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.
Výsledky společnosti a ESG
11 listopadu, 2026
From evidence gathering to risk analysis: How AI is reshaping internal audit
Hlavní poznatky
- 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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