Preparing for your first clinical AI governance audit

Whether it comes from a medical indemnity insurer, a regulatory body, or an internal quality committee, your first clinical AI governance audit will ask a predictable set of questions. Practices that prepare for these questions before the audit arrives are in a fundamentally different position from those that scramble to assemble evidence after the fact.

This guide covers what to expect, what evidence to have ready, and the governance gaps that cause the most problems during review.

What triggers an audit

Clinical AI governance audits do not yet follow a standardised schedule in Australia. They emerge from several sources:

• Medical indemnity reviews: Insurers like Avant, MDA National, and MIGA are beginning to include AI governance questions during policy renewals, particularly for practices that have disclosed AI tool usage
• Regulatory inspections: While the TGA's current focus is on pre-market assessment of AI as Software as a Medical Device (SaMD), post-market surveillance is expanding
• Internal quality reviews: Practice accreditation processes under the National Safety and Quality Health Service Standards increasingly touch on digital health governance
• Incident response: An adverse event involving an AI-assisted finding will trigger retrospective review of the governance arrangements that were in place at the time

The most stressful audits are reactive — triggered after something goes wrong. The most productive audits are proactive ones you initiate yourself.

The five evidence categories

Regardless of who initiates the audit, the evidence they require falls into five categories that map directly to the CAIOS framework domains.

1. Governance structure

Auditors want to see that your practice has assigned governance responsibility for clinical AI. This means:

• A named governance lead (typically the practice director or a senior clinician)
• Documented governance meeting minutes showing AI tools are discussed regularly
• A governance policy that is current, signed, and reviewed within the last 12 months
• Clear escalation paths for AI-related concerns

Common failure: Governance responsibility is assumed but not documented. The practice director "oversees everything" but there is no evidence of regular governance review.

2. Tool inventory and risk assessment

Every AI tool in clinical use should be registered with sufficient detail to demonstrate informed deployment:

• Tool name, vendor, version, and TGA classification status
• Intended clinical use and known limitations
• Risk assessment outcome (risk level, mitigations, residual risk acceptance)
• Deployment date and review schedule

Common failure: Tools are listed in IT asset registers but without clinical governance dimensions. Risk assessments were done at deployment but never updated after software changes or expanded use cases.

3. Clinical oversight documentation

This is where audits get specific. The auditor wants to see not just that oversight exists in theory, but that it operates in practice:

• Documented workflows showing where human oversight occurs in each AI-assisted process
• Evidence of clinician training on AI tool capabilities and limitations
• Records of cases where clinician judgment overrode AI output
• Defined protocols for handling discordant findings

Common failure: Practices state that radiologists "always review AI outputs" but cannot produce evidence of a systematic process. Override decisions are made but not logged.

4. Performance monitoring records

Ongoing monitoring evidence demonstrates that your practice does not simply deploy AI tools and forget about them:

• Quarterly (minimum) performance review records
• Concordance metrics between AI outputs and clinical findings
• Documentation of any performance drift or anomalies detected
• Evidence that monitoring findings informed governance decisions

Common failure: No local performance data exists. The practice relies entirely on vendor validation studies conducted in different clinical environments.

5. Incident management history

The absence of incident records is itself a finding. Auditors expect to see:

• A defined incident classification framework (what constitutes a reportable AI incident)
• Incident logs with investigation notes and resolution documentation
• Evidence that incidents were reviewed at governance meetings
• Corrective actions taken and their outcomes

Common failure: AI-related incidents are discussed informally but never formally logged. Staff are unsure what constitutes a reportable incident versus normal clinical disagreement with AI output.

Preparing your evidence pack

Ahead of any audit, assemble a governance evidence pack containing:

Governance layer - Current AI governance policy (signed, dated, reviewed) - Governance meeting minutes from the past 12 months - Governance structure chart showing oversight responsibilities

Tool layer - Complete AI tool register with clinical governance metadata - Risk assessments for each registered tool - Evidence of periodic tool reviews

Operational layer - Clinical workflow documentation for each AI-integrated process - Staff training records for AI tool competency - Performance monitoring reports (most recent 4 quarters)

Incident layer - Incident log (even if empty, the existence of the process matters) - Investigation and resolution records for any logged incidents - Evidence of corrective actions implemented

Evidence integrity - Sealed or timestamped evidence documents - Version history showing document evolution - Export capability for the complete evidence set

The 30-day preparation timeline

If you know an audit is coming, this timeline covers the minimum preparation:

Week 1: Assemble your tool register. Verify every entry is current. Update TGA classification status, version numbers, and review dates.

Week 2: Review your governance policy. If it is older than 12 months, update and re-sign it. Ensure it addresses all five CAIOS domains: governance structure, risk management, clinical validation, monitoring, and evidence management.

Week 3: Compile your performance monitoring data. If you have not been monitoring, start now. Even two weeks of concordance tracking is better than nothing — but acknowledge the gap honestly.

Week 4: Review your incident logs. If none exist, create the process immediately and document it. Conduct a retrospective review of any known incidents and log them formally.

What auditors value most

The practices that perform best in governance audits share a common trait: they treat governance as infrastructure rather than documentation. Their evidence is not assembled for the audit — it exists because governance is integrated into their clinical workflows.

Auditors can tell the difference between a practice that maintains governance continuously and one that assembled evidence the week before the review. Timestamp patterns, document version histories, and the specificity of monitoring data all signal whether governance is operational or performative.

The goal is not to have perfect governance. It is to demonstrate that your practice has a functioning system for governing clinical AI, that you are aware of your gaps, and that you have a credible plan for closing them. Auditors value honest self-assessment over polished documentation that papers over structural gaps.