TL;DR: AI agents are effectively operating as delegated identities across endpoints, SaaS platforms, and the “mushy middle” of productivity tools, with long-lived OAuth and SAML grants creating persistent access paths that security leaders often do not fully see, according to Reveal Security. The identity model is shifting faster than conventional IAM review cycles, because agents can act as the user while retaining permissions long after the original task ends.
At a glance
What this is: NHI Mgmt Group's analysis, drawing on Reveal Security's blog post: AI agents are behaving like delegated identities, with long-lived grants and broad permissions creating access paths that most enterprise IAM controls do not fully track.
Why it matters: IAM, PAM, and NHI teams need to govern agent permissions as active identity states, not one-time approvals, because persistent grants can outlive the task and the operator’s awareness.
Context
AI agent identity risk emerges when an autonomous process acts with a user’s permissions across multiple tools and services. In practice, that means the agent is not just an application feature. It becomes an identity-bearing executor that can inherit access, retain tokens, and operate after the original task is complete.
The governance gap is not only technical. Conventional IAM reviews, consent screens, and deprovisioning workflows are built around people or static service accounts. AI agents compress the decision cycle, create broader delegated access, and move activity into the productivity tools where oversight is weakest.
That makes the central question one of identity scope and lifecycle, not model quality. If an agent can act as the user inside SaaS, endpoint, and browser workflows, enterprise control has to follow the delegation chain rather than the application boundary.
Key questions
Q: What breaks when AI agents are given broad standing access?
A: Broad standing access breaks governance because the agent can move from one task to another without a fresh authorization check. That creates a control gap between intended scope and actual runtime behaviour. The result is weak accountability, limited containment, and audit trails that show activity without explaining why the activity was allowed.
Q: Why do long-lived user tokens create governance risk for AI agents?
A: Long-lived tokens assume access remains valid until a person revokes it, but agent behaviour changes the risk model because the actor can decide and act at runtime. That makes persistent delegation harder to justify, especially when the task is narrow and short-lived. Session-bound authorization reduces the exposure window and fits the way agents actually execute work.
Q: How can security teams tell whether agent permissions are too broad?
A: The clearest signal is whether the agent can still complete its job after permissions are reduced in a sandbox. If the task keeps working after you remove broad access, the original entitlement was inflated. A second signal is the presence of unused permissions that persist across reviews and deployments.
Q: Who should be accountable for AI agent approvals and audits?
A: Accountability should sit with the human owner of the agent path, the application owner, and the identity governance process together. The agent cannot be the sole accountable subject because it is not a governance endpoint. Teams should tie approvals, logs, and access reviews to the person or team responsible for the agent’s use.
Technical breakdown
How AI agents inherit identity and permissions
An AI agent is an autonomous process that can take action on a user’s behalf, which means it often operates under delegated identity rather than a distinct machine account. In the article’s model, OAuth, SAML, login federation, and platform permissions become the mechanisms that bind the agent to the user’s access. The important detail is that the agent is not merely reading data. It can execute commands, use tools, and persist tokens across sessions, which makes the identity relationship durable and operational, not temporary or symbolic.
Practical implication: treat every agent permission grant as an identity delegation event that needs explicit scope, owner, and expiry.
Why long-lived tokens create agent identity debt
Long-lived tokens are the structural problem because they outlast the immediate task. Once an agent receives OAuth or similar delegated access, that token can continue to authorize actions long after the operator has moved on or the original need has ended. This creates identity debt: permission that remains valid, useful, and exploitable even when the business reason has changed. The article’s key point is that the enterprise often sees the login event, but not the continuing authority the token preserves.
Practical implication: inventory delegated tokens and revoke access based on business need, not just user account status.
Why the “mushy middle” is hardest to govern
The “mushy middle” is the set of productivity and collaboration tools where agentic features sit between endpoint control and first-party SaaS governance. These tools can spawn agents, move data across third-party services, and blend human and automated actions in ways that are harder to log, classify, and review. That ambiguity matters because it weakens control ownership. Security teams may have partial visibility on the endpoint and stronger controls in core SaaS, but far less clarity when the agent lives inside layered workflow services.
Practical implication: classify the collaboration stack by control ownership and visibility before allowing agents to operate across it.
Threat narrative
Attacker objective: The objective is to use the agent’s delegated identity to expand access to data and actions that were never meant to remain continuously authorized.
- Entry occurs when a user or administrator grants an AI agent OAuth, SAML, or platform permissions to complete a task across SaaS, endpoint, or browser tools.
- Credential access follows when the agent receives long-lived delegated tokens that let it continue acting as the user after the original request ends.
- Escalation occurs when broad permissions or approval-for-everything consent screens expose email, files, chats, calendars, and connected services beyond the intended task scope.
- Impact emerges when stale or overbroad delegated access is used to read, move, or manipulate data at machine speed before controls notice the authority is still active.
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 and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
AI agents turn delegated access into a standing governance problem: once an agent can act as the user, IAM is no longer only authenticating a person. It is governing an executor that can inherit, retain, and reuse authority across sessions. That shifts the control point from login assurance to delegation scope, which is where many programmes are still weakest. The practitioner conclusion is that agent access must be managed as an active identity state, not a one-time approval.
Long-lived consent creates identity debt: OAuth and similar grants do not just authorize a task, they can preserve authority long after the task is done. That is why access review cadences miss the real risk. Reviews assume a human or service account exists long enough to be recertified, but agents can acquire useful privilege faster than the governance cycle can observe. The implication is that lifecycle discipline has to move to issuance and revocation events, not annual review rituals.
The mushy middle is an access-control blind spot, not just a deployment trend: productivity platforms with embedded agents blur the boundary between sanctioned SaaS, local execution, and third-party workflows. That creates a control ownership problem because no single team sees the full delegation chain. The result is not merely more automation. It is a fractured identity surface where visibility, authorization, and accountability drift apart. Practitioners need to treat the collaboration stack as a governance domain in its own right.
AI agent permission screens are becoming the new consent boundary: users are being asked to approve broad access in contexts they do not fully understand, and the agent then operates under that approval at machine speed. This is where human IAM assumptions start to fail, because the decision-maker and the executor are no longer the same actor in practice. The conclusion is that consent UX, scope design, and revocation mechanics now belong in identity governance conversations, not only in product design discussions.
From our research library:
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
- 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.
- Read next: AI Agent Identity Security Buyer's Guide
What this signals
Delegated identity is now the default risk pattern for AI agents: enterprise programmes should assume that agents will inherit human permissions through OAuth, SAML, and embedded platform workflows. That means visibility has to shift from account inventory to consent inventory, because the meaningful control question is which delegated grants still exist and why.
Agent governance needs a lifecycle model, not a feature checklist: access reviews, deprovisioning, and recertification all become less effective when an agent can obtain and use privilege within one session. The practical response is to govern issuance, scope, and revocation as first-class identity events.
Consent design is an identity control, not a UX detail: when users approve broad access without understanding downstream sharing, the organisation has effectively delegated security decisions to interface design. Teams that want safer adoption should treat consent boundaries, permission bundles, and revocation paths as part of the identity architecture.
For practitioners
- Define explicit agent ownership Assign a business owner and technical owner to every AI agent that can act on behalf of a user, service, or team. The owner must approve scope, review usage, and accept responsibility for delegated authority.
- Scope delegated access to the task Limit agent permissions to the minimum tool set, data set, and action set required for the specific workflow. Avoid broad consent screens that bundle email, files, calendars, chats, and other unrelated resources into one grant.
- Track and revoke long-lived tokens Inventory OAuth and SAML grants that outlive the immediate use case, then remove access when the workflow ends or the agent is retired. Token persistence should be treated as an identity lifecycle event.
- Classify the mushy middle Map collaboration and productivity tools that can spawn autonomous workflows, then decide which team owns logging, authorisation, and incident response for each platform. Hidden agent surfaces need explicit governance boundaries.
Key takeaways
- AI agents are not just tools in the workflow. They are delegated identities that can carry user authority across systems and continue acting after the immediate task ends.
- The biggest governance gap is persistence. Long-lived grants and broad consent create access that survives the moment of approval and often outlasts the business need.
- Security teams should move agent governance from review-only processes to explicit ownership, scoped consent, and aggressive token revocation.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centres on agents inheriting and reusing user authority across tools. |
| ASI09 — Human-Agent Trust Exploitation | Users are approving broad access through consent screens they do not fully understand. | |
| Recommendation — Scope agent permissions tightly and monitor identity transitions that expand privilege beyond the intended workflow. Redesign consent flows so users can evaluate what authority an agent actually receives before approval. | ||
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | OAuth, SAML, and login federation are the identity mechanisms that let agents act as the user. |
| NHI-05 — Overprivileged NHI | The article shows AI agents receiving broader permissions than their tasks require. | |
| NHI-07 — Long-Lived Secrets | Long-lived tokens persist after the original use case and keep the agent authorised. | |
| Recommendation — Review delegated authentication paths and remove any grant that lets an agent inherit excessive authority. Enforce least-privilege scope for agent credentials and trim permissions to the minimum workflow boundary. Shorten token lifetimes and revoke stale delegated credentials as soon as the business need ends. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The piece is fundamentally about governing agent entitlements and delegated access. |
| Recommendation — Govern AI agent entitlements as active authorizations and continuously verify that scope still matches purpose. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Token persistence and revocation are core to the article’s risk pattern. |
| Recommendation — Manage agent authenticators with strict lifecycle controls and remove stale credentials when tasks end. | ||
Key terms
- AI Agents: AI agents are autonomous software entities that act within organisational environments and make runtime decisions within assigned boundaries. They can hold identities, authenticate to systems, and exercise permissions, which makes them comparable to other non-human identities that require inventory, governance, and continuous activity monitoring.
- Delegated Identity: Delegated identity is when one actor acts on behalf of another with explicit permission and bounded authority. In AI-assisted commerce, it requires clear consent, limited scope, and traceable records so the retailer can distinguish authorised delegation from unauthorised automation.
- Consent Inventory: Consent inventory is the record of which applications, agents, and platforms have been granted access and under what terms. It matters for AI agents because security teams need to see not only who signed in, but what delegated rights still exist and whether they remain justified.
- Identity Debt: Identity debt is the accumulation of unowned, over-permissioned, or poorly governed non-human identities that security teams cannot cleanly inventory or retire. It usually grows when experimentation outruns access governance, leaving service accounts and tokens active long after their original purpose has passed.
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.
Published by the NHIMG editorial team on July 12, 2026.
Updated on October 13, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org