Topic starter
07/06/2026 9:19 pm
TL;DR: Agentic AI digital employees could shift audits from periodic evidence collection to continuous investigation, context-building, and live assurance, according to Twine Security’s analysis of manual audit pain points. That changes the compliance assumption from human-paced reconstruction to machine-paced review, which is a governance model shift rather than a productivity tweak.
Editorial analysis by NHI Mgmt Group, based on content published by Twine Security: “The Last Manual Audit: Agentic AI and the Future of Compliance”.
Key questions
Q: How do teams handle audits when evidence is changing continuously?
A: Teams need to shift from static evidence packs to continuously queryable identity records.
Q: Why do manual audit processes struggle with agentic AI review workers?
A: Manual processes assume that evidence will stay stable long enough for people to collect, reconcile, and certify it.
Q: What breaks when audit reviews still depend on periodic certification?
A: Periodic certification becomes a weak control when access changes faster than the review cycle.
Practitioner guidance
- Map audit evidence to live identity data Replace screenshot-based evidence packs with continuously queryable records for access, approvals, role changes, and usage history.
- Build lineage into identity records Capture who approved access, when it changed, and how the entitlement was inherited so an audit worker can reconstruct context without manual chasing.
- Separate evidence collection from authorization Treat continuous monitoring as an assurance input, not as an approval decision, so the same runtime signal does not become the control itself.
Bottom line: Manual audits depend on after-the-fact reconstruction, and that assumption weakens as agentic AI can investigate continuously.
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This topic was modified 4 days ago by NHI Mgmt Group
08/06/2026 9:13 am
Manual audit is a governance model, not just a workflow, and it is now under structural strain. The article describes audits as reconstruction after the fact, which is exactly the assumption agentic AI begins to erase. When review becomes continuous and context is assembled on demand, the control question shifts from how fast humans can gather evidence to whether the governance model still depends on human pacing. Practitioners should treat audit design as a lifecycle problem, not a productivity problem.
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 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. The control model should define provenance, review points, and exception handling so automation improves assurance without becoming an unreviewed source of truth.
👉 Read our full editorial: Agentic AI audit workers expose compliance limits in manual review
This post was modified 4 days ago by NHI Mgmt Group