What is AI Risk Register?
AI Risk Register is a structured, living document that catalogues all identified risks associated with an organisation's AI systems, including their likelihood, potential impact, current mitigation measures, risk owners, and status, serving as the central tool for managing AI risk across the enterprise.
What is an AI Risk Register?
An AI Risk Register is a formal document or database that records every identified risk related to an organisation's AI systems in a structured format. For each risk, it captures a description of the risk, its likelihood and potential impact, the current mitigation measures in place, the person or team responsible for managing it, and the current status of both the risk and its mitigations.
In simple terms, it is a comprehensive inventory of everything that could go wrong with your AI systems, together with your plan for preventing or managing each issue. It serves as the single source of truth for AI risk management across your organisation.
Why an AI Risk Register Matters
AI systems create risks that are distinct from those of traditional software. Models can drift over time, producing increasingly inaccurate results. Training data can contain biases that produce discriminatory outcomes. AI components from third-party suppliers can introduce hidden vulnerabilities. Regulations can change, making previously compliant systems non-compliant.
Without a centralised register, these risks are managed in isolation, if they are managed at all. Individual teams may be aware of risks within their own scope but lack visibility into risks across the broader AI portfolio. The result is gaps in risk coverage, duplicated effort, and no clear picture of the organisation's overall AI risk exposure.
An AI risk register provides that clear picture. It enables leadership to understand the organisation's aggregate AI risk, prioritise mitigation investments, and demonstrate risk management discipline to regulators and stakeholders.
Key Components of an AI Risk Register
Risk Identification
Each entry begins with a clear description of the risk. Be specific and concrete. Rather than recording "bias risk," describe the specific risk: "The customer credit scoring model may produce higher rejection rates for applicants from certain ethnic groups due to historical bias in the training data."
Risk Classification
Classify each risk along multiple dimensions to support prioritisation and reporting. Common classifications include risk category such as technical, regulatory, ethical, operational, or reputational. Some organisations also classify by the AI lifecycle stage where the risk originates, such as data collection, training, deployment, or monitoring.
Likelihood Assessment
Estimate the probability that the risk will materialise. Use a consistent scale across all risks, whether qualitative (low, medium, high) or quantitative (percentage ranges). Base your assessment on evidence where possible, including historical data, industry benchmarks, and expert judgment.
Impact Assessment
Estimate the potential consequences if the risk materialises. Consider financial impact, regulatory consequences, reputational damage, operational disruption, and harm to individuals. Use a consistent scale that aligns with your organisation's broader risk management framework.
Risk Score
Combine likelihood and impact into an overall risk score that supports prioritisation. The most common approach is a simple matrix where risk score equals likelihood multiplied by impact, but more sophisticated scoring methods may be appropriate for complex AI portfolios.
Current Mitigations
Document the controls and measures currently in place to reduce the likelihood or impact of each risk. This includes technical controls, policies, processes, monitoring, and insurance. Be honest about the effectiveness of current mitigations; overstating their effectiveness creates a false sense of security.
Residual Risk
After accounting for current mitigations, assess the residual risk that remains. This is the level of risk your organisation is currently accepting. If the residual risk exceeds your risk tolerance, additional mitigation action is needed.
Risk Owner
Assign a specific individual as the owner of each risk. The risk owner is accountable for monitoring the risk, ensuring mitigations are maintained, and escalating changes in risk level. Risk ownership should sit with someone who has the authority and resources to take action.
Action Plans
For risks that require additional mitigation, document specific action plans with timelines, responsibilities, and success criteria. Track these plans to completion and verify that they actually reduce the risk as intended.
Building and Maintaining an AI Risk Register
Step 1: Inventory Your AI Systems
Before you can identify risks, you need a complete inventory of your AI systems. This includes systems built in-house, third-party AI tools, AI features embedded in existing software, and AI systems under development. Each system is a potential source of risk that should be assessed.
Step 2: Conduct Risk Identification
For each AI system, systematically identify risks using a combination of structured risk assessment, expert workshops, review of industry incident databases, and analysis of regulatory requirements. Involve stakeholders from technical, business, legal, and compliance functions to ensure comprehensive coverage.
Step 3: Assess and Score
Rate each risk for likelihood and impact, calculate risk scores, and assess residual risk after considering current mitigations. Use consistent scales and document the reasoning behind your assessments.
Step 4: Assign Ownership and Actions
Assign risk owners and develop action plans for risks that exceed your risk tolerance. Ensure owners have the authority and resources to act on their assigned risks.
Step 5: Review and Update Regularly
An AI risk register is a living document. Review it at least quarterly, and update it when new AI systems are deployed, when incidents occur, when regulations change, or when risk assessments change. The register is only useful if it reflects your current risk landscape.
Integrating with Enterprise Risk Management
Your AI risk register should not exist in isolation. Integrate it with your organisation's broader enterprise risk management framework. AI risks should be visible alongside financial, operational, strategic, and compliance risks, enabling leadership to make informed decisions about resource allocation and risk tolerance across all risk categories.
Regional Considerations
Regulatory developments across Southeast Asia are making AI risk management increasingly important. Singapore's Model AI Governance Framework recommends that organisations assess and manage AI risks systematically. The ASEAN Guide on AI Governance and Ethics emphasises risk-based approaches to AI management. Indonesia, Thailand, and the Philippines are developing regulations that will likely require demonstrated risk management for AI systems.
Having a well-maintained AI risk register positions your organisation to demonstrate compliance with these requirements and provides the foundation for regulatory reporting as requirements become more specific.
An AI Risk Register is the essential management tool that gives leadership visibility into the organisation's AI risk exposure. Without it, AI risks are invisible at the executive level until they materialise as incidents, at which point the cost of response far exceeds the cost of prevention.
For business leaders in Southeast Asia, an AI risk register serves three critical functions. First, it enables informed decision-making by providing a clear picture of what risks exist, how severe they are, and whether current mitigations are adequate. Second, it demonstrates risk management discipline to regulators, auditors, customers, and partners. Third, it provides the foundation for prioritising security and safety investments where they will have the greatest impact.
The organisations that manage AI risk most effectively are those that make risk visible, assign clear ownership, and review it regularly. The AI risk register is the tool that makes this possible.
- Start with a complete inventory of all AI systems in your organisation, including third-party tools and embedded AI features.
- Be specific and concrete when describing risks rather than using vague categories like "bias risk" or "security risk."
- Use consistent scales for likelihood and impact assessment that align with your broader enterprise risk management framework.
- Assign specific individuals as risk owners with the authority and resources to act on their assigned risks.
- Review and update the risk register at least quarterly and whenever significant changes occur in your AI portfolio or regulatory environment.
- Integrate your AI risk register with your enterprise risk management framework so AI risks are visible alongside other business risks.
- Use the register to prioritise security and safety investments where they will have the greatest risk reduction impact.
Frequently Asked Questions
How is an AI risk register different from a general IT risk register?
An AI risk register includes risk categories that are specific to AI systems and do not appear in general IT risk registers. These include model drift and degradation, training data bias, adversarial attack vulnerabilities, AI-specific regulatory compliance, algorithmic fairness, and explainability gaps. While AI risks should ultimately be integrated into your enterprise risk management framework, they require specialised assessment methods and technical expertise that general IT risk processes may not provide.
Who should own the AI risk register?
Ownership of the AI risk register should sit with a senior leader who has visibility across the organisation's AI portfolio and the authority to drive risk mitigation actions. This could be a Chief AI Officer, Chief Risk Officer, Chief Technology Officer, or the chair of an AI governance committee. The owner is responsible for ensuring the register is maintained, reviewed regularly, and integrated with enterprise risk management. Individual risk entries should have their own designated owners who are accountable for specific mitigations.
More Questions
The number of risks depends on the size and complexity of your AI portfolio. A small organisation with a few AI systems might have 20 to 30 identified risks. A large enterprise with dozens of AI systems could have hundreds. The goal is comprehensiveness without redundancy. Each risk should be distinct and specific enough to be actionable. If your register has too few entries, you are likely missing risks. If it has too many, consider whether some entries are duplicates or can be consolidated.
Need help implementing AI Risk Register?
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