Topic starter
02/10/2025 2:29 pm
TL;DR: AI agents are now accessing sensitive data and behaving like high-risk identities, with 96% of tech leaders calling them a growing security threat and only 44% of organisations having policies to manage them, according to SailPoint-commissioned Dimensional Research. The governance gap is no longer about visibility alone; it is about controlling autonomous access before behaviour turns into exposure.
Editorial analysis by NHI Mgmt Group, based on content published by SailPoint: “AI agents: The new attack surface”.
By the numbers:
- 96% of tech leaders agree AI agents are a growing security threat.
Key questions
Q: What breaks when AI agents are monitored like ordinary automated jobs?
A: What breaks is the assumption that fixed schedules and static logs are enough.
Q: Why do AI agents increase identity risk even when the login succeeds?
A: A successful login only proves that the agent reached the system.
Q: How can organisations tell whether AI agent governance is actually working?
A: Look for evidence that agent access is ephemeral, traceable, and constrained at the action level.
Practitioner guidance
- Define agent identity classes Separate AI agents from human users and scripted automation in your identity model so policy, review, and monitoring can reflect runtime behaviour rather than job role.
- Scope agent permissions before execution Assign each agent the minimum data sources, APIs, and tools it needs, and prevent it from inheriting broad workspace or production access by default.
- Bind approvals to task boundaries Require explicit approval points for sensitive actions such as data export, credential use, or privilege escalation so the agent cannot freely expand its own scope.
Bottom line: AI agents are now identity subjects with enough autonomy to outgrow controls that were designed for predictable, human-paced access patterns.
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06/10/2026 4:37 pm
AI agent identity risk is a governance problem before it is a threat problem: enterprises are already granting AI systems access that rivals or exceeds human access, but most controls still assume a human operator behind the session. That assumption breaks when the actor can decide, select tools, and act at runtime. The implication is that IAM programmes must stop treating agents as edge cases and start treating them as governed identity subjects.
A few things that frame the scale:
- 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.
- 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.
A question worth separating out:
Q: Should organisations treat AI coding agents as part of IAM and PAM governance?
A: Yes, when those agents can act on code, data, or tools in ways that affect production risk. Their permissions should be scoped, reviewed, and audited like other privileged systems, especially when they interact with sensitive routes, secrets, or regulated data. The governance question is who can let the agent act, and under what policy.
👉 Read our full editorial: AI agent identity risk is outpacing enterprise IAM controls