What is a technical audit for AI and why is it critical for regulated businesses?

A technical AI audit is about evidence, not opinions

Scope a technical AI audit to a defined system and use case: its tools, data flows, access, logs and stated controls. Record what was tested, the results, limits and gaps, with owners for follow-up. Technical findings do not settle privacy, legal or professional approval.

What a technical audit for AI covers

Agree which of the six areas below are relevant to the system and task. Record exclusions and assign privacy or legal questions to the appropriate reviewer rather than treating this list as a universal audit standard:

• Tooling inventory: sanctioned and unsanctioned AI tools (Shadow AI), extensions, SaaS, meeting tools, code assistants
• Data pipelines: what data is used, how it is transformed, where it is stored, and who can access it
• Identity and access: tenant controls, role-based access, least privilege, logging
• Prompt and output controls: redaction, validation, human review, citations, guardrails
• Security: secrets handling, supply chain risk, dependency scanning, environment separation
• Compliance readiness: gather the relevant DPIA, supplier-contract, retention, incident-response and audit-trail records; have the appropriate reviewers assess any gaps or required approvals

AI Risk & Efficiency Audit

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Why it is critical for regulated businesses

Use the four issues below to select evidence and tests for the workflow. Establish whether an issue is present and what it affects; listing a risk is not evidence that it has occurred or that a regulatory requirement has been met:

• data leakage into public models
• untracked tools creating audit gaps
• outputs entering decision-making without verification
• vendor AI processing that is not contractually visible

What ‘good’ looks like after the audit

Connect each finding to the proposed actions below, an owner and a completion check. An audit recommendation remains open until the relevant change and its verification are recorded:

1. record internal high/medium/low tiers for the AI use cases with reasons; assess any legal classification separately
2. define approved tools and workflows for each tier
3. implement monitoring, logging and evidence capture
4. tighten vendor controls and data contracts
5. train teams with role-specific ‘dos and don’ts’

How to start

Inventory the tools and data in use, then select workflows for examination based on their information, intended decisions and unresolved controls. Agree the scope, access and tests before starting, and record what the audit will not establish.

AI Implementation

Need help assessing an AI workflow and its unresolved risks?

For a technical AI audit, agree the components, test conditions, evidence and exclusions before work starts. Record what the tests do not cover.

• Audit AI risk and opportunity
• Build a usable AI governance framework
• Implement AI with supplier and data controls
• See AI support for financial advisers