TL;DR: AI agents are increasingly authenticating into services, chaining actions, and operating with real authority, which turns them into machine-speed identities that existing IAM models often cannot inventory or govern effectively, according to Token Security. The control problem is no longer agent usefulness but access scope, autonomy, and ownership discipline.
At a glance
What this is: This is an analysis of how enterprise AI agents are starting to behave like identities, with access, autonomy and ownership becoming the main governance problem.
Why it matters: It matters because IAM, IGA and PAM teams need to decide whether AI agents are governed as users, services or something new before access sprawl becomes unmanageable.
Context
AI agent identity risk is the problem this article puts at the centre of the discussion. The core issue is not AI capability in the abstract, but the fact that agents are now authenticating into services, inheriting permissions and taking actions that look operationally similar to human or service identities without being governed that way.
That creates a gap for identity programmes built around stable users and well-bounded workloads. When agents can be created quickly, inherit access implicitly and act with varying levels of autonomy, inventory, ownership and review processes start to lag behind actual use.
Token Security frames this as an emerging identity category rather than a tooling problem alone. The practical question for practitioners is whether current IAM, IGA and PAM controls can describe who or what an agent is, what it may do, and who is accountable when it changes scope.
Key questions
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius. Human-centric controls can misclassify normal agent activity as compromise, or miss policy violations that happen entirely within legitimate access. The failure is not only technical, it is governance design that assumes a person is always behind the action.
Q: Why do AI permissions create more risk when they inherit access from other systems?
A: Inherited access is risky because the AI layer often borrows trust without inheriting the original governance discipline. That means the organisation may not know why access exists, whether it is still needed, or what sensitive data it can reach, which makes least privilege hard to prove and harder to maintain.
Q: How can teams tell whether AI access is actually under control?
A: Look for evidence that access is limited by purpose, not just by account. If you can show which data the system can reach, which actions it can trigger, and how policy changes when the use case changes, you have real governance. If you only have sign-off at deployment time, control is still mostly theoretical.
Q: Should organisations prioritise access scope or autonomy when governing AI agents?
A: Access scope should come first because it is the control surface teams can define and audit today. Autonomy matters, but reducing it can also reduce the business value the agent was introduced to deliver. The better sequencing is to constrain access tightly, then allow only the level of autonomy that the use case genuinely needs.
Technical breakdown
Access and autonomy form the real risk model
The article argues that AI agent risk is best understood through two independent variables: access and autonomy. Access defines what an agent can touch, including services, data and APIs. Autonomy defines how independently it can choose actions, chain tasks and execute without human intervention. Those dimensions are not the same thing. A low-autonomy agent with broad access can still create serious exposure, while a highly autonomous agent with narrow access is more constrained. Risk rises sharply when both increase together, because errors, prompts and delegated actions can cascade faster than normal review cycles can keep up.
Practical implication: evaluate AI agents on both privilege scope and runtime independence, not just on the fact that they use AI.
Why inherited permissions create hidden access sprawl
Local agents and user-triggered tools often inherit the permissions of the human account behind them, which makes their effective access much broader than an inventory page may suggest. That is a governance problem because the agent is then operating inside a user identity boundary without being subject to the same review discipline as a normal employee workflow. The article’s point is that this creates invisible access sprawl: the permissions are not always explicitly granted to the agent, but they are still real, active and actionable. In practice, that undermines least privilege because the true subject using the privilege is not visible in the access record.
Practical implication: inventory inherited permissions separately from explicitly assigned agent credentials and review them as distinct access paths.
Production agents behave like non-human identities with delegated authority
Production agents are described as cloud or backend systems that run with dedicated service identities and credentials, often in event-driven workflows. That places them firmly in NHI territory, even when the business user thinks of them as an AI feature rather than a service account. The technical issue is delegation: once external inputs, chained actions and machine-speed execution combine, the agent can act far beyond a static request-response model. Traditional controls assume a more predictable actor. These systems do not just consume access; they can propagate it through automated steps in ways that look routine until something fails.
Practical implication: govern production AI agents as service identities with explicit delegation boundaries, not as informal application features.
Threat narrative
Attacker objective: The attacker objective is to use the agent’s legitimate access and delegated authority to reach systems, data or workflows that normal users should not be able to affect at machine speed.
- Entry occurs when an AI agent is provisioned with access through API keys, OAuth tokens, inherited user permissions or a dedicated service identity.
- Escalation happens when the agent chains actions, follows external inputs and accumulates effective authority beyond what operators informally expected.
- Impact follows when broad access and autonomy combine, allowing errors or abuse to propagate across services, data sets and workflows faster than human review can intervene.
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.
- AI agent retail card theft campaign 2026: AI agents breached 27+ retailers for about $25 each, used cloud keys and a Secrets Manager dump, and stole 600,000+ payment cards.
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 agent identity is no longer a future category, it is an operational one. The article shows that agents are already authenticating into systems, inheriting permissions and taking actions with business impact. That means IAM programmes are not dealing with a hypothetical class of tooling but with a live identity boundary that must be governed as such. The practitioner conclusion is straightforward: if the actor can act independently and hold access, it belongs in the identity model.
Access review processes were designed for stable identities, and that assumption is starting to fail. Human and service identities are usually reviewed on a cadence that assumes the subject, the permissions and the owner remain identifiable long enough to certify. AI agents can be created quickly, change scope quickly and operate with unclear ownership, which makes that assumption brittle. The implication is not just more review, but a rethink of what review can even prove when access is fluid.
Invisible access sprawl is the named concept practitioners should watch. The article captures a pattern where access is inherited, decentralised and poorly inventoried, so security teams lose track of who or what is actually acting with authority. That is especially dangerous when local agents blend into user activity and production agents blend into backend automation. The practitioner conclusion is that visibility must shift from account-centric reporting to actor-centric governance.
Production agents expose a delegation problem, not just a permissions problem. When an agent runs with a dedicated identity and can act on external input, it becomes a delegated operator with its own failure modes. That means the relevant governance question is not only what the agent can access, but who approved that delegation and how far the delegated chain extends. The practitioner conclusion is that ownership, delegation and revocation need to be treated as first-class controls.
Least privilege remains valid, but it has to be applied to a more dynamic subject. The article is right to emphasise access over autonomy because access is the more controllable dimension. The harder question is how to scope a subject whose role, prompts and tool use can shift with every task. That does not break the principle, but it does make static privilege assumptions much less reliable. The practitioner conclusion is to scope the agent as the actor it becomes at runtime, not the helper it was marketed as.
From our research library:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
- Read next: AI Agent Identity Security Buyer's Guide
What this signals
Invisible access sprawl: AI agents that inherit user permissions or receive ad hoc access through decentralised workflows are already outpacing the inventory discipline most identity teams rely on. That shifts the control question from authentication alone to actor visibility, delegated authority and continuous ownership.
Access reviews cannot stay user-shaped if the subject can be created quickly, act at machine speed and change scope between review cycles. Identity programmes that still assume a stable human or service subject will miss the point where an agent’s effective privilege diverges from its recorded privilege.
For practitioners
- Define AI agents as governed identities Add AI agents to your identity inventory with an owner, a purpose, an access scope and a review path, rather than letting them remain implicit tools inside other accounts.
- Separate inherited access from assigned access Track permissions inherited from the human user separately from permissions granted directly to the agent, especially for local agents that operate inside employee environments.
- Bound production agent delegation Document the service identity, credential source and external inputs that allow a production agent to act, then limit the chain of authority to the minimum required for the workflow.
- Review agent access on task boundaries Move beyond periodic recertification alone and check whether the agent’s access still matches its current task, data scope and autonomy level after each material change.
Key takeaways
- AI agents are becoming governed identities in their own right, not just new interfaces sitting on top of existing accounts.
- The main risk is the combination of access and autonomy, which makes it harder for IAM and IGA teams to see and control what an agent can do.
- Identity programmes need explicit ownership, scoped delegation and actor-level visibility if they want to keep AI agent access from becoming invisible sprawl.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 — Vulnerable Third-Party NHI | AI agents operating through inherited or delegated access create third-party-like identity risk. |
| NHI-05 — Overprivileged NHI | The article centres on agents being granted broader access than their task needs. | |
| NHI-10 — Human Use of NHI | Human users are creating and operating agents that behave like identities without proper governance. | |
| Recommendation — Inventory delegated agent identities and revoke any access that is not explicitly owned and reviewed. Scope AI agent permissions to the minimum task boundary and remove excess privilege by default. Prevent users from creating unmanaged agent identities outside approved governance and approval paths. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article focuses on agents inheriting and exercising privilege beyond intended oversight. |
| Recommendation — Map agent privilege inheritance and block any scope expansion that is not explicitly authorised. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The core governance issue is whether AI agent entitlements are defined and controlled. |
| Recommendation — Apply PR.AA-05 to define, review and enforce every AI agent entitlement. | ||
| MITRE ATT&CK | TA0006;TA0004 — Credential Access; Privilege Escalation | The article describes agents operating with credentials and growing authority through delegated use. |
| Recommendation — Track agent credential use and investigate any unexpected privilege growth during execution. | ||
Key terms
- AI Agent Identity: The digital identity used by an autonomous AI agent to authenticate to external systems, APIs, and services. Managing AI agent identities is an emerging and rapidly evolving area of NHI security.
- Access and autonomy: A practical way to assess AI agent risk by looking at what the agent can reach and how independently it can act. Access determines blast radius, while autonomy determines how quickly that blast radius can spread across systems without human intervention.
- Delegated Authority Model: A delegated authority model defines who is allowed to approve, review, or execute control-related decisions across the enterprise. It helps ensure requests reach the correct responsible party, especially when control owners, managers, and process owners sit in different teams, regions, or systems.
- Invisible Access Sprawl: Invisible access sprawl is the accumulation of agent permissions that are hard to see, hard to inventory and easy to overextend through inheritance or local configuration. The problem is not just excessive access, but the fact that the control plane cannot reliably describe who or what currently holds the authority to act.
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 responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.
Published by the NHIMG editorial team on May 30, 2026.
Updated on October 6, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org