Topic starter
02/01/2026 10:45 am
TL;DR: Agentic AI could change compliance audits from periodic reconstruction to continuous evidence gathering, with one IBM audit described as consuming 2,300 hours of human labor over three weeks, according to Twine Security. The real shift for IAM teams is not speed alone but whether machine-driven evidence can preserve context, lineage, and access accountability without weakening trust.
Editorial analysis by NHI Mgmt Group, based on content published by Twine Security: “The Last Manual Audit: Agentic AI and the Future of Compliance”.
By the numbers:
- That IBM identity and access management audit consumed 2,300 hours of human labor over three weeks, according to Twine Security.
Key questions
Q: How should security teams use agentic AI in compliance audits?
A: Security teams should use agentic AI to gather evidence, correlate records, and flag anomalies, but keep human owners in charge of final audit decisions.
Q: What breaks when audit evidence is still collected as screenshots and exports?
A: Screenshots and exports decay the moment they are taken, so they quickly diverge from the live identity state.
Q: When should organisations prioritise continuous evidence over periodic audit preparation?
A: Prioritise continuous evidence when identity changes are frequent, approvals span multiple systems, or audit questions depend on lineage rather than a single control snapshot.
Practitioner guidance
- Define evidence lineage requirements Map which identity events must be preserved with approval, usage, and ownership context so audit questions can be answered without manual reconstruction.
- Replace snapshot evidence with live records Prioritise systems that maintain continuously updated access histories, entitlement changes, and ticket links instead of one-time exports and screenshots.
- Instrument identity changes at the point of change Capture business reason, approver, and entitlement delta as part of the access workflow so the audit trail is created when the decision is made.
Bottom line: Agentic AI points to a new audit model where evidence is assembled continuously rather than reconstructed after the fact.
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This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 9:16 pm
Agentic AI turns audit work from reconstruction into continuous evidence operations: The core shift is not faster reporting but a different control model for proving identity governance. When evidence is assembled continuously, audit readiness becomes a standing operational state instead of a period-end scramble. Practitioners should treat this as a redesign of audit evidence architecture, not a productivity tweak.
A few things that frame the scale:
- Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously.
A question worth separating out:
Q: How do auditors evaluate AI-generated evidence narratives?
A: Auditors should test whether the narrative is traceable to authoritative source records, whether it preserves approval and usage context, and whether it can be reproduced on demand. A useful narrative explains the sequence of identity decisions, not just the final access state. If the chain cannot be inspected, the narrative is only a summary, not proof.
👉 Read our full editorial: Agentic AI and compliance audits: what changes for identity teams