Risk Management Assessment
Comprehensive evaluation of risk identification, mitigation, and governance capabilities for Agentic AI
Version: 1.0 | Last Updated: January 2025
Part of: Agentic AI Executive Guide - Appendix A
Instructions for Use
- Engage risk management, legal, compliance, and security teams in the assessment
- Consider both technical and non-technical risks across the AI lifecycle
- Use the risk heat map to prioritize mitigation efforts
- Review compliance requirements specific to your industry and geography
- Update risk assessments quarterly as the AI landscape evolves
Category 1: Governance & Compliance
Assesses the maturity of AI governance structures, policy frameworks, and regulatory compliance capabilities.
Q1.1: AI Governance Framework
1 - No formal AI governance structure
2 - Basic policies without enforcement mechanisms
3 - Documented governance with defined roles
4 - Comprehensive framework with regular reviews
5 - Adaptive governance with continuous improvement
Q1.2: Regulatory Compliance Management
1 - Limited awareness of AI regulations
2 - Manual tracking of key regulations
3 - Systematic compliance monitoring
4 - Automated compliance validation with controls
5 - Predictive compliance with regulatory influence
Q1.3: Third-Party Risk Management
1 - No vendor risk assessment for AI services
2 - Basic vendor security questionnaires
3 - Comprehensive vendor risk assessments
4 - Continuous vendor monitoring with SLAs
5 - Integrated supply chain risk intelligence
Q1.4: Audit & Assurance Capabilities
1 - No AI-specific audit procedures
2 - Ad-hoc reviews of AI systems
3 - Structured AI audit program
4 - Continuous assurance with automated testing
5 - AI-powered audit with predictive controls
Q1.5: Board Oversight & Reporting
1 - No board visibility into AI risks
2 - Annual AI risk updates to board
3 - Quarterly risk reporting with metrics
4 - Real-time risk dashboards for executives
5 - Board AI committee with external experts
Category 2: Operational Risk Management
Evaluates capabilities for managing technical, operational, and business continuity risks in AI systems.
Q2.1: Model Risk Management
1 - No formal model risk framework
2 - Basic model documentation and testing
3 - Structured model validation process
4 - Comprehensive model lifecycle management
5 - Automated model risk monitoring with drift detection
Q2.2: Data Quality & Integrity Controls
1 - No data quality controls for AI
2 - Manual data validation processes
3 - Automated data quality monitoring
4 - Real-time data anomaly detection
5 - AI-driven data quality optimization
Q2.3: System Reliability & Resilience
1 - No AI-specific reliability measures
2 - Basic uptime monitoring
3 - Comprehensive SLA management
4 - Chaos engineering for AI systems
5 - Self-healing systems with predictive maintenance
Q2.4: Incident Response Readiness
1 - No AI incident response procedures
2 - Generic IT incident processes
3 - AI-specific incident playbooks
4 - Automated incident detection and response
5 - AI-powered incident prediction and prevention
Q2.5: Business Continuity Planning
1 - AI systems not included in BCP
2 - Basic disaster recovery for AI
3 - Comprehensive AI continuity plans
4 - Active-active failover with testing
5 - Autonomous recovery with zero downtime
Category 3: Ethical & Social Considerations
Measures capabilities for managing ethical risks, bias, fairness, and societal impact of AI systems.
Q3.1: Bias Detection & Mitigation
1 - No bias testing or awareness
2 - Ad-hoc bias reviews for some models
3 - Systematic bias testing protocols
4 - Automated bias detection with remediation
5 - Continuous fairness optimization with guarantees
Q3.2: Transparency & Explainability
1 - Black box AI with no explainability
2 - Limited documentation of AI decisions
3 - Explainability tools for key models
4 - Comprehensive explanation frameworks
5 - Real-time interpretability for all stakeholders
Q3.3: Privacy Protection Measures
1 - Basic data protection only
2 - GDPR compliance for AI systems
3 - Privacy-by-design implementation
4 - Advanced privacy-preserving techniques
5 - Federated learning with differential privacy
Q3.4: Human Oversight & Control
1 - Fully autonomous AI without oversight
2 - Minimal human review of AI decisions
3 - Human-in-the-loop for critical decisions
4 - Graduated autonomy with clear boundaries
5 - Adaptive human-AI collaboration frameworks
Q3.5: Societal Impact Assessment
1 - No consideration of broader impacts
2 - Basic stakeholder impact analysis
3 - Comprehensive impact assessments
4 - Proactive stakeholder engagement programs
5 - Continuous impact monitoring with public reporting
AI Risk Heat Map
Visual representation of risk severity across different categories. Focus mitigation efforts on critical and high-risk areas.
Risk Category
Very Low
Low
Medium
High
Critical
Model Performance
Current
Data Privacy
Current
Regulatory Compliance
Current
Bias & Fairness
Current
Security Threats
Current
Risk Mitigation Strategies
Proven approaches for addressing the most common AI risks based on industry best practices.
Technical Risk Mitigation
Model Risk
- Implement comprehensive model validation framework
- Deploy continuous drift monitoring
- Establish model versioning and rollback procedures
- Create model performance thresholds and alerts
Data Risk
- Deploy data quality monitoring at ingestion
- Implement data lineage tracking
- Use synthetic data for sensitive use cases
- Establish data retention and deletion policies
Operational Risk Mitigation
System Reliability
- Design for graceful degradation
- Implement circuit breakers and timeouts
- Deploy across multiple availability zones
- Establish comprehensive monitoring and alerting
Incident Response
- Create AI-specific incident playbooks
- Conduct regular tabletop exercises
- Implement automated rollback capabilities
- Establish clear escalation procedures
Compliance Risk Mitigation
Regulatory Compliance
- Map AI systems to regulatory requirements
- Implement automated compliance checks
- Maintain comprehensive audit trails
- Engage with regulators proactively
Third-Party Risk
- Conduct thorough vendor assessments
- Include AI-specific clauses in contracts
- Monitor vendor security posture continuously
- Maintain vendor contingency plans
Ethical Risk Mitigation
Bias Prevention
- Implement bias testing in CI/CD pipeline
- Use diverse training datasets
- Deploy fairness metrics monitoring
- Establish bias remediation procedures
Transparency
- Deploy explainability tools for all models
- Create user-friendly decision explanations
- Publish AI transparency reports
- Enable user control and consent mechanisms
AI Incident Response Readiness Checklist
Ensure your organization is prepared to handle AI-specific incidents effectively.
Regulatory Compliance Status
Track compliance with major AI regulations and frameworks globally.
| Regulation/Framework | Jurisdiction | Applicability | Current Status | Key Requirements |
|---|---|---|---|---|
| EU AI Act | European Union | High-Risk AI Systems | Partial | Risk assessment, transparency, human oversight |
| GDPR (AI aspects) | European Union | All AI processing personal data | Compliant | Privacy by design, data minimization, consent |
| NIST AI RMF | United States | Federal agencies & contractors | Partial | Governance, mapping, measuring, managing risks |
| ISO/IEC 42001 | International | AI management systems | Gap | AI policy, objectives, risk treatment |
| Singapore Model AI Governance | Singapore | All AI deployments | Compliant | Internal governance, risk management, operations |
| Canada AIDA (proposed) | Canada | High-impact AI systems | Partial | Impact assessments, mitigation measures |
Risk Management Improvement Roadmap
Prioritized action plan based on your assessment results.
Immediate Actions
0-30 days
- Establish AI risk register with ownership
- Conduct initial bias assessment of production models
- Update incident response procedures for AI
- Begin regulatory gap analysis
Short-term Initiatives
1-3 months
- Implement model performance monitoring
- Deploy bias detection tools
- Create AI-specific audit procedures
- Develop vendor risk assessment framework
Medium-term Programs
3-6 months
- Achieve ISO/IEC 42001 certification
- Implement automated compliance monitoring
- Deploy explainability framework
- Establish continuous risk assessment process
Long-term Transformation
6-12 months
- Achieve full regulatory compliance globally
- Implement predictive risk analytics
- Deploy AI-powered risk management
- Establish industry-leading risk practices