Navigating the AI governance landscape: NIST risk management insights

Governance: Building the foundations of trust

Effective AI governance is crucial for managing risks associated with AI technologies. The Playbook emphasises the importance of:

• Legal and Regulatory Compliance: Ensuring AI systems align with relevant laws, such as data privacy, non-discrimination, and security standards.
• Transparency and Documentation: Establishing clear policies and procedures to document the design, deployment, and monitoring of AI systems.
• Defining Risk Tolerances: Creating policies that outline acceptable levels of risk across operational, reputational, and safety domains.

Action Point for CISOs and ISOs: Develop a centralised inventory of AI systems, documenting compliance status, system capabilities, and risk mitigation measures.

Mapping: Identifying AI risks

Mapping risks involves a systematic evaluation of how AI systems are designed, used, and potentially misused. This includes:

• Impact Assessments: Evaluating how AI might affect users, communities, and business objectives.
• Stakeholder Engagement: Involving diverse perspectives, including end-users and impacted communities, to anticipate risks early.

Action Point: Regularly conduct algorithmic impact assessments to identify and mitigate potential biases or inequities in AI systems.

Measuring: Quantifying risks

To manage AI risks effectively, organisations need robust measurement tools to assess potential impacts and their likelihood. Key strategies include:

• Risk Scoring Models: Use tools like RAG (Red-Amber-Green) scales or quantitative models to prioritise risks.
• Performance Monitoring: Continuously track system performance to identify model drift or deviations from expected behaviour.

Action Point: Implement ongoing monitoring systems that capture data on AI performance and its alignment with organisational risk tolerances.

Managing: Mitigating and monitoring risks

The Playbook advocates for proactive management to address emerging AI risks, including:

• Incident Response Protocols: Establish clear guidelines for handling AI-related incidents, such as performance degradation or ethical concerns.
• Decommissioning Processes: Develop policies for safely phasing out outdated or high-risk AI systems.
• Stakeholder Feedback Mechanisms: Create channels for users and communities to report AI-related issues and provide input on system improvements.

Action Point: Build a multidisciplinary incident response team to handle AI-related crises swiftly and effectively.

Leadership and culture

The role of senior leadership is critical in embedding a culture of accountability and risk awareness. The Playbook highlights:

• C-Suite Involvement: Encourage executives to take an active role in defining risk tolerances and supporting governance efforts.
• Workforce Training: Provide targeted training for all AI actors, from developers to compliance teams, to enhance their understanding of risk management.

Action Point: Promote a safety-first mindset by integrating risk management into organisational training programmes and performance evaluations.