Why Existing Risk Frameworks Aren't Enough
Most organizations have technology risk programs, cybersecurity frameworks, and vendor management processes — but few have an AI-specific risk management framework that addresses the unique characteristics of AI: model drift, algorithmic bias, data poisoning, unexplainable outputs, and third-party AI dependencies.
The EU AI Act introduces a new mandatory risk classification system for AI — prohibited, high-risk, limited-risk, and minimal-risk. Organizations must now map their AI tools against this taxonomy and implement controls that match the classification. SR 11-7 requires a parallel model risk management program. A unified AI risk management framework satisfies both.
What a Complete AI Risk Management Framework Requires
- Governance structure: defined roles, accountability, and board-level oversight
- AI inventory: a complete register of all AI tools and models in use
- Risk identification: systematic discovery of AI use cases across the organization
- Risk assessment methodology: consistent criteria for evaluating likelihood and impact
- EU AI Act classification: risk tier mapping for all AI systems
- Risk tiering: low, medium, and high classifications with corresponding required controls
- Validation requirements: testing and challenge processes for high-risk models
- Ongoing monitoring: performance tracking, drift detection, and periodic revalidation
- Incident response: procedures for AI-related failures and data exposures
- Third-party risk: vendor assessment and contractual protections for AI providers
A framework that addresses SR 11-7, NIST AI RMF, and the EU AI Act in a single cohesive program positions organizations to satisfy regulators across multiple jurisdictions with consistent documentation.
What Makes Risk Frameworks Fail
- Adopting a framework on paper without building the operational processes to support it
- Inconsistent risk assessments — different analysts producing different results for similar AI tools
- No validation for high-risk models — deployment without independent challenge
- Monitoring that relies on manual review rather than systematic performance tracking
- Framework documents updated annually but not kept current as AI tools change throughout the year
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