Topic starter
07/06/2026 8:51 pm
TL;DR: C1.ai says agentic AI is forcing auditors and compliance teams to treat every AI agent as a non-human identity with access, control, and evidence requirements, while traditional audit cadences assume stable identities and reviewable access. The audit problem is no longer just access management, but governance for identities that can change scope and activity faster than review cycles can catch up.
Editorial analysis by NHI Mgmt Group, based on content published by C1.ai: “Here's What Your Auditor Thinks About Agentic AI”.
Key questions
Q: How should organizations approach the governance of AI agents?
A: Organizations should adopt a governance framework that incorporates continuous visibility, adaptive IAM practices, and stringent policy-based controls.
Q: Why do traditional audit cycles struggle with agentic AI?
A: Traditional audit cycles assume access and activity persist long enough to be reviewed later.
Q: What breaks when agentic AI has no acceptable use policy?
A: Without an acceptable use policy, teams lose the baseline for deciding which actions are allowed, which data may be touched, and who approves exceptions.
Practitioner guidance
- Define AI agents as governed identities Add every agent that can act on behalf of a human into the identity inventory, with named ownership, access boundaries, and lifecycle status.
- Move evidence capture upstream Collect authorisation, configuration, and workflow evidence at issuance and execution time instead of relying on post-hoc audit sampling.
- Write an acceptable use policy for agents Set explicit rules for what agentic systems may do, what data they may touch, and which workflows they are allowed to influence.
Bottom line: Agentic AI changes the audit subject by turning each AI agent into a governed identity with access, evidence, and ownership requirements.
What's in the full article
C1.ai's full blog covers the conversational details and audit framing this post intentionally leaves at a higher level:
- The discussion points from C1's conversation with BARR Advisory on how auditors should evaluate agentic AI
- The practical guidance on acceptable use policy, evidence gathering, and governance ownership for AI agents
- The examples of how auditors are using AI internally, including front-stage analysis and backstage prompt structuring
- The standards discussion around ISO 42001, HITRUST AI certification, and why SOC 2 is limited for AI governance
👉 Read C1.ai's analysis of what agentic AI means for auditing and identity governance →
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This topic was modified 3 days ago by NHI Mgmt Group
08/06/2026 8:34 am
Agentic AI turns the audit subject into a governed non-human identity: once an AI agent can act on behalf of a human, it is no longer enough to audit the human and assume the system is covered. The identity subject has changed, so the governance object has changed as well. That means access, evidence, and accountability must be assigned to the agent as a first-class identity.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should organisations test before adopting agentic AI in security operations?
A: Organisations should test whether the agent can act safely under failure, whether its actions are traceable, and whether an incorrect decision can be rolled back. The key question is not only what the agent can do, but what happens when upstream telemetry is wrong or incomplete. Without that test, automation can spread error faster than humans can correct it.
👉 Read our full editorial: Agentic AI raises the identity governance bar for auditors
This post was modified 3 days ago by NHI Mgmt Group