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AI Risk Register Template

Purpose: Central tracking tool for identifying, assessing and managing AI-related risks Audience: Risk managers, governance teams, compliance officers | Time: 30-45 minutes setup, ongoing updates

This register provides a starting point for organisations to identify, assess and manage risks associated with the use of AI. Each entry includes example control measures, while columns for likelihood, residual risk and risk owner are left blank for organisations to complete based on their context.

How to Use This Template

  1. Connect this AI risk register to your organisation's existing Governance, Risk, and Compliance (GRC) processes and enterprise risk register
  2. Review the example risks below (common AI-related risks for Australian businesses)
  3. Tailor risks to your organisation's industry, scale and regulatory environment
  4. Assess likelihood and residual risk using your established risk rating framework
  5. Assign risk owners to relevant leaders or teams for ongoing monitoring
  6. Implement control measures starting with the examples provided, then expand with your own policies and oversight mechanisms
  7. Update regularly as AI tools, regulations and use cases evolve

AI Risk Register

Risk ID Risk Name Description Potential Impact Example Control Measures Likelihood Residual Risk Risk Owner
R1 Regulatory Non-Compliance Use of AI systems that breach Australian privacy law, consumer protection, workplace legislation, or sector-specific obligations. Legal penalties, reputational damage, loss of customer trust, forced system shutdown. Conduct legal/ethical reviews of AI use; align with Privacy Act and sector standards; maintain audit trail of AI decisions.
R2 Bias and Discrimination AI models embed or amplify bias in decision-making (e.g. hiring, credit, customer service). Discrimination claims, HR disputes, reputational harm, reduced fairness and equity. Implement fairness testing; diverse training data; human-in-the-loop for sensitive decisions; regular bias audits.
R3 Data Security & Privacy Breach Sensitive personal or business data used in AI training or prompts leaks through insecure systems. Regulatory fines, lawsuits, cyber incidents, erosion of customer confidence. Apply data minimisation; use approved secure platforms; encryption; staff training on safe data handling.
R4 Misinformation & Hallucination AI systems generate inaccurate, misleading, or fabricated outputs presented as factual. Misguided decisions, reputational risk, financial loss, reduced trust in AI. Require human review of critical outputs; maintain verification steps; label AI-generated content clearly.
R5 Vendor Lock-In & Dependency Heavy reliance on a single AI provider without clear exit strategies. Rising costs, limited flexibility, exposure if provider fails or withdraws service. Diversify providers; contract clauses for portability; maintain internal capability; develop contingency plans.
R6 Workforce & Cultural Impact Poorly managed AI adoption leads to fear, resistance, or job displacement concerns. Staff disengagement, loss of talent, industrial relations challenges. Communicate openly with staff; provide reskilling programs; include employees in AI adoption planning.
R7 Operational Failures Over-automation or inadequate human oversight leads to business process failures. Service disruption, financial loss, customer dissatisfaction. Define human oversight points; fallback processes; robust testing before deployment.
R8 Reputational Backlash Public perception that AI is unsafe, unethical, or misaligned with community expectations. Negative media coverage, brand damage, customer attrition. Publish responsible AI policy; adopt transparent communication; align with community values; independent review boards.

How to Assess AI Risk

Assessment Lenses

When assessing AI risks in your organisation, consider these dimensions:

  • 🏢 Business Context – Where is AI being used (customer-facing, internal, back-office)? How critical are these processes to operations or reputation?
  • ⚖️ Legal & Regulatory Obligations – Which laws, industry codes, or standards apply (e.g. Privacy Act, workplace law, consumer law, sector-specific regulators)?
  • 🔒 Data Sensitivity – What kind of data is being processed (personal, confidential, intellectual property)? Could exposure cause harm?
  • 👥 Human Impact – How could staff, customers, or communities be affected by errors, bias, or automation decisions?
  • 🤖 Technology Reliability – What are the limits of the AI model? How often are errors or hallucinations likely to occur and in what context?
  • 📊 Governance & Oversight – Who is responsible for approving, monitoring and reviewing AI systems? Are there escalation paths when risks materialise?
  • 🌐 Reputation & Trust – How might customers, employees, regulators, or the media react if something goes wrong?

A Practical Approach:

  1. Identify the AI system, its purpose and stakeholders impacted
  2. Analyse risks across the above dimensions
  3. Rate each risk using your organisation's existing risk rating framework (likelihood × consequence), ensuring consistency with enterprise risk management
  4. Mitigate by implementing appropriate controls (technical, procedural, cultural)
  5. Monitor & Review regularly, as both technology and regulation evolve rapidly
  6. Integrate into the broader GRC processes — AI risks should sit within the same governance and reporting mechanisms as other strategic, financial and operational risks

How the Risk Register Process Supports the Guardrails

The risk register process is not just a compliance exercise — it is a practical way to demonstrate alignment with the Australian Government’s Voluntary AI Safety Standard. By identifying, analysing and monitoring AI risks, organisations strengthen their ability to act consistently with the 10 guardrails.

  • It ensures AI adoption is human-centred, fair and respectful of wellbeing.
  • It embeds privacy, security, reliability and safety considerations into day-to-day governance.
  • It supports transparency, accountability and contestability by assigning risk owners and documenting controls.
  • It reinforces compliance with law and integrates AI into existing GRC frameworks.
  • It encourages responsible design, deployment and ongoing monitoring, ensuring AI adoption is beneficial and sustainable.

Alignment with Australian Standards

Standards Compliance

✓ Measure and manage risks — This register is the core tool for ongoing risk management, tracking likelihood, impact and control measures

✓ Decide who is accountable — "Risk Owner" column ensures every risk is assigned to a specific individual

✓ Share essential information — Central documentation shares safety information with stakeholders and governance bodies

✓ Guardrail 2 – Risk management — Provides structure for documenting risk assessments and mitigation effectiveness

✓ Guardrail 1 – Accountability — "Risk Owner" assignment supports clear accountability for risk outcomes

✓ Guardrail 9 – Record-keeping — Serves as formal record of the organisation's risk landscape and management efforts

✓ Guardrail 7 – Monitoring impacts — "Residual Risk" and review columns support ongoing monitoring of risk levels

Next Steps

Where to go from here:

Related templates:

External resources:

Disclaimer & Licence

Disclaimer: This template provides best practice guidance for Australian organisations. SafeAI-Aus has exercised care in preparation but does not guarantee accuracy, reliability, or completeness. Organisations should adapt to their specific context and may wish to seek advice from legal, governance, or compliance professionals before formal adoption.

Licence: Licensed under Creative Commons Attribution 4.0 (CC BY 4.0). You are free to copy, adapt and redistribute with attribution: "Source: SafeAI-Aus (safeaiaus.org)"