Agentic identities force IAM to extend governance to AI agents

At a glance

What this is: This is an analysis of Gartner’s agentic identities category and the claim that AI agents now need first-class identity governance.

Why it matters: It matters because IAM, IGA, and PAM programmes will need to govern agent actions, runtime credentials, and delegated access alongside human and machine identities.

By the numbers:

• By 2026, 40% of enterprise apps will be integrated with task-specific agents, up from less than 5% now.

👉 Read Strata Identity's analysis of Gartner's agentic identities category

Context

Agentic identities are AI agents that can perceive, reason, and act autonomously inside enterprise systems. The governance problem is that many IAM programmes still assume access is requested by a person, assigned to a stable account, and reviewed on a predictable lifecycle. That assumption no longer holds when the actor can choose actions at runtime.

Strata Identity’s recognition in Gartner’s Emerging Tech Impact Radar is a signal about category formation, not vendor merit. The real issue for practitioners is whether identity orchestration, policy enforcement, and lifecycle control can extend beyond human logins and static service accounts to AI agents that initiate work, call tools, and act within delegated limits.

Key questions

Q: How should security teams govern AI agents that can act independently?

A: Treat AI agents as governed identities with accountable ownership, scoped permissions, and per-action policy enforcement. The important shift is from managing a static account to managing runtime behaviour. If the agent can choose actions, it needs controls that evaluate intent, context, and effect before sensitive work proceeds.

Q: Why do AI agents complicate least privilege for IAM teams?

A: Least privilege becomes harder because an agent’s intent is not fully known at provisioning time and may change during execution. Traditional role design assumes stable duties, but agentic systems can combine tools and sequence actions dynamically. IAM teams need to define permission boundaries around tasks, not just around roles.

Q: What breaks when access reviews are applied to agentic identities?

A: Access reviews often miss agentic risk because they look for persistent entitlements rather than short-lived, behaviour-driven authority. An agent may obtain and use privilege inside a single workflow, leaving little to certify later. That means review cycles alone are not enough to prove governance, especially for high-impact actions.

Q: Who should own AI agent identity governance in an enterprise?

A: Ownership should sit across identity, security, and the business team using the agent, with one named accountable owner for each system. That model is necessary because agent behaviour affects access, data handling, and operational outcomes at the same time. Shared ownership prevents agents from becoming unmanaged shadow identities.

Technical breakdown

What makes an agentic identity different from a normal service account?

An agentic identity is not just a token attached to automation. It is a governed digital identity assigned to an AI system that can perceive context, select actions, and execute tasks toward a goal. That creates a different control problem from traditional workload identity because the actor can shift intent mid-session, combine tools, and generate new action paths that were not fully known at provisioning time. The result is an identity that behaves more like a runtime decision-maker than a fixed integration account.

Practical implication: teams need runtime policy enforcement for agent actions, not only provisioning-time permissions.

Why do short-lived credentials matter for AI agent governance?

Short-lived, scoped credentials reduce the exposure window, but they do not solve the underlying question of who or what is authorised to make the next decision. In agentic systems, the risk is often not persistence alone, but delegated capability being exercised too broadly inside a single task sequence. That is why the control stack needs both credential limits and decision limits, especially when agents can trigger tools, APIs, or downstream workflows without a human pause between steps.

Practical implication: constrain both token lifetime and the actions a token can authorise in real time.

How does zero trust change when the actor is an AI agent?

Zero trust for agents is not just about authenticating the system once and then trusting the session. The policy must continually evaluate the identity, the context, and the action being attempted, because agent behaviour can change faster than lifecycle reviews can capture. In practice, that means authentication, authorisation, and auditing have to operate at the level of each sensitive action, especially where agents can interact with high-value systems or privileged APIs.

Practical implication: move from static trust decisions to per-action policy checks and auditable execution records.

NHI Mgmt Group analysis

Agentic identities are a new governed class, not just another workload. The article points to a structural change in identity architecture: AI agents are being treated as unique, verifiable, and manageable identities rather than hidden automation. That shifts the governance burden from infrastructure teams alone to IAM, PAM, and IGA functions that must understand how runtime delegation works. The practitioner conclusion is straightforward: if an agent can act independently, it must be governed as an identity class in its own right.

Traditional IAM lifecycle assumptions break when the actor chooses actions at runtime. Access review processes were designed for stable entitlements that persist long enough to be observed, certified, and revoked on schedule. That assumption fails when an agent can acquire, use, and discard capability within a task sequence. The implication is not simply better tooling, but a rethinking of lifecycle governance for actors whose privileges are both temporary and behaviour-driven.

Runtime policy enforcement is the named control gap for agentic systems. The article’s most useful concept is agentic identity policy enforcement at the moment of action, not after the fact. Identity orchestration, short-lived credentials, and human-in-the-loop approval are all part of that model, but the deeper point is that enterprise controls must evaluate context at execution time. Practitioners should treat this as an identity blast-radius problem, not a generic AI oversight issue.

Agentic governance will converge IAM, PAM, and AI risk management. The boundaries between identity administration, privileged access, and AI oversight are starting to blur because agent actions can create real operational effects. That means the governance model has to connect who the agent is, what it can do, and when it may do it. The practitioner takeaway is that agentic identity programmes will need shared ownership across identity, security, and AI governance teams.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• For the broader control context, see OWASP NHI Top 10 for the agentic risks that emerge when runtime decisions meet privileged access.

What this signals

Agentic identity governance is moving from theory to operational necessity. With 96% of technology professionals identifying AI agents as a growing security threat and 66% calling the risk immediate, programmes that wait for a mature market standard will already be behind the curve. The practical signal is that agent inventories, ownership, and runtime authorisation need to become part of the core identity operating model now.

Identity blast radius becomes the useful planning concept for agentic systems. Once agents can take actions that affect systems, data, and downstream workflows, the real question is how far a compromised or mis-scoped agent can move before a control intervenes. That shifts programme design toward tighter scopes, stronger auditability, and better separation of duties across agent use cases.

Teams should expect AI governance, IAM, and PAM controls to converge around agent sessions rather than static accounts. The organisations that succeed will be the ones that can prove what an agent was allowed to do, what it actually did, and who was accountable when those two things diverged.

For practitioners

• Classify AI agents as governed identities Inventory every agent that can call tools, access data, or trigger downstream workflows and assign an accountable owner, lifecycle state, and approval path for sensitive actions.
• Enforce action-level policy checks Require runtime authorisation for high-risk agent actions so the system evaluates context before each sensitive tool call, not just when the agent is provisioned.
• Limit delegated capability to the task scope Issue short-lived credentials with tightly scoped permissions and remove any standing access that would allow an agent to expand beyond the immediate task boundary.
• Build audit trails for agent decisions Capture the identity, prompt context, tool used, and outcome for each agent action so security and compliance teams can reconstruct behaviour after an incident or review.

Key takeaways

• AI agents are becoming a distinct identity class that requires runtime governance, not just onboarding controls.
• The evidence shows that most organisations already see agent behaviour exceed intended scope, which makes the problem current rather than hypothetical.
• Practitioners should redesign identity controls around per-action authorisation, task-scoped access, and auditable agent decisions.

Key terms

• Agentic Identity: A digital identity assigned to an AI system that can perceive context, choose actions, and execute work toward a goal. Unlike a static service account, it needs governance for runtime behaviour, delegated authority, and auditable actions because the risk comes from what the agent decides to do, not only from what it can technically access.
• Runtime Authorisation: A control pattern that checks whether a specific action should be allowed at the moment it is attempted. For agentic systems, this matters because privilege cannot be judged only at login or provisioning time. The authorisation decision must reflect current context, task scope, and the sensitivity of the action being requested.
• Identity Orchestration: The coordination layer that connects identity sources, policy engines, and applications so access decisions are applied consistently. In agentic environments, orchestration has to manage human, workload, and AI identities together, while preserving auditability and enforcing limits across changing execution paths.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Strata Identity: Strata Identity recognized by Gartner as a sample vendor for agentic identities. Read the original.