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AI Risk Register Template

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 Page

  1. Connect to your broader risk framework – This AI risk register should not stand alone. Integrate it into your organisation’s existing Governance, Risk, and Compliance (GRC) processes, enterprise risk register, and risk appetite statements.
  2. Review the example risks – These are common AI-related risks relevant to many Australian businesses.
  3. Tailor to your organisation – Add, remove, or adjust risks so the register reflects your industry, scale, and regulatory environment.
  4. Assess likelihood and residual risk – Use your organisation’s established risk rating framework to complete the blank fields.
  5. Assign risk owners – Allocate responsibility to relevant leaders or teams to ensure ongoing monitoring.
  6. Implement control measures – Use the example measures provided as a starting point, and expand with your own policies, technologies, or oversight mechanisms.
  7. Update regularly – As AI tools, regulations, and organisational use cases evolve, revisit this register to keep it accurate and effective.

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

When assessing AI risks in your organisation, consider the following lenses:

  • 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.

Next Steps

Ready to assess specific AI risks? → AI Risk Assessment Checklist

Template Disclaimer & Licence

Disclaimer

The purpose of this template is to provide best practice guidance on implementing safe and responsible AI governance in Australian organisations.

SafeAI-Aus has exercised care and skill in the preparation of this material. However, SafeAI-Aus does not guarantee the accuracy, reliability, or completeness of the information contained.

The content reflects best practice principles but is intended as a starting point only. Organisations should adapt this template to their specific context and may wish to seek advice from legal counsel, governance, risk, or compliance officers before formal adoption.

This publication does not indicate any commitment by SafeAI-Aus to a particular course of action. SafeAI-Aus accepts no responsibility or liability for any loss, damage, or costs incurred as a result of the information contained in this template.

Licence

This template is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.

You are free to:

  • Share — copy and redistribute the material in any medium or format.
  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

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Attribution statement for reuse:
"This template was developed by SafeAI-Aus and is licensed under CC BY 4.0. Source: SafeAI-Aus."

Full licence text: https://creativecommons.org/licenses/by/4.0/