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.  

Manufacturing organizations can’t afford to rely on detection and response. Security teams know microsegmentation is the answer for building preemptive cyber resilience, but legacy approaches have a track record of failing manufacturing environments specifically: complex, agent-based deployments strain lean IT teams, manual policy management can't keep pace with production schedules, and the fear of breaking a live system stalls projects before they start. 

This roundup features three Zero Networks customers that successfully deployed identity-based microsegmentation to strengthen cyber resilience without adding operational complexity. We'll walk through how these manufacturers eliminated downtime risk during implementation, met cyber insurance requirements, and extended protection to legacy systems without disrupting the infrastructure their operations depend on. 

Non-Disruptive Network Segmentation: Eliminating Outage Risk 

Many manufacturers implementing microsegmentation fear that a new security tool might lead to operational disruption. Reconfiguring network architecture often risks the exact outcome security teams are trying to prevent: unplanned downtime. That’s why half of manufacturing security leaders point to implementation complexity as their top microsegmentation concern, followed closely by disruption to existing operations. 

Real-World Examples 

Vermeer manufactures the industrial and agricultural equipment that keeps global communities fed and fueled. The security team was on a mission to find a virtual airgap solution that could enhance overall security, but with a complex network and no appetite for downtime, Vermeer couldn’t rely on traditional tools or manual approaches.  

Atlantic Constructors – a commercial construction, prefabrication, and industrial services provider – faced a version of the same concern. Rising cyber insurance requirements pushed the team to look for a microsegmentation solution, but every other product they evaluated threatened a long, complex implementation that could disrupt the business before it delivered any protection. 

The Solution: Agentless Deployment and Deterministic Policy Automation 

Vermeer and Atlantic Constructors achieved granular protection without the outage risks and implementation complexity of legacy microsegmentation tools thanks to Zero’s automated, agentless approach:  

• With an agentless architecture, Zero Networks orchestrates native, host-based firewalls already present in the environment, avoiding the latency and integration risk that comes with agent-based tools. 
• A defined learning period allows Zero's automation engine to observe real traffic and build policies from actual network behavior, removing the upfront manual burden.  
• Policies generated and enforced via deterministic, human-on-the-loop automation reflect what the network actually needs to keep running securely, not a best guess that risks blocking legitimate traffic. 

“[Zero Networks] is an agentless solution, so it’s relying on the network firewall that’s native to Windows which allows us to control both allows and denies to the PC or the host that the connections are going between. And then on top of that, they have the automatic engine that is learning, and taking that data, and translating it into rules.”  

Justin Manifold, Senior IT Security Engineer, Vermeer  

By leveraging Zero’s non-disruptive, deterministic solution, Vermeer segmented their network in 30 days while maintaining 100% of legitimate traffic. Similarly, Atlantic Constructors deployed Zero in just 15 minutes, ultimately improving network performance and reliability by 100%. 

Legacy Systems and Vendor Lock-In: Extending JIT MFA Everywhere  

Manufacturing environments are built on decades of accumulated infrastructure that many security tools aren’t designed to address. Microsegmentation vendors that require complex configurations and proprietary MFA tools force manufacturers to accept coverage gaps or introduce additional complexity into an environment that can't afford failure.  

Real-World Examples 

Mikron is the leading partner for high performance production systems, building the complex components used to produce the things that matter – pharmaceutical devices, precision medtech, automotive parts, and more. With frequent changes to the environment, maintaining consistent policies via Group Policy Objects (GPOs) proved nearly impossible, but other segmentation vendors couldn’t easily integrate with Mikron’s existing environment and required proprietary MFA systems that threatened continuity.  

Similarly, Vermeer needed a way to protect privileged access without introducing the kind of friction that would disrupt the production and engineering workflows already running smoothly. 

The Solution: Just-in-Time Network-Layer MFA  

Mikron and Vermeer closed identity least privilege gaps without risking critical connections or leaving legacy systems vulnerable by enforcing Zero’s network-layer MFA for just-in-time access:  

• Network-layer MFA extends authentication to admin protocols and privileged activities without touching the underlying legacy systems or introducing new failure points. 
• Seamless integration with existing infrastructure means a consistent user experience rather than forcing a second, proprietary authentication system into the environment. 
• Added on top of identity-based microsegmentation, network-layer MFA effectively prevents compromised credentials from delivering access to production-critical systems.  

For Vermeer, the addition of network-layer MFA gave the security team confidence to strengthen privileged access protection while maintaining the stability its always-on manufacturing environment requires. Since Zero Networks integrated seamlessly with Mikron’s environment – no proprietary tooling or rip-and-replace approach required – Mikron achieved the comprehensive protection they needed without risking operational disruption, completing full network segmentation rollout within weeks and at a fraction of the effort GPOs demanded.  

Cyber Insurance and Compliance Requirements: Validating Protection 

In the industrial sector, cyber insurance has become table stakes, and carriers are asking for proof that cybersecurity strategies translate to real resilience. Over two-thirds of manufacturers say their cyber insurance carrier requires network segmentation specifically, and that mandate is only getting harder to satisfy with labor-intensive legacy approaches.  

Real-World Example 

Atlantic Constructors' search for a microsegmentation solution was driven directly by rising cyber insurance requirements. The team needed to strengthen its overall security posture in a way that would hold up to scrutiny – and once Zero Networks was deployed, the organization put it to the test.  

The Solution: Cyber Resilient Segmentation Architecture 

With Zero’s identity-based microsegmentation in place, Atlantic Constructors unlocked the compliance evidence they needed without creating a new manual burden for the security team:  

• Continuous, automated policy enforcement generates audit-ready evidence of segmentation as a byproduct of normal operation, rather than requiring a separate compliance exercise. 
• Because policies enforce least-privilege access by default, the pathways attackers and penetration testers rely on are closed structurally, before a test ever finds them. 
• Real-time network mapping demonstrates current resilience posture, unlocking up-to-date visibility into real risk exposure.  

“After we got Zero, I decided to run another pen test to see how things were working, and we literally could not get it to penetrate one of the machines we were testing … We did some speed tests and [network] performance probably doubled. Reliability went up 100%.” 

Jim Paolicelli, IT Director, Atlantic Constructors 

With the impact of microsegmentation validated through penetration testing, Atlantic Constructors ultimately traded multiple legacy platforms for Zero Networks’ unified solution.  

Built-in Cyber Resilience and Supply Chain Stability with Zero Networks  

From outage risks due to implementation complexity to legacy system constraints and beyond, manufacturers have long faced microsegmentation hurdles that cause initiatives to stall. Zero Networks removes those barriers and enables manufacturing organizations to secure complex environments, protect legacy systems, and boost operational resilience in days – not years.  

By delivering automated, identity-driven microsegmentation that seamlessly integrates with existing infrastructure combined with network-layer MFA for privileged access, Zero removes the manual effort and outage concerns that make legacy tools too risky to industrial orgs.  

See how Zero Networks makes cyber resilience practical for manufacturers – request a demo.  

We’ll break down why machine identities are uniquely risky, how attackers exploit overprivileged service accounts and AI agents, and what enterprises can do to expand least privilege enforcement to every network identity – from human users and admins to service accounts and AI agents.  

Key Answers 

• What are non-human identities (NHI)? An identity used by software rather than a human user to authenticate and access systems, such as service accounts, AI agents, cloud workloads, or APIs. 
• How can security teams protect service accounts and machine identities from abuse? By discovering every identity in the environment via an automated solution, scoping access to observed operational need identified during a learning period, and enforcing granular, identity-based access controls, backed by automated policy management that adapts as the network changes. 
• What are some best practices for AI agent access control? Treat every agent as its own governed identity: discover what's running, scope access to what each agent's function requires, and apply the same identity-based restrictions used for human users rather than granting AI agents ambient trust. 
• How can enterprises enforce least privilege across non-human identities? Continuously map what every identity – including service accounts and AI agents – can reach, restrict identities to pre-approved assets and logon types, and automate policy updates so access stays current as the network changes, rather than relying on static rules or manual review. 

What Is Non-Human Identity (NHI) Security?  

NHI security is the practice of applying the same governance to machine identities, service accounts, API keys, workload identities, and AI agents that enterprises enforce for human users.  

While “machine identity” originated as a narrower term for cryptographic credentials and “non-human identity” was introduced as a broader umbrella term, both are used to cover the same expanding category in practice: identities used by software (rather than human users) to authenticate and gain access to systems.  

This category encompasses a range of subtypes, with some of the most common including: 

Although every non-human identity carries a unique risk profile, two types in particular represent the greatest enterprise threat today: service accounts and AI agents.   

Machine Identity Security Risks and Challenges  

Like most non-human identities, service accounts and AI agents share an underlying set of risks: they run on standing access that’s rarely reviewed after being provisioned, and they’re built to operate in the background, so misuse goes unnoticed. What differs is how fast access accumulates and how well-established the governance gap already is – service account sprawl is an established vulnerability, while the full scope of the agentic attack surface is still unfolding. Some of the most pervasive NHI risks span: 

• Static credentials with no MFA checkpoint: AI agents operate across open-ended workflows without a human approving each step – they authenticate, hold tokens, and gain access autonomously through accumulated entitlements, yet less than half of organizations report securing NHI in AI workflows. Likewise, service accounts run on static credentials that are rotated quarterly at best, with some organizations even setting them to never expire at all, since rotating a credential tied to a live production process carries operational risk of its own.  
• Standing permissions and privilege creep: Many service accounts are over-provisioned with domain admin or enterprise-wide access that’s not operationally necessary. The same is true for agentic identities – the more tasks AI agents are assigned, the more entitlements they accumulate through policy drift, tool chaining, and expanding scope.  
• Shadow AI and the agentic governance gap: Nearly 80% of enterprises are already deploying AI agents internally, and roughly two-thirds have no governance policies in place to manage them. That gap starts as a visibility problem – security teams can’t discover, let alone control the shadow AI running in their environment. Sanctioned or not, agentic adoption is expanding AI attack surfaces faster than organizations can scale governance.  
• Activity evades detections tools: Service account and AI agent traffic blends in with normal activity because it relies on legitimate access. When attackers exploit those permissions to move laterally and access sensitive systems, the threat stays hidden from detection tools since it doesn’t look anomalous.  

How Attackers Exploit Overprivileged Service Accounts and AI Agents: Real-World Scenarios 

The risk of NHI exploitation isn’t theoretical – it’s a documented threat, backed up by real-world attacks.  

Stored Service Account Credentials Create Lateral Movement Pathways  

An incident response investigation carried out by Michael Matok, Incident Remediation & Recovery Lead at Sygnia, uncovered that after attackers breached a vulnerable NetScaler appliance via a widely exploited CVE, service account credentials stored in the compromised systems ultimately allowed attackers to move laterally via RDP.  

What should have been a low-privilege, read-only LDAP bind account had accumulated far more access than necessary, enabling hackers to pivot, change a domain admin account’s password, and take control. In other words, one misconfigured service account turned an initial foothold into a widespread breach.  

Agentic Threats Move Laterally at Machine Speed  

In July of 2026, Hugging Face disclosed an intrusion into its production infrastructure, later attributed by OpenAI to its own models operating autonomously during an internal security evaluation. The models found a zero-day in a package proxy, escalated privileges, and moved laterally through Hugging Face's internal infrastructure – autonomously – until they reached the target data in a production database.  

While the AI vector is distinct, the lesson is similar to takeaways from attacks involving overprivileged service accounts: when networks aren’t built to constrain lateral movement by design, a single vulnerability exploitation can quickly spiral into a business-impacting incident. 

How to Enforce Least Privilege for Non-Human Identities  

Closing machine identity security gaps isn’t a matter of layering on more monitoring to the same static access models. Instead, enterprises need dynamic access enforcement built around how non-human identities actually behave. 

Discover Every Service Account and AI agent in the Environment  

Because service accounts typically operate in the background, they’re notoriously hard to monitor. Similarly, widespread AI adoption – often beyond IT oversight – has created a massive AI agent blind spot in many enterprises. The first step in enforcing least privilege controls for NHI is implementing continuous network monitoring that maps every identity, tool, and connection in the environment, forcing shadow AI and long-hidden service accounts into the spotlight.

Scope NHI Access to Business Need  

Visibility establishes what agents and service accounts can reach; the next step is understanding what access they need to perform their intended functions. By observing real network behavior over time, security teams can identify which connections are genuinely necessary, where provisioned access exceeds true requirements, and where that gap creates the riskiest exposure. With this learned baseline, teams can down-scope access to reflect operational need without breaking key connections.  

“Among organizations that suffered AI-related breaches in this year’s study, 92% lacked proper access controls. This finding reveals a systemic failure to treat identity as mission-critical infrastructure. In the race to deploy AI agents to automate business processes—including security operations defending against frontier AI models—teams must fundamentally transform identity systems to secure not just humans, but NHIs, too.” 

IBM, 2026 Cost of a Data Breach Report 

Enforce Granular, Identity-Based Access Controls 

Granular access controls must apply to every identity – human, machine, or AI. Service accounts, AI agents, and all machine identities should be restricted to pre-approved assets and logon types, ensuring lateral movement and unauthorized access to sensitive systems are blocked even if machine identities are compromised.  

Automate Policy Lifecycle Management with a Deterministic Engine  

Modern enterprise environments are too dynamic for static, IP-centric policies that govern a location rather than an identity; that’s why Gartner says it’s time to pivot from IP-centric rules to unified identity fabric where enforcement adapts dynamically.  

“The shift from network-centric to identity-first segmentation is a response to the evolution of traditional perimeters to hybrid architectures, the rise of dynamic, cloud-native environments, and the adoption of NHI … Relying on static, IP-based microsegmentation guarantees catastrophic vulnerability to AI-driven attacks.” 

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

A deterministic policy engine that leverages learned network baselines to create accurate rules can also be used to automatically adapt policies as the network changes, ensuring access is always tailored to identity.  

Close Identity Least Privilege Gaps with Zero Networks  

Most organizations struggle to answer a simple question: how many service accounts and AI agents are running in their environment right now, and what can each one actually reach? That blind spot, not attacker sophistication, turns a single overprivileged non-human identity into a business-disrupting breach.  

Zero Networks closes the least privilege gaps that have traditionally left machine identities over-permissioned and under-monitored with automated, identity-driven microsegmentation. Zero makes it easy to discover and control service accounts, AI agents, and every other identity:  

• After deploying in a click, Zero delivers comprehensive visibility into every network asset and identity on the network before tracking all logon activities, account behaviors, and asset access patterns during a learning period.  
• A deterministic automation engine maps observed network behavior, generates least-privilege policies based on learned baselines, and keeps teams in control through human-on-the-loop simulation and staged rollout before enforcement. 
• Identity-based policies govern access at the network layer, tied to user, machine, or AI identity, and automatically updated as environments change.  

Learn how you can secure service accounts and AI agents without slowing the pace of innovation in your organization – request a demo.  

While most businesses know what their critical assets are, far fewer have a clear understanding of their true cyber risk exposure – or what to do about it. This is especially true for cyber pros watching AI proliferate in the wild and within their networks; most organizations are still working to understand external and internal AI threat exposure, let alone forge a path to AI control. We’ll lay out a framework for analyzing attack paths as part of a business impact analysis, enabling security leaders to build a cyber resilience strategy that directly maps to business outcomes.  

Key Answers 

• How does attack path analysis work? An attack path analysis traces the route an attacker could take from an initial point of compromise to a critical asset and measures the controls that stand in the way to uncover the true scope of exposure. This can be completed as a phased process (involving discovery, modeling, and pathfinding), or automated using free resources like Zero Networks’ Breach Map tool.  
• What is the role of an attack path analysis in a business impact analysis (BIA)? A BIA identifies which assets are critical and what downtime would cost; attack path analysis clarifies how exposed those critical assets are and identifies priority interventions for security leaders targeting cyber resilience objectives.   
• How should enterprises prioritize business resilience investments? By identifying the attack paths with the highest business impact relative to containment readiness, then implementing controls that increase path distance or eliminate entire attack scenarios (like microsegmentation), minimize privilege exposure (just-in-time authentication), and constrain potential damage to key resources (identity-based access controls). 

What is Attack Path Analysis?  

An attack path analysis identifies the route an attacker could take from an initial point of compromise to a critical asset, translating unstructured risk exposure insights into a view of real, exploitable paths.  

In the context of a business impact analysis (BIA), attack path analysis shouldn’t only identify exploitable attack paths to critical assets specifically but should also uncover the interventions that will most meaningfully improve cyber and operational resilience. 

How to Use Attack Path Analysis in a BIA 

A business impact analysis starts with identifying which systems and assets are critical to the strategic success and day-to-day operations of the company. Documenting critical assets – and establishing a baseline estimate of what downtime would cost – is the first step in tailoring cyber resilience strategies to business priorities. An attack path analysis answers the logical follow-up question: how exposed are critical assets?  

To uncover the scope of business exposure, attack path analysis maps open pathways and evaluates the level of effort it would take an attacker to move from a common ingress point to a critical asset, giving organizations a documented map of true exposure tied to likely attack tactics rather than a general sense of risk.  

A list of critical assets tells you what’s important, not where to act first. A cyber resilience focused attack path analysis turns that list into a prioritized map of where risk exposure and business impact intersect most sharply. 

Attack Path vs. Attack Vector vs. Attack Surface  

Security teams will deal with three related yet distinct terms when mapping and evaluating attack paths as part of a BIA:  

• Attack surface is the sum of all points in your environment where an attacker could potentially gain entry or exploit a vulnerability – every exposed port, every identity, every endpoint, every cloud workload, and every third-party integration. The larger and more complex your environment, the broader your attack surface.  
• An attack vector is the specific method an attacker uses to exploit a point in the attack surface and gain an initial foothold. For example, compromised credentials or vulnerability exploitation are common attack vectors.  
• An attack path is the route an attacker travels along through the environment after gaining initial access to reach critical assets – the amount of damage they could do along the way makes up an organization’s blast radius.  

In other words, attack surface describes what can be breached, an attack vector is how breaches can occur, and attack paths illustrate where adversaries can go after gaining initial access to eventually reach critical systems.  

4 Attack Vectors to Map for Every Critical Asset  

Starting with the most business-critical assets, security teams should analyze attack paths using scenarios that reflect today's threat landscape: 

• Compromised user: A standard user account compromised through phishing, credential theft, or malware.  
• Compromised cloud identity or AI agent: Stolen credentials or session tokens for a SaaS, IaaS, or agentic identity, often exploited to pivot into on-premises resources through federation, single sign-on trust relationships, or an AI agent’s standing access. 
• Technical perimeter entry: Exploitation of an internet-facing device or service, such as a VPN concentrator, edge firewall, or public-facing web application, to gain code execution and a foothold for further lateral movement. 
• Trusted vendor/third-party access: A compromised or malicious third party using pre-existing privileged access, like a vendor VPN connection or API integration, to reach internal systems. 

For each critical asset, trace the shortest plausible path per vector.  

How to Map Attack Paths: Discovery, Modeling, and Pathfinding  

With critical assets and relevant attack vectors identified, security teams can begin mapping the attack paths they’ll use to analyze exposure – when done manually, this typically happens in three stages:   

1. Discovery: Inventory Systems, Identities, and Connections 

Uncovering attack paths starts with an accurate view of all network assets, identities, connections – including service accounts, AI agents, and other traditionally under-monitored parts of the network – to effectively track how they might contribute to an exploitable pathway between a given entry point and a critical asset.  

2. Modeling: Uncover Communication Pathways  

Convert discovered inventory into a graphical view of what’s reachable across on-prem, cloud, IoT/OT, and Kubernetes: which systems and identities can access which assets, through what trust relationships, and whether or not that access shows up in routine traffic. 

3. Pathfinding: Chain Exploitable Steps Together  

Starting from a pre-defined entry point, trace the routes an attacker could exploit, including paths gated behind authentication or JIT approval, that lead to a critical asset. At enterprise scale, this usually requires graph traversal algorithms to enumerate every path reachable from an entry point to a given asset.  

Automated Attack Path Mapping  

For enterprises that need an accurate view of exposure without months of manual background work, free tools like Zero Networks’ Breach Map automate all three stages. Breach Map scans the internal network to discover reachable assets, then generates an interactive visual map showing how assets connect, where attackers would move, and what they could reach. 

The report surfaces total assets discovered, how many are reachable via lateral movement, average blast radius, attack surface, and direct and indirect crown jewel risk – without weeks of manual discovery and log analysis. It also acts as a personalized benchmark tool for cyber leaders and teams as they work to lock down key attack paths. 

Analyzing Attack Paths: How to Measure Cyber Risk Exposure  

After mapping attack paths from entry point to critical asset, security teams can analyze paths against three threat containment metrics that signal the true scope of exposure.  

Path Distance: How Attackers Move Laterally Through the Network 

Path distance is the most consequential dimension to measure, because interventions here can remove risk rather than just raising its cost. For example, microsegmentation can close lateral movement pathways and effectively eliminate entire compromise scenarios. For the purposes of measuring containment, focus on assessing four things:   

• Authentication boundaries crossed: Distinct credential challenges between entry point and asset, excluding any boundary satisfied by credentials the attacker already holds. 
• Network segments traversed: Segments governed by actively enforced, granular policy – not segmentation that exists on paper but permits broad access in practice due to accumulated exceptions and policy drift. 
• Enforced inspection points in path: Tools actively operating in prevention mode; detection-only controls don't count, since they depend on a human responding in time. 
• Cross-domain traversal: Genuine trust boundary crossings, like on-prem to cloud or IT to OT, that demand a distinct credential set rather than one satisfied by shared federation. 

Privilege Requirements: What Permissions Attackers Need to Access Critical Assets  

Reaching an asset isn't the same as accessing it. Privilege requirements measure how difficult it is for an attacker to obtain the privileges needed to actually impact a critical asset. As a measure of containment, this dimension carries less weight than path distance because privilege controls typically raise the cost of an attack (rather than removing the route entirely via structural controls), but the two are deeply interconnected. Privilege requirements should be captured through details like:  

• Escalation levels required: Each distinct privilege escalation is a separate point of potential failure for attackers, though shortcuts like cached credentials or over-provisioned service accounts can collapse multiple levels into one. 
• Persistent privileged access: Standing access without just-in-time reauthentication means a single compromised credential can be a ticket to critical assets on its own. 
• Service account density: Broadly scoped service accounts are frequent escalation targets and are often excluded from user activity monitoring. 

Additional authentication requirements: Hardware tokens or out-of-band approval that the entry vector can't reasonably satisfy caps the blast radius outright. 

Data-Layer Controls: Limiting Damage After a Breach 

The final containment dimension to assess along identified attack paths answers the only remaining question: if an attacker reaches the critical asset and manages to gain access, how much damage can they do? This can be measured through controls such as:  

• Encryption at rest: With separated key management, a successful compromise can corrupt or delete data but not read or exfiltrate it. 
• Identity-based access controls: Granular access controls limit what any single compromised identity can reach even after accessing a resource, enforcing a final layer of protection for the most sensitive data.  

How to Identify Top Business Resilience Priorities and Investments  

After analyzing attack paths to critical assets for current containment controls, security leaders should have three things:  

1. An inventory of business-critical assets and an understanding of downtime thresholds  
2. A view of all pathways from common ingress points to those critical resources  
3. A breakdown of how exposed the exploitable paths truly are, measured against containment controls  

With these insights, the final analysis can be completed – the goal is to find where business impact and containment readiness converge most sharply. For example, if a billing system has an extremely low downtime threshold due to its high revenue impact but is directly accessible within two lateral movement pivots via compromised user credentials, then it should rank as a high priority.  

With priorities defined, security leaders have a blueprint for tying cyber resilience directly to business impact. Resilience strategies should be implemented using same threat containment dimensions that help uncover risk exposure and define urgency:  

• Increase path distance or completely remove pathways to critical assets: Granular network segmentation, additional authentication boundaries, and inspection points can eliminate entire compromise scenarios. 
• Reduce privilege exposure: Eliminating persistent privileged access, reducing service account scope, and enforcing just-in-time reauthentication or additional authentication requirements for critical assets can rein in the privilege sprawl attackers rely on.  
• Enforce data layer controls: Implementing granular identity-based access controls and encryption at rest limits the damage an attacker can do if they manage to reach critical systems. 

As security leaders take a more active role in business continuity, this framework delivers a repeatable way to align cyber resilience strategies with board-level priorities.  

Cyber Resilient by Design: Protect Uptime with Zero Networks' Automated, Identity-Driven Microsegmentation  

Zero Networks proactively blocks threats to protect operational continuity with automated, identity-based microsegmentation, delivering the containment layer environments need to close attack paths without impacting legitimate traffic.  

Zero provides immediate visibility into every identity and asset on the network, then automatically enforces adaptive, identity-aligned policies based on observed network behavior. With always-current network visibility and a deterministic, human-on-the-loop automation engine, Zero Networks delivers preemptive cyber resilience with no manual effort or operational complexity. 

Learn how you can build a closed-by-default architecture that measurably improves business resilience – request a demo.