D17: Risk Management

Governance Pillar

Risk Management covers the frameworks for identifying, assessing, mitigating, and monitoring AI-specific risks throughout the AI lifecycle. It includes risk taxonomies, assessment methodologies, mitigation strategies, risk appetite definition, and the integration of AI risk into enterprise risk management.

Why It Matters

AI introduces risks that traditional risk frameworks do not adequately address — model drift, training data bias, adversarial manipulation, hallucination, and opaque decision-making. Organizations that do not extend their risk management to cover AI-specific categories make deployment decisions without full understanding of the potential consequences, exposing themselves to operational, financial, and reputational harm.

Maturity Levels

Level 1: Foundational

AI risks are not systematically identified; risk management relies on general IT risk frameworks that do not cover AI-specific categories.

Level 2: Developing

An AI risk taxonomy has been developed and initial assessments conducted for some projects, but integration with enterprise risk management is limited.

Level 3: Defined

A standardized AI risk assessment methodology is applied to all initiatives; risk appetite is defined, and AI risks are reported within the enterprise risk framework.

Level 4: Advanced

Continuous AI risk monitoring operates with automated alerts; scenario analysis and stress testing are conducted regularly, and risk metrics inform deployment decisions.

Level 5: Transformational

AI risk management is predictive, using AI itself to identify emerging risks; the organization's risk management capability is recognized as industry-leading.

Key Activities

• Develop an AI-specific risk taxonomy covering technical, operational, ethical, and regulatory risk categories
• Implement standardized AI risk assessment methodology for all initiatives
• Define organizational AI risk appetite and escalation thresholds
• Integrate AI risk reporting into enterprise risk management frameworks
• Conduct scenario analysis and stress testing for high-impact AI systems
• Monitor AI risks continuously with automated alerting and dashboards

Assessment Criteria

• Existence of an AI-specific risk taxonomy and assessment methodology
• Percentage of AI initiatives with documented risk assessments
• Integration of AI risk reporting into enterprise risk management
• Evidence of risk assessment outcomes influencing deployment decisions

Abdelalim, T. (2025). “Risk Management — COMPEL Governance Pillar.” COMPEL by FlowRidge. https://www.compel.one/domain/risk-management