Regulators no longer accept policy documents as proof of governance. If you can't demonstrate active, continuous management of AI-related risks with operational evidence, you're exposed.
The Australian Prudential Regulatory Authority (APRA) recently emphasized this to banks, insurers, and superannuation trustees: compliance must be grounded in evidence, not vendor slide decks or annual reviews. The Australian Securities and Investments Commission (ASIC) reinforced the message, urging organizations not to wait for perfect clarity before acting.
This checklist translates those regulatory expectations into actionable governance requirements. Each item represents a defensible control you should be able to demonstrate on demand.
Prerequisites
Before you start this checklist, confirm you have:
- Executive sponsorship for AI governance as an operational accountability issue, not just a policy exercise
- Cross-functional representation from legal, risk, IT, and business units that deploy AI
- Access to systems where AI decisions, approvals, and monitoring outcomes are currently documented (even if incomplete)
- Authority to request evidence from third-party AI vendors and SaaS platforms
AI Governance Compliance Checklist
1. Maintain an Authoritative AI System Inventory
□ Every AI system operating in your environment is catalogued with: system name, business owner, risk classification, data types processed, and current lifecycle stage (pilot, production, decommissioned).
What good looks like: When a regulator asks which AI systems process customer financial data, you produce a filtered view from a single source of truth within hours, not days of detective work across departments.
2. Document Supplier Obligations for Each AI System
□ For every AI system, you have records showing: the model provider, any third or fourth parties in the supply chain, data residency commitments, and evidence that each supplier met contractual obligations.
What good looks like: If APRA questions your loan decisioning model's supply chain, you pull a complete supplier record showing who built it, where data is processed, and dated evidence of their last security attestation.
3. Capture Lifecycle Decisions as Records
□ Approvals, risk acceptances, scope changes, and decommissioning decisions are recorded with date stamps, approving parties, and rationale, not buried in email threads or meeting notes.
What good looks like: You can demonstrate who approved your claims triage AI to expand from summarization to decisioning, when that approval occurred, and what risk assessment supported it.
4. Establish Visibility into Embedded AI Usage
□ You have a method to detect AI capabilities embedded in SaaS platforms and browser-based assistants that employees use outside formally approved channels.
What good looks like: When your team discovers employees using an AI summarization feature in a third-party platform, you already knew it existed, assessed it, and either approved it with controls or blocked it.
5. Record Human-in-the-Loop Interventions
□ When humans override AI recommendations or intervene in automated processes, those actions are captured with context: who intervened, why, and what the AI originally recommended.
What good looks like: During an audit of your fraud detection system, you show exactly how many times analysts overrode the AI's classification in the past quarter and the documented reasons for each override.
6. Monitor AI Systems Continuously, Not Quarterly
□ Monitoring runs, drift detection, and exception handling generate time-stamped records, not just summary dashboards reviewed in periodic meetings.
What good looks like: If a regulator asks whether your customer interaction AI has drifted from its approved scope, you produce monitoring logs showing daily checks and the date any anomaly was detected and addressed.
7. Align Board Reporting with Operational Evidence
□ The AI risk position you present to the board is derived from the same records you would provide to a regulator, not a separate summary deck.
What good looks like: Your board receives a quarterly AI risk report, and every metric in that report traces back to the same inventory, monitoring records, and lifecycle documentation that supports regulatory inquiries.
8. Classify AI Systems by Regulatory Impact
□ Each AI system carries a documented risk classification reflecting whether it processes regulated data, makes decisions affecting customers, or operates in a high-consequence environment.
What good looks like: You can instantly filter your AI inventory to show only systems that process sensitive financial information or make automated decisions about customer eligibility.
9. Maintain Evidence of Assurance Activities
□ Security reviews, model validation, bias testing, and third-party audits are recorded with dates, findings, and remediation actions, not just attestation letters filed once.
What good looks like: When asked whether your AI underwent independent validation, you produce the assessment report, the date it was completed, findings that required remediation, and dated evidence those findings were addressed.
10. Document Data Flows for Each AI System
□ You have records showing what data each AI system accesses, where that data originates, whether it includes customer information, and whether it crosses jurisdictional boundaries.
What good looks like: If a regulator questions whether your AI assistant processes medical information, you show exactly which data sources it connects to and confirm whether protected health data is in scope.
Common Mistakes
Treating AI governance as a cybersecurity-only problem. Runtime protection and adversarial testing matter, but they don't create the operational governance record regulators expect. You need both security controls and lifecycle documentation.
Relying on policy documents as proof of compliance. APRA's letter explicitly rejected this approach. Evidence means records of what actually happened: approvals granted, monitoring performed, controls enforced.
Waiting for perfect clarity before acting. ASIC warned against this. Regulatory expectations will continue to evolve, but the organizations building defensible records now will adapt faster than those starting from zero when enforcement arrives.
Maintaining AI inventories in spreadsheets. A spreadsheet updated quarterly can't answer real-time questions about which AI systems were running last Tuesday or who approved a scope change six months ago. You need records management infrastructure.
Assuming embedded AI features don't count. If your employees use AI summarization, translation, or search capabilities inside approved SaaS platforms, those systems process your data and fall under governance expectations. Formal approval doesn't exempt embedded capabilities from oversight.
Next Steps
If you checked fewer than seven items, you have a governance gap that would likely surface in a regulatory inquiry. The organizations that will handle the next wave of AI regulation successfully are building operational evidence now, not scrambling to reconstruct it when APRA sends a letter.
Start with the inventory. You can't govern what you can't see, and you can't prove governance without records. Everything else builds from there.
Australian Prudential Regulatory Authority's official guidelines
