TL;DR: Gartner says AI agent identity is now one of its top strategic technology trends, alongside a forecast that 25% of enterprise security breaches will be linked to AI agent abuse by 2028 and 50% of organisations will run agents with minimal human involvement by 2030, according to Aizome. Identity controls built for human logins do not govern autonomous execution, intent drift, or attributable tool use.
At a glance
What this is: NHI Mgmt Group's analysis, drawing on Aizome's blog post: AI agent identity has moved from a niche control question to a board-level governance issue, with Gartner linking it to breach risk, operational risk and cost exposure.
Why it matters: Practitioners need to treat AI agents as governed identities because human-centric IAM, PAM and IGA controls do not explain or constrain autonomous tool use, privilege drift or accountability.
By the numbers:
- 25% of enterprise security breaches will be linked to AI agent abuse by 2028, according to Gartner research cited by Aizome.
- 50% of organisations will be running AI agents with minimal human involvement in IT operations by 2030, according to Gartner research cited by Aizome.
Context
AI agent identity is the governance problem that appears when a software actor can initiate work, call tools and continue operating without a human at every step. Existing identity models assume a person logs in, completes a task and ends the session, but that assumption breaks when the actor is autonomous and can keep acting beyond the moment of provisioning.
The practical gap is not just authentication. It is the absence of a control model for intent, delegated authority, runtime scope and lifecycle ownership. Agentic AI identity is the right lens here because the issue is who or what is allowed to act, for how long, and under whose accountability.
Once AI agents start touching business systems, the question changes from whether they can access a tool to whether they should still have that access after their purpose, owner or context changes. That is a governance problem, not a feature problem, and it sits across IAM, PAM and IGA rather than inside any single stack.
Key questions
Q: What should teams do first when AI systems start operating on existing access paths?
A: Start by removing the access paths that require the least governance and create the highest blast radius. That means revoking stale credentials, eliminating bypass logins where possible, and isolating service identities that exceed their intended scope. The goal is to reduce the number of shortcuts before machine speed turns them into incidents.
Q: Why do existing IAM controls struggle with autonomous AI agents?
A: Existing IAM controls were designed around human users and predictable workload behaviour. Autonomous agents can make repeated tool calls, chain permissions, and keep acting after the original task context changes. That creates lifecycle, privilege, and accountability gaps that traditional role models do not close on their own.
Q: How do organisations know if an AI system has drifted beyond its mandate?
A: Organisations know an AI system has drifted when its behaviour remains technically permitted but no longer matches the intended purpose, scope, or business outcome. The most useful signals are expanded tool use, new workflow paths, and actions that still pass policy checks while exceeding the original mandate.
Q: What is the difference between access control and intent governance for AI agents?
A: Access control answers whether an identity is allowed to do something. Intent governance asks whether the action makes sense in the current context and whether the input driving that action is trustworthy. For AI agents, both are necessary because legitimate permissions can still be abused through malicious content.
Technical breakdown
Why human IAM assumptions break for autonomous agents
Human identity systems are built around stable operators, visible login events and session boundaries that end when a person stops working. AI agents invert that model. They can inherit credentials, act across tools, and continue executing without a human present for each action. That makes role assignment, session tracking and offboarding materially weaker as governance signals, because the system no longer has a reliable human rhythm to anchor control decisions. Read AI Agents vs Agentic AI.
Practical implication: Model agent identity as a runtime actor with explicit ownership and expiry, not as a human account with a different interface.
What intent governance adds beyond access governance
Access governance answers what an identity is permitted to do at provision time. Intent governance asks whether its live behaviour still matches the purpose it was created for. For AI agents, that distinction matters because an agent can remain authorised while drifting into new data sets, tools or workflows that were never part of the original business need. The governance problem is not only excess privilege; it is the lack of a live reference point for acceptable action. See the AI Agent Authorisation Guide.
Practical implication: Define the agent's declared purpose and compare live actions against it as part of the control model.
Why auditability becomes a security control for agent identity
When an AI agent acts through multiple tool calls, attribution has to follow the identity, not just the platform. A useful audit trail needs to connect the agent, the owner, the permissions in force, the tools invoked and the outcome produced. Without that chain, incident response cannot separate normal automation from abuse, and finance teams cannot distinguish approved consumption from runaway usage. Identity and telemetry therefore become linked controls rather than separate disciplines. See the AI Agent Observability, Audit and Incident Response Guide.
Practical implication: Capture per-agent action logs that tie every tool call back to a governed identity and owner.
Threat narrative
Attacker objective: The objective is not necessarily to compromise the model itself, but to exploit governed access paths through an agent so work, data access or spend can be extended beyond intended limits.
- Entry occurs when an AI agent is provisioned with inherited access from the human who built it, rather than a purpose-scoped identity of its own.
- Escalation happens when the agent keeps using that access beyond the original task, crossing into systems, data sets or tools outside its intended mandate.
- Impact follows when the unbounded agent activity creates breach exposure, cost overruns or operational risk that no existing human-centric control chain can attribute quickly.
Breaches seen in the wild
- postmark-mcp malicious MCP server 2025: A fake Postmark MCP server on npm quietly BCC'd every email AI agents sent through it to an attacker, using victims' own Postmark tokens.
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 agent identity is now a governance primitive, not an application feature. The reason is simple: once software can independently initiate action, the old split between authentication, authorisation and workflow execution becomes too thin to carry accountability. Security leaders need to treat the agent itself as the governed subject, because the board-level risk is not the model prompt but the actor that keeps working after the human has moved on.
The broken assumption is that privilege can be safely reviewed after issuance. That assumption was designed for access that persists long enough to be seen in a review cycle. It fails when an autonomous actor can receive, use and outlive that access within a workflow window that never maps cleanly to human recertification. The implication is that governance must stop assuming stable access states as the default condition.
Intent drift is the named concept this market now has to govern. AI agents can remain formally authorised while their live behaviour moves away from the purpose that justified their access in the first place. That is not the same as privilege creep, because the permissions may not change at all. Practitioners need a control model that treats purpose drift as an identity event, not just a behavioural anomaly.
Audit trails without identity attribution will fail the next incident review. Gartner's warnings about breach exposure, operational convergence and cost overruns all point to the same structural gap: organisations need to know which agent acted, under whose authority and with which permissions. Without that chain, response teams will be left reconstructing events after the fact instead of proving control in real time.
AI agents are forcing IAM, PAM and IGA to converge around runtime authority. Human review cycles, privileged vaulting and access certification were built as separate disciplines because human behaviour was separable from machine action. That separation no longer holds when a software actor can make decisions, call tools and continue executing across systems. The implication is a shared governance model for all non-human and autonomous actors.
From our research library:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, 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 Authorisation Guide
What this signals
Intent drift: AI governance for agents will increasingly move from provisioning-time approval to runtime supervision, because a permission that looks valid on paper can still become unsafe when the agent's work expands beyond its original task. That shift changes the control objective from static entitlement review to continuous attribution and purpose checking.
The practical consequence for IAM, PAM and IGA teams is that recertification alone will not answer the agent problem. Organisations will need to know which agent is active, what it is doing right now, and whether that behaviour still matches the approval basis that justified its access in the first place.
For practitioners
- Establish governed identities for every AI agent Assign each agent an explicit owner, declared purpose and expiry condition so access can be tied to accountability from day one.
- Scope agent permissions to task boundaries Remove inherited human access wherever possible and issue only the minimum tool and system access needed for the agent's current job.
- Monitor live behaviour against declared intent Compare actual tool use, data reach and action patterns against the agent's approved purpose to detect drift before it becomes an incident.
- Build decommissioning into the agent lifecycle Retire agent credentials and revoke linked access when the owner changes role, the use case ends or the agent is no longer needed.
Key takeaways
- AI agents create an identity problem because they can keep acting after the human operator has stepped out of the loop.
- The article ties that problem to breach exposure, cost overruns and weak attribution, which are all governance failures as much as technical ones.
- Security teams need runtime ownership, purpose scoping and auditable action trails if they want to govern agents instead of merely observing them.
Related NHI Mgmt Group resources: Agentic AI Identity Guide · AI Agent Authorisation Guide · AI Agent Observability, Audit and Incident Response Guide · AI Agents vs Agentic AI
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, OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The piece centres on agent identities inheriting and misusing authority at runtime. |
| Recommendation — Constrain agent authority to declared tasks and monitor for privilege use outside approved intent. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI agents are treated as non-human identities when they inherit access beyond task scope. |
| Recommendation — Reduce inherited access and scope each agent to the minimum permissions needed for its job. | ||
| NIST AI RMF | GOVERN — AI Governance and Accountability | The article is about organisational governance for AI agents, not just technical controls. |
| Recommendation — Assign accountability, oversight and traceability for each AI agent across its lifecycle. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | Agent identity governance depends on controlling entitlements, permissions and approvals. |
| Recommendation — Review and constrain agent entitlements so access matches current business purpose. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | The risk discussion maps to abuse of inherited credentials and movement across systems. |
| Recommendation — Track agent abuse patterns as credential access and lateral movement behaviour in detection workflows. | ||
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.
- Intent Governance: Intent governance is the practice of controlling not only what an AI agent can access, but whether a proposed action matches the user’s actual purpose and context. It combines permission checks, contextual verification, human approval for high risk actions, and tool controls to reduce misuse of valid authority.
- Runtime authority: Runtime authority is the permission an AI system has while it is actively deciding and acting, not just when it is approved. In governance terms, it is the point where access, tool use, and action scope become operational, which is why build-time review alone cannot prove safety.
- Purpose Drift: Purpose drift is the gradual shift of an identity, process, or AI agent away from its original approved use. In security, it happens when permissions, data access, or automation expand beyond the stated business purpose, creating excess exposure, governance gaps, and higher risk of misuse or compromise.
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.
Published by the NHIMG editorial team on November 5, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org