Machine Identity Security: How to Protect Service Accounts and AI Agents

Machine and service identities now outnumber human users 109:1 – a trend that’s on track to accelerate as organizations anticipate 85% growth in AI agent adoption over the next year. These non-human identities (NHI) are typically over-privileged and under-monitored, creating a growing network security blind spot that attackers can use to move laterally without triggering alerts. In fact, less than a third of organizations extend granular access controls and zero trust architecture to NHI, according to IBM’s 2026 Cost of a Data Breach Report.  

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: 

Machine Identity Type	What It Is
Service accounts	Non-human accounts running applications, automation, and backend processes
API keys and tokens	Credentials that authenticate machine-to-machine calls
Workload identities	Identities assigned to cloud roles, containers, or serverless functions
AI agents	Autonomous processes that reason, chain tool calls, and act with minimal human oversight

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.