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Executive guide

AI Audit Readiness: From Evidence Requests to Continuous Proof

How internal audit, risk and AI teams can reduce retrospective evidence reconstruction and create defensible AI records continuously.

Internal Audit · Risk · Compliance5 operating steps
01

Identify material AI decisions

Prioritise approvals, model changes, exceptions, incidents, supplier dependencies and customer-impacting activity that may need future reconstruction.

02

Define evidence requirements in advance

Specify what evidence must exist for approvals, controls, testing, human review and regulatory obligations before audit begins.

03

Preserve provenance and integrity

Evidence should identify source, time, version and integrity context rather than relying on screenshots or copied documents.

04

Separate evidence from assertions

Post-hoc interpretation can be valuable, but it should not silently alter the original record. Preserve later assertions as separate evidence.

05

Create an authorised Audit Room

Give reviewers scoped, traceable access to evidence while preserving tenant, role and confidentiality boundaries.

Implementation note

This guide provides an operating structure. The specific controls, authorities, legal interpretations and assurance requirements should be adapted to the institution, use case and jurisdiction.

From guidance to operating control

Put this operating model into practice.

Cybatar connects AI inventory, policy, accountable decisions, evidence, assurance and economics so governance can operate continuously rather than live in disconnected documents.