AI agents are multiplying NHI sprawl and access risk

At a glance

What this is: This analysis argues that AI agents are turning NHI management from single-identity oversight into multi-identity governance, with broader access sprawl, weaker ownership clarity and a larger abuse surface.

Why it matters: IAM and PAM teams need to treat AI agents as access aggregators, because their permissions can expand faster than governance, making inventory, offboarding and monitoring materially harder across NHI and autonomous programmes.

Context

AI agents change the identity problem because they do not behave like fixed scripts or ordinary service processes. They can request access dynamically, chain tools and operate with enough variability that the resulting non-human identity footprint is harder to predict, classify and govern.

That matters for identity programmes because the control model for NHIs has usually assumed a relatively stable owner, a bounded purpose and a small access set. Once an AI agent can assemble multiple credentials across business systems, cloud services and collaboration tools, the governance question shifts from single-account hygiene to collective access accountability.

Key questions

Q: What breaks when AI agents are given access without identity governance?

A: What breaks is accountability. The organisation may see actions, logs, and alerts, but it cannot reliably tie them to a governed identity with clear scope and revocation. That creates uncontrolled blast radius, especially when agents can reach sensitive systems through shared tokens, delegated service accounts, or broad API access.

Q: Why do autonomous AI agents increase insider risk even when access is technically authorized?

A: Autonomous agents increase insider risk because authorization alone does not explain purpose. A headless agent can inherit legitimate access and still move data, trigger workflows, or expose information in ways humans never intended. Risk rises when security teams cannot see the action’s context, intended outcome, and data sensitivity together.

Q: How can security teams tell whether AI-associated NHIs are being abused?

A: Look for behaviour that diverges from the agent's established task pattern, such as unusual tool chains, access to unfamiliar systems or high-frequency calls that do not fit the expected workload. The key is to baseline agent behaviour separately so normal automation does not hide malicious use.

Q: How should organizations manage credentials for AI agents?

A: Organizations should transition from hard-coded credentials to runtime-fetched credentials that enhance security by ensuring that tokens are not stored permanently. Utilizing solutions like the MCP Secret Wrapper can help eliminate the risks associated with static credentials.

Technical breakdown

Why AI agents create multi-NHI access chains

Traditional NHIs usually map to a narrow function such as one workload, one integration or one automation path. AI agents are different because the same agent may need access to email, chat, SaaS applications, databases and cloud APIs to finish a task. That creates a multi-NHI access chain, where several identities collectively represent one operational actor. The security problem is not simply volume. It is that each credential may look acceptable on its own while the combined entitlement set becomes much broader than any one owner would consciously approve. The result is access sprawl that is hard to see from an individual-account review.

Practical implication: Model and review the agent as a composite identity, not as isolated credentials.

How autonomous AI changes least-privilege assumptions

Least privilege is easier to define when the actor's purpose is known in advance and its actions are predictable. AI agents weaken that assumption because they can decide at runtime which tool or system to call next, and that can change the access needed mid-task. In practice, this means the permission boundary is not fixed at provisioning time in the same way it is for a conventional service account. The article's core point is that governance becomes less about granting one bounded role and more about managing a dynamic access footprint that may grow as the task evolves.

Practical implication: Treat runtime access scope as a governance object, not just the initial permission grant.

Why living-off-the-land gets easier through AI-associated NHIs

Living-off-the-land attacks rely on legitimate identities and native tools, not obvious malware. When an attacker compromises an AI-associated NHI, the activity can blend into normal agent traffic because the agent itself is expected to access many systems, often at high frequency. That makes abuse harder to distinguish from routine automation. The real technical risk is not just credential theft. It is that the identity layer becomes camouflage for lateral movement, data collection and persistence, especially when monitoring has no behavioral baseline for what the agent should and should not do.

Practical implication: Baseline agent behaviour separately and alert on deviations from its expected access pattern.

Threat narrative

Attacker objective: The attacker wants to hide behind legitimate AI-agent activity while using inherited permissions to expand access and sustain stealthy operations.

1. Entry begins when an attacker gains control of an AI-associated non-human identity or the credentials behind it, such as an API key, OAuth application or service account.
2. Escalation follows as the compromised identity is used to reach multiple systems that the AI agent is authorised to touch, turning one foothold into a broader access path.
3. Impact occurs when the attacker masks malicious activity as ordinary agent behaviour, moves laterally and extends dwell time long enough to collect data or strengthen persistence.

Breaches seen in the wild

• CoPhish OAuth phishing via Copilot Studio: Datadog showed Copilot Studio agents on a Microsoft domain can front OAuth consent phishing and forward stolen tokens; no victims reported.
• Spain's first AI agent data breach 2026: Spain's AEPD logged its first breach notification attributed to an attacker's AI agent, which altered personal data and accessed invoices.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI agents are turning NHI governance into composite-identity governance. The article shows that a single agent may rely on multiple service accounts, API keys and OAuth applications across different business systems. That means ownership, scope and offboarding can no longer be judged one credential at a time. The practitioner implication is that identity governance must move from asset-level review to actor-level accountability.

Least privilege was designed for bounded, predeclared access, and that assumption fails under autonomous behaviour. When an agent can choose tools and request access dynamically during execution, privilege is no longer fully knowable at provisioning time. That is an assumption collapse, not just a control gap. Practitioners need to recognise that static entitlement models do not fully describe agent behaviour once runtime decisions enter the picture.

Identity review cycles are too slow for agent-paced access accumulation. Traditional recertification assumes a person or service will retain access long enough for an owner to inspect it later. AI agents can assemble and use permissions within a task window that may be shorter than the review interval. The implication is that governance must emphasise issuance, scope and expiry rather than relying on periodic inspection after the fact.

Living-off-the-land risk is amplified when AI traffic becomes normalised background noise. The article correctly points to the operational challenge: malicious use can hide inside expected agent activity, which reduces the signal value of volume-based monitoring. That weakens confidence in manual review and broadens the dwell-time window. Security teams should treat agent behaviour as a distinct telemetry class, not as generic automation.

Ephemeral access does not solve ownership ambiguity unless the lifecycle is explicit. Expiration times and automated decommissioning help, but only when the organisation can answer who created the identity, why it exists and when it should disappear. Without that provenance, short-lived access can still become orphaned access. The practitioner conclusion is that lifecycle discipline matters as much as the credential format itself.

From our research library:

• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
• Read next: Ultimate Guide to NHIs

What this signals

AI-agent sprawl is now a governance design problem, not just an operational scaling problem. As agent counts rise, so does the number of identities that must be owned, scoped, reviewed and retired. That creates pressure to move from per-credential oversight to per-actor governance, especially where one agent can touch business systems, cloud platforms and collaboration tools in the same task.

Access review alone will not keep pace with agent-paced privilege accumulation. Review cycles assume privilege persists long enough to be certified, but autonomous behaviour can compress creation, use and retirement into one execution window. The control question shifts to who approved issuance, what the agent could do at the moment of use and whether expiry was enforced.

53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey. That expectation means identity teams need telemetry, ownership and offboarding models that can survive machine-paced change, not just human review cadences.

For practitioners

• Map each AI agent to a composite identity record Record every service account, API key and OAuth application an agent can use, then link them to one owner, one purpose and one expected expiry date.
• Separate provisioning from approval for sensitive access Require explicit approval for administrative writes, high-risk data access and external sharing before an agent receives credentials that can reach those actions.
• Set hard expiry on agent-linked credentials Use time-bounded permissions and automatic decommissioning so credentials cannot outlive the task or the agent that needed them.
• Build behaviour baselines for agent telemetry Monitor agent logins, API usage and downstream tool calls as their own behavioural class so deviations can be distinguished from expected background activity.
• Review offboarding as a lifecycle control Make sure agent shutdown removes the attached non-human identities, revokes tokens and closes any residual delegated access paths.

Key takeaways

• AI agents do not just add more identities, they change how access is assembled, owned and retired across the enterprise.
• The main governance weakness is composite identity drift, where multiple credentials collectively create more access than any one review can easily see.
• Lifecycle control, behavioural monitoring and explicit ownership are the practical controls that matter most when agents can request access dynamically.

Key terms

• Composite Identity: An identity made up of more than one control relationship, typically a human owner, an AI agent, and the credentials or services the agent uses. It matters because accountability, access scope, and runtime behaviour all have to be governed together, not as separate problems.
• Multi-NHI Access Chain: A multi-NHI access chain is a sequence of linked non-human identities used by the same actor to move between systems and actions. In agentic environments, this chain can span APIs, service accounts and OAuth applications, making ownership and blast radius harder to trace.
• Living-off-the-Land: Living-off-the-land attacks use legitimate enterprise tools instead of custom malware. In identity environments, that means abusing approved administrative functions to perform disruptive actions while blending into normal operational traffic.
• Agent Offboarding: The process of formally retiring an AI agent by revoking credentials, detaching tools, closing access paths, and recording evidence that authority has ended. In NHI programs, offboarding is as important as onboarding because abandoned access is still active risk.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.