube. 

Questions that were once simple become surprisingly hard to answer: 

• Which services communicate with each other? 
• Which communications are business critical? 
• Which namespaces should never communicate? 
• Which connections were introduced for testing and never removed? 
• What would happen if access was restricted? 
• Who owns the risk when unnecessary access is discovered? 

In many organisations, nobody can confidently answer all of these questions. 

This isn't necessarily a failure of technology or process. It is the reality of operating a modern Kubernetes environment at scale, where the environment can change faster than teams can collectively understand it. 

Complexity accumulates faster than understanding. 

The Cost of Not Knowing 

Most platform and security teams understand the value of segmentation. 

Yet many Kubernetes segmentation initiatives never move beyond planning. Why? 

Because nobody wants to break production. 

Imagine being asked to implement restrictive Network Policies across a cluster containing hundreds of applications and thousands of workloads. 

Before creating a single policy, you need to understand what traffic is required, what traffic is optional, what traffic is legacy and what traffic represents unnecessary risk. 

That understanding is rarely owned by a single team. Application teams understand their services and dependencies. Platform teams understand the infrastructure. Security teams understand the risk and controls. 

Without that understanding, every policy becomes a gamble. 

Block the wrong communication path and a customer-facing application may fail. Restrict access incorrectly and a business-critical service may stop functioning. The operational consequences often feel more immediate than the security risks. 

As a result, organisations frequently choose the safest operational option. 

They leave access open. 

The business impact of this decision is significant. Excessive connectivity increases attack surface, expands blast radius during incidents, complicates compliance efforts, and makes investigations substantially more difficult. Security teams understand the risk, but reducing that risk often appears riskier than accepting it. 

The organisation becomes trapped between security and operational certainty. 

Why Native Kubernetes Controls Aren't Enough 

Kubernetes provides Network Policies that allow organisations to control communication between workloads. The concept is powerful. 

The implementation is often challenging. 

In smaller environments, manually managing Network Policies may be achievable. In larger environments, the reality becomes much more complicated. 

Different teams create policies. Applications evolve. New services are deployed. Existing services are modified. Documentation becomes outdated. New namespaces and workloads appear. Clusters grow. Additional clusters are added. 

The challenge is that Kubernetes environments never stay still. Development teams continuously ship changes, often at a pace that security teams cannot manually review. A cluster may initially align with CIS Benchmarks or an organisation's internal security baseline, but as new workloads, configurations and communication paths are introduced, the environment can gradually drift away from that intended state. 

This configuration drift is rarely the result of a single major change. It accumulates over time as applications evolve, teams move quickly and exceptions are introduced. At scale, maintaining consistent policy enforcement becomes just as challenging as defining the policies in the first place. 

Over time, organisations accumulate hundreds or even thousands of policy definitions spread across namespaces and environments. Policies that were appropriate when they were created may no longer reflect how applications operate today. 

The challenge isn't creating policies. 

The challenge is understanding whether those policies accurately reflect how applications should communicate and ensuring they continue to do so as the environment changes. 

Most teams can view individual policies. 

Few teams can easily understand the cumulative impact of all policies across the environment, identify where configuration drift has occurred, or determine whether new communication paths have introduced unnecessary exposure. 

Without visibility, organisations are effectively trying to solve a Rubik's Cube while only seeing one side. 

What Happens When a Workload Is Compromised? 

Security teams invest significant effort into preventing compromise. 

Clusters are protected by vulnerability scanners, image scanning solutions, admission controllers, runtime protection platforms, secrets management solutions and RBAC controls. These technologies are essential and form a critical part of a defence-in-depth strategy. 

However, no organisation can guarantee that a workload will never be compromised. 

The more important question is what happens next. 

Imagine a single workload becomes compromised. Not the entire cluster. Not a cluster administrator account. 

Just one pod. 

What can it reach? 

• Can it communicate with other workloads in the same namespace? 
• Can it access services in another namespace? 
• Can it discover internal APIs? 
• Can it reach backend databases? 
• Can it communicate with management services or infrastructure components? 

For many organisations, the answer is unclear. 

By default, Kubernetes environments can provide significantly more connectivity than teams realise. Over time, communication paths accumulate. Some are necessary. Others exist because of historical decisions, temporary exceptions, inherited configurations, or simply because nobody wanted to risk breaking an application. 

An attacker doesn't necessarily need cluster-admin privileges to cause damage. 

They need a path to something valuable. 

Once an attacker gains access to a workload, trusted east-west communication paths can allow them to discover internal services, communicate across namespaces, access backend APIs, interact with databases or explore management components within the environment. 

This is where a contained compromise can become a much broader incident. 

A workload that should have been isolated can become a launchpad for lateral movement. Sensitive services that were never intended to communicate may become accessible. Databases, internal APIs and critical business systems can all become part of the attack path. 

The challenge isn't simply that these communication paths exist. 

The challenge is knowing which ones should exist. 

Without visibility into workload-to-workload communication, it becomes extremely difficult to distinguish legitimate business traffic from unnecessary exposure. 

The risk isn't necessarily the compromised workload. 

The risk is everything connected to it. 

Before organisations can reduce lateral movement, they first need to understand it. 

Seeing the Whole Cube 

The turning point occurs when the environment becomes visible. 

Most Kubernetes tools provide visibility into individual components. Teams can inspect pods, deployments, services, logs and policies. While each of these views is valuable, they rarely provide a complete understanding of how the environment functions as a whole. 

This is similar to studying a Rubik's Cube one square at a time. You can understand individual pieces without understanding how they relate to each other. 

As Kubernetes environments grow, those relationships become increasingly important. 

Security teams need to understand which namespaces communicate with each other. Which communication paths are unrestricted. Which workloads are isolated. Which areas of the environment have no policy enforcement. Which connections are expected and which represent unnecessary exposure. 

But visibility also needs to be shared. 

Application teams understand how their applications are designed. Platform teams understand how the Kubernetes environment operates. Security teams understand the organisation's risk and governance requirements. Giving these teams a common view of communication, access and policy helps turn fragmented knowledge into coordinated decision-making. 

This is where the Zero Networks Kubernetes Access Matrix becomes valuable. 

Rather than analysing workloads and policies individually, the Access Matrix provides a visual representation of communication and policy enforcement across the cluster. Teams can identify unrestricted communication paths, isolated namespaces, partially restricted access and areas where no policies exist at all.

Combined with visibility into actual data flows and the existing rule baseline, teams can begin identifying where access is required, where it may be excessive and where ownership or policy decisions are needed.

What previously required teams to piece together information from multiple views becomes visible in a single place. More importantly, the conversation changes.

Teams stop asking: "What happens if we block this?" And start asking: "Why are we allowing this?"

Visibility transforms security from guesswork into decision-making.

And, just as importantly, it gives Application, Platform, and Security teams a common view from which to make those decisions together.

Solving the Cube Layer by Layer 

Nobody solves a Rubik's Cube by looking at it once and immediately making the perfect move. 

They start by understanding the current state of the puzzle. Only then can they begin making deliberate decisions that move them closer to the end goal. 

Kubernetes security follows a similar journey. 

The first challenge is understanding how workloads communicate. Which namespaces talk to each other? Which connections are required? Which exist purely because they were never removed? Which could become attack paths if a workload is compromised? 

Without those answers, implementing segmentation becomes difficult and potentially disruptive. 

This is where Zero Networks starts. 

Using eBPF-based monitoring, workload communication is automatically discovered and mapped across the cluster. The Kubernetes Access Matrix provides a clear view of namespace-to-namespace relationships, helping teams understand how applications actually communicate rather than relying on outdated documentation or assumptions. 

Once communication becomes visible, organisations can begin making informed security decisions. 

Instead of attempting to segment the entire environment at once, teams can progressively introduce least-privilege controls using Kubernetes-native Network Policies. Existing policies can be synchronised from Kubernetes into Zero Networks, giving Security and Platform teams a central view of the policies already defined across the environment. 

Policies can then be reviewed and approved within Zero Networks before enforcement, adding a governance layer around Kubernetes-native controls. This allows application teams to continue defining the policies their applications require, while giving Security and Platform teams the oversight needed to ensure those policies align with the organisation's security requirements. 

Where appropriate, policies can also be automatically enforced by Zero Networks, reducing the operational effort required to implement and maintain segmentation at scale. 

This becomes particularly important as Kubernetes environments continue to change. New workloads, namespaces and communication paths are constantly introduced, making it difficult for Security teams to manually keep policies aligned with the environment. 

With automated learning capabilities being introduced, Zero Networks can take this further by learning how workloads communicate and helping create policies based on observed behaviour. Combined with automated enforcement, this can help security controls keep pace as applications and Kubernetes environments evolve. 

The result is a Kubernetes environment that looks very different from where it started. 

What was once a flat environment with unclear communication paths becomes an environment where connectivity is understood, governed and intentionally controlled. 

Attack surface is reduced. Lateral movement opportunities are limited. 

And perhaps most importantly, application, platform and security teams gain a common framework for improving security without unnecessarily slowing development or risking disruption to production. 

Just like solving a Rubik's Cube, success doesn't come from making more moves. 

It comes from understanding the puzzle, making the right move and continuing to adapt as the puzzle changes. 

Final Thoughts 

The challenge with a Rubik's Cube isn't that it's impossible to solve. 

The challenge is understanding how every piece relates to every other piece. 

Kubernetes environments present a remarkably similar problem. As clusters grow, complexity accumulates faster than understanding. Communication paths multiply, applications continuously change and responsibility becomes distributed across Application, Platform and Security teams. 

Most organisations don't struggle because Kubernetes lacks security controls. Kubernetes already provides powerful capabilities such as Network Policies. The challenge is maintaining visibility and governance as the environment evolves — understanding what should be allowed, what can be restricted and how policies can be consistently enforced without disrupting business operations. 

Zero Networks provides the visibility and governance layer that helps bring those pieces together. By providing a shared understanding of workload communication and Kubernetes-native policies, teams can identify unnecessary exposure, coordinate policy decisions and progressively enforce least-privilege segmentation at scale. 

The goal isn't simply to solve the cube once. 

It's to keep it solved as the environment continues to change. 

The timing of this trend directly coincides with the rise of frontier AI models like Mythos and Daybreak, which represent a growing class of AI-driven vulnerability research tools capable of finding, analyzing, and generating exploits for vulnerabilities at a speed and scale no human team can match – a development that led Open AI to call on CISOs to implement network segmentation.

IBM’s 2026 Cost of a Data Breach report spells out the financial weight behind this rise in machine-speed vulnerability exploitation: AI-driven attacks add an average of $1 million to the cost of a breach as new velocity and scale reshape the economics of cyber incidents.  

When patches are released at an impossible volume, the window between vulnerability discovery and exploit shrinks to nothing, and security teams face mounting pressure to protect uptime. In response, enterprises need to fundamentally rethink vulnerability management, prioritizing proactive containment rather than faster patching.  

Key Answers 

• What are the latest trends in vulnerability management? Vulnerability management is shifting from patch-speed to containment-first strategies. Frontier AI models like Mythos and Daybreak have compressed discovery-to-exploit timelines beyond what patch management cycles can match, making proactive containment a key defense.  
• Are there any new tools or techniques for vulnerability mitigation? Security teams can build a standing protection layer that limits what an exploited asset can reach with identity-based microsegmentation, buying more time to test and validate patches before deploying them without leaving the business exposed to critical vulnerabilities in the interim. Tools like Zero Networks' Breach Map help identify exposure and uncover targeted opportunities for mitigating a vulnerability.  
• How can enterprises manage the rise in discovered vulnerabilities and patches driven by frontier AI models? Security teams can't realistically test and validate every patch fast enough to keep up with frontier AI-driven discovery volume. Rather than rushing unvalidated fixes into production, enterprises should close unnecessary access paths by default, rely on identity-based microsegmentation to buy the time needed to patch safely, and prioritize remediation based on reachability.  
• What are some best practices for proactively defending against vulnerabilities? Measure blast radius to prioritize business exposure over CVSS severity alone, close unnecessary access paths by default with microsegmentation, apply targeted access rules to buy time while a patch is tested, and ensure containment is enforced architecturally so protection doesn't depend on detecting an exploit first. 

What Is Vulnerability Management?  

Vulnerability management is the ongoing practice of identifying, assessing, resolving, and verifying fixes for security weaknesses across an environment.  

Continuous vulnerability management is one of 18 critical security controls included in the Center for Internet Security (CIS) Cybersecurity Framework – in theory, an always-on cycle of vulnerability scanning, assessment, and remediation enables organizations to remediate vulnerabilities before they can be exploited. However, AI-driven attacks have challenged the assumptions that underpin traditional vulnerability management cycles.  

The Vulnerability Management Lifecycle 

Some vulnerabilities pose a greater threat than others. The vulnerability management lifecycle is meant to help organizations catch and fix the most urgent problems – it typically revolves around four core stages:  

1. Identify: Continuous scanning and asset discovery keep a network map up to date, enabling security teams to effectively identify assets that could be impacted by known and newly disclosed vulnerabilities.  
2. Assess and prioritize: Security teams score vulnerabilities by severity (often using the Common Vulnerability Scoring System [CVSS]), exploitability, and business context to determine what needs attention first. 
3. Resolve: The most common approach to resolving a vulnerability is remediation, often achieved by applying a patch. But in some cases, a patch isn’t yet available or can’t be applied without risking disruption. When full remediation isn’t possible, organizations may resolve a vulnerability through mitigation – applying controls that make the vulnerability significantly harder to exploit or minimize the impact of exploitation, even if the vulnerability technically still exists.  
4. Verify: Whether the vulnerability is fully remediated or mitigated, teams confirm the fix closed the exposure to the greatest extent possible without introducing new risk or breaking dependent systems. 

How Patch Management Works  

Patching is a tried-and-true staple in vulnerability remediation. Until now, patch management has followed a predictable cycle: a vendor discloses a vulnerability and it’s assigned a CVE, the vendor issues a security patch, and organizations test the patch before deploying it into production. Testing is a critical step in the cycle – an unvalidated fix can break critical connections and disrupt operations. Once testing clears, the patches are typically deployed on a scheduled cadence, such as Microsoft's monthly Patch Tuesday; the team verifies the fix afterward.  

This cycle was built on the assumption that the time between a vulnerability's discovery and its mass exploitation would be measured in weeks or months, giving defenders room to test patches safely before deploying. But that timeline has collapsed in the wake of frontier AI models such as Mythos and Daybreak.  

“The announcement in April 2026 of a frontier model that managed to find thousands of high-severity vulnerabilities—including some in every major operating system and web browser—is a signal warning to security teams … In the hands of attackers, these tools will collapse the time between vulnerability discovery and exploitation. Attackers are abandoning human speed for machine speed.”  

IBM, 2026 Cost of a Data Breach Report  

Why Frontier AI Broke the Traditional Patch Management Model  

Even before AI-enabled discovery and exploitation upended the threat landscape, security teams were struggling to keep up with patching – and the data proves it. Now, as attackers increasingly weaponize AI, traditional patch management workflows are an untenable solution.  

So, the rapid acceleration in vulnerability discovery is forcing already strained patch management cycles to a breaking point. For example, Microsoft released its largest Patch Tuesday ever in July of 2026: 570 fixes, including three zero-day vulnerabilities – in total, a more than 3x increase since April.  

The practical impact for defenders that continue to rely on patching as their core vulnerability management strategy is a forced tradeoff: you can patch immediately and manage updates that contain thousands of fixes at a time, or wait and validate the highest priority patches while accepting risk exposure in the interim. In either case, operational continuity is at risk.   

“We cannot patch Mythos or Daybreak away. Discovery is infinite now. We can find thousands or even millions of new vulnerabilities every day. And the patching time we have is finite – it's impossible to digest all those patches, validate that a new patch isn't generating a business impact, and move fast enough. Even with prioritization, it will always be late. Because the time AI needs to generate an exploit will always be faster than the time you need to prioritize, test, and apply the patch.” 

Albert Estevez, Field CTO, Zero Networks 

Enterprises understand the importance of taking action – 85% of organizations plan to increase security spending in response to frontier AI model threats. But simply throwing more budget at existing strategies won’t give defenders a reliable advantage. Instead, organizations need a containment-first architecture that stops attacks by design – regardless of their speed or initial access vector.  

How to Prevent Vulnerability Exploitation: 4 Best Practices for Preemptive Cyber Resilience  

Gartner noted after Mythos launched that "CIOs must tell their boards they will have to recalibrate their risk appetite for vulnerabilities because faster patch cycles won't be enough."  

The need for vulnerability management and patching hasn’t gone away, but the discipline must evolve – rather than chasing every vulnerability, organizations should prioritize cyber resilience and proactively limit the blast radius of vulnerability exploits by following these 4 best practices.  

1. Measure Blast Radius for Tailored Vulnerability Risk Insights 

While CVSS measures theoretical severity, it doesn’t clarify your business’ actual exposure. To assess the potential real-world impact of a vulnerability exploit, enterprises should map their internal network to discover reachable assets, how they connect, where attackers could move, and what they could reach. Free tools like Zero Networks’ Breach Map are the fastest way to uncover your blast radius and learn where an exploit could cause the most damage.  

2. Close Unnecessary Access Paths by Default with Microsegmentation  

Vulnerabilities give attackers an initial foothold but excessive lateral movement exposure is what turns minor cyber incidents into widespread breaches. Zero Networks' 2026 Lateral Movement Exposure Report found 80% of enterprise servers are reachable from anywhere on the network, and 85% of internal systems are directly accessible from a single compromised host. In other words, attackers typically inherit broad internal access regardless of how they initially breach the network – whether that’s via a vulnerability exploit or any other vector.  

Microsegmentation eliminates the internal pathways that exist for convenience rather than verified business need. Even if an attacker manages to gain initial access to a granularly segmented network, they’ll hit an immediate dead end because lateral movement is prevented by design.  

3. Buy Time to Patch Safely with Network- and Identity-Based Controls  

If a patch introduces outage risks of its own, it’s not a true fix. While patch availability is skyrocketing, security teams can’t realistically validate every patch quickly enough to remediate all newly discovered vulnerabilities. Even if bandwidth weren’t a barrier, OT and legacy systems can't always be patched on short notice without planning significant change windows that risk production impact. 

Organizations that have already implemented identity-based microsegmentation have built a foundational buffer against vulnerabilities; when a specific flaw poses a particularly urgent business risk, security teams have the tools in place to build a targeted rule that addresses the exposure until a patch can be deployed safely.  

For example, B. Riley Financial faced a Microsoft Outlook vulnerability involving outbound SMB traffic that left the firm exposed with no patch available from the vendor. Waiting for a fix meant leaving the exposure open, so they worked with Zero Networks to deploy a targeted rule blocking outbound SMB traffic from Outlook to the internet, closing the specific path the vulnerability relied on.   

“Having microsegmentation in place has really given us a precautionary protection layer so we can delay applying some of these patches. This gives us plenty of time to test, make sure everything’s working, and then apply it, but still have that protection layer in place.” 

- Aaron Goodwin, CISO, B. Riley Financial     

4. Automate Threat Containment Independent of Detection  

Last year, 29% of vulnerabilities in CISA’s KEV catalog were attacked before public disclosure, meaning exploitation was underway before defenders knew what to look for. Containment that depends on catching an exploit in progress inherits the same speed disadvantage as patching: a human SOC – or even an automated detection tool – still has to notice, verify, and respond before damage spreads. The probability that every step in the detect-and-respond chain works flawlessly and fast enough to stop machine-speed threats? Near zero.  

Built-in containment removes the dependency on detection and response workflows. If access paths are closed by default and enforcement doesn't require a trigger, a compromised asset can't move laterally regardless of whether the malicious activity is immediately detected. That's the difference between a reactive strategy and architecture that makes the attack's next move impossible from the start – and it becomes more consequential every month frontier AI compresses the window for containment.  

Build Proactive Vulnerability Protection in the AI Era with Zero Networks  

Zero Networks’ automated, identity-based microsegmentation agentlessly orchestrates native firewalls to isolate every asset, preventing lateral movement and building a containment layer against vulnerability exploits – no matter how quickly they’re executed.  

Unlike reactive tools that depend on known indicators, Zero builds a proactive security posture that prevents attacks from spreading at the architectural level, effectively mitigating the impact of vulnerabilities so security teams can protect business continuity while buying time to patch safely.  

Get a firsthand look at how you can build a closed-by-default architecture and stay resilient against machine-speed threats – request a demo.