AI Risk Management Playbook
Executive Summary
The AI Risk Management Playbook provides financial institutions with a comprehensive framework for implementing responsible AI governance. Built on the G3MA methodology (Govern, Map, Measure, Manage), this playbook offers practical guidance for managing AI risks across all stages of the AI lifecycle.
🎯 What You'll Achieve
- Comprehensive AI governance framework
- Risk-based approach to AI assurance
- Regulatory compliance alignment
- Operational excellence in AI deployment
📋 What's Included
- 150+ pages of guidance and templates
- 6 core AI risk domains mapped
- 62+ control implementations
- 56+ key performance measures
The G3MA Framework
G3MA represents a systematic approach to AI assurance that scales across organizational maturity levels and regulatory environments. Each pillar builds upon the previous, creating a comprehensive governance ecosystem.
1. Govern
Establish AI governance foundations
Key Components
- AI Ethics and Principles Framework
- Governance Structure and Roles
- Policy Development and Approval
- Decision-Making Frameworks
- Stakeholder Engagement Model
Deliverables
- AI Governance Charter
- Ethics Review Board Charter
- AI Risk Appetite Statement
- Policy Framework Document
- RACI Matrix for AI Decisions
2. Map
Identify and document risks and controls
Key Components
- AI Risk Domain Identification
- Control Mapping and Assessment
- Relationship Documentation
- Impact and Likelihood Analysis
- Control Effectiveness Evaluation
Deliverables
- AI Risk Register
- Control Catalog
- Risk-Control Matrix
- Control Gap Analysis
- Risk Heatmap
3. Measure
Define metrics and monitoring systems
Key Components
- KPI and Metric Definition
- Monitoring System Design
- Threshold and Alert Setting
- Reporting Dashboard Creation
- Performance Benchmarking
Deliverables
- AI Metrics Framework
- Monitoring Dashboard
- Alerting System
- Performance Reports
- Benchmark Analysis
4. Manage
Implement continuous management processes
Key Components
- Continuous Monitoring Processes
- Incident Response Procedures
- Remediation and Action Plans
- Regular Review and Updates
- Improvement Feedback Loops
Deliverables
- Management Procedures
- Incident Response Playbook
- Remediation Templates
- Review Calendar
- Improvement Framework
Implementation Guide
12-Week Implementation Roadmap
Phase 1: Foundation (Weeks 1-3)
Establish governance foundation and organizational readiness.
Week 1-2: Setup
- Stakeholder identification
- Governance structure design
- Charter development
Week 3: Validation
- Charter approval
- Team formation
- Resource allocation
Phase 2: Risk Mapping (Weeks 4-7)
Comprehensive risk identification and control mapping.
Week 4-5: Risk Assessment
- AI inventory creation
- Risk identification workshops
- Initial risk scoring
Week 6-7: Control Mapping
- Control identification
- Effectiveness assessment
- Gap analysis completion
Phase 3: Measurement (Weeks 8-10)
Implement monitoring and measurement frameworks.
Week 8-9: Metrics Design
- KPI definition
- Data source identification
- Dashboard design
Week 10: Implementation
- Monitoring system setup
- Alert configuration
- Initial reporting
Phase 4: Management (Weeks 11-12)
Establish ongoing management and improvement processes.
Week 11: Process Setup
- Management procedures
- Response protocols
- Review schedules
Week 12: Validation
- End-to-end testing
- Training completion
- Go-live readiness
AI Risk Domains
R-MP: Model Performance
Risks related to AI model accuracy, reliability, and performance degradation over time.
- Model drift and degradation
- Performance monitoring gaps
- Accuracy threshold breaches
- False positive/negative rates
R-DQ: Data Quality
Risks arising from poor data quality, including completeness, accuracy, and consistency issues.
- Training data poisoning
- Data completeness issues
- Labeling errors
- Data freshness problems
R-CP: Cyber/Privacy
Cybersecurity and privacy risks including data breaches, adversarial attacks, and compliance violations.
- Adversarial attacks
- Data privacy violations
- Model extraction attacks
- Prompt injection risks
R-FB: Fairness/Bias
Risks related to algorithmic bias, unfair treatment, and discriminatory outcomes.
- Algorithmic bias
- Discriminatory outcomes
- Protected class impact
- Fairness metric violations
R-EG: Explainability/Governance
Risks from lack of transparency, explainability, and proper governance structures.
- Black box decision-making
- Inadequate documentation
- Governance gaps
- Audit trail deficiencies
R-OS: Operational/Systemic
Operational and systemic risks including system failures, integration issues, and business disruption.
- System integration failures
- Operational downtime
- Process disruption
- Dependency vulnerabilities
Regulatory Mapping
The G3MA framework has been designed to align with major global AI regulations and standards. This section provides a comprehensive mapping to help organizations ensure compliance across multiple jurisdictions.
EU AI Act
The European Union's comprehensive AI regulation framework. G3MA maps directly to the risk-based approach and governance requirements.
Key Alignments:
- Article 9: Risk management system ↔ G3MA Map phase
- Article 15: Quality management ↔ G3MA Measure phase
- Article 16: Record keeping ↔ G3MA Govern documentation
- Article 17: Transparency obligations ↔ G3MA Manage reporting
US Executive Order 14110
US Federal framework for AI safety, security, and trustworthiness. G3MA provides structured approach to meet federal requirements.
Key Alignments:
- Section 4.1: Safety testing ↔ G3MA Measure testing controls
- Section 4.2: Content authentication ↔ G3MA Govern provenance
- Section 4.3: Risk assessments ↔ G3MA Map phase
Canada AIDA
Canada's Artificial Intelligence and Data Act. G3MA framework anticipates requirements for risk-based governance.
Anticipated Alignments:
- Risk assessment requirements ↔ G3MA Map methodology
- Mitigation measures ↔ G3MA control framework
- Monitoring obligations ↔ G3MA Measure phase
ISO/IEC 23053:2022
International standard for AI risk management. G3MA framework directly implements the standard's risk-based approach.
Direct Mappings:
- Clause 5: Risk management process ↔ Complete G3MA lifecycle
- Clause 6: Risk assessment ↔ G3MA Map phase
- Clause 7: Risk treatment ↔ G3MA Manage phase
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