Explore how agentic AI can automate audit evidence collection,
strengthen verification and chain of custody, support continuous controls monitoring, and help
auditors focus on strategic risk analysis.
Virksomhedens præstationer og ESG
november 11, 2026
From evidence gathering to risk analysis: How AI is reshaping internal audit
Viktige læringspunkter
- Agentic AI can reduce the evidence collection burden by automating repetitive audit tasks, allowing auditors to spend more time analyzing risk and evaluating controls
- Trust must be built into AI-driven evidence collection. Verification methods such as tool receipts, provenance logging, verifier agents, and practitioner review help ensure AI-generated evidence can be validated.
- Cryptographic controls can strengthen evidence integrity. Hashing, timestamping, hash-chaining, and Merkle trees can help demonstrate that evidence has not been altered after collection.
- AI agents require strong governance as non-human identities. Organizations should address credential provisioning, access and privileges, ownership, maintenance, oversight, and incident response.
- Continuous controls monitoring can move audit beyond point-in-time testing, enabling broader testing, faster identification of control issues, and evidence that reflects changes over time.
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