1.1 Model Risk (Performance & Accuracy)

• Model bias (systematic errors against certain groups)
• Performance degradation (accuracy drops over time)
• Adversarial attacks (bad actors manipulate model)
• Black swan events (unexpected failures)
• Model interpretability challenges

1.2 Data Risk

• Data quality issues (incomplete, inaccurate, unrepresentative)
• Data bias (historical discrimination in training data)
• Data privacy violations (GDPR/CCPA non-compliance)
• Data breaches and security
• Data provenance and lineage issues

1.3 Operational Risk

• Model monitoring failures (problems not detected)
• Inadequate documentation (insufficient audit trail)
• Insufficient testing before deployment
• Human error in implementation
• System outages and availability issues

1.4 Compliance & Regulatory Risk

• Non-compliance with AI regulations (EU AI Act, sectoral)
• Regulatory penalties and fines (up to 6% of revenue)
• Litigation and discrimination lawsuits
• Audit failures and inability to demonstrate compliance
• Regulatory inquiries and investigations

1.5 Fairness & Discrimination Risk

• Disparate impact (unequal rejection rates by group)
• Disparate treatment (treating similar people differently)
• Proxy discrimination (using proxy variables for protected characteristics)
• Systemic bias (perpetuating historical discrimination)
• Legal exposure and remediation

1.6 Security Risk

• Model poisoning (malicious data in training)
• Evasion attacks (manipulating inputs to fool model)
• Model stealing (competitors steal proprietary model)
• Data leakage and reverse-engineering
• Unauthorized access and insider threats

1.7 Reputational Risk

• Media coverage of AI failures
• Customer backlash and lost trust
• Talent retention issues (ethical concerns)
• Brand damage and market positioning
• Stakeholder confidence erosion

1.8 Strategic Risk

• Over-investment in AI (poor ROI)
• Under-investment (competitor advantage)
• Technology lock-in and vendor dependency
• Organizational disruption from rapid changes
• Misalignment with business strategy

2.1 Risk Assessment Methodology

• Risk identification (brainstorming, industry lessons, regulatory review)

• Stakeholder involvement (diverse perspectives)

• Comprehensive risk inventory development

2.2 Risk Characterization & Measurement

• Likelihood assessment (Low, Medium, High)

• Impact assessment (Low, Medium, High severity)

• Risk rating calculation (Likelihood × Impact)

• Risk thresholds and materiality

2.3 Risk Prioritization & Matrix Analysis

• Risk Matrix construction (Likelihood vs. Impact)

• Critical risk identification

• Risk ranking and sequencing

• Management by exception approach

2.4 Risk Dashboards & Key Risk Indicators (KRIs)

• AI Risk Dashboard design

• KRI selection and thresholds

• Real-time monitoring capabilities

• Alert and escalation procedures

• Executive reporting and communication

2.5 Continuous Risk Monitoring

• Quarterly risk reviews

• Annual comprehensive risk assessment

• Risk trend analysis

• Emerging risk identification

• Risk remediation tracking

3.1 Model Development Validation

• Test design and planning

• Baseline performance establishment

• Testing protocols and procedures

• Model documentation and record-keeping

3.2 Deployment Validation

• Pre-deployment comprehensive testing

• Documentation review and completeness

• Governance approval processes

• Deployment planning and execution

3.3 Post-Deployment Monitoring Framework

• Performance metrics tracking

• Fairness metrics monitoring

• Automated alert configuration

• Incident logging and escalation

• Regular governance reviews

3.4 Model Drift Detection & Management

• Data distribution shift monitoring

• Model performance degradation detection

• Retraining triggers and schedules

• Fairness re-assessment procedures

• Version control and audit trails

3.5 Model Retirement & Decommissioning

• Retirement decision documentation

• Migration planning and execution

• Data and model archival

• Post-mortem analysis

• Lessons learned capture

3.6 Validation Controls by Risk Tier

• Low-risk model controls (basic monitoring)

• Medium-risk model controls (performance + fairness testing)

• High-risk model controls (extensive validation, human review)

• Critical-risk model controls (expert review, board oversight)

4.1 Fairness Metrics & Assessment

• Demographic parity assessment

• Equalized odds evaluation

• Calibration analysis

• Individual fairness assessment

• Threshold effects and decision boundaries

4.2 Bias Identification & Sources

• Historical bias (past discrimination in data)

• Representational bias (underrepresented groups)

• Statistical discrimination (proxy variable usage)

• Aggregation bias (one model for diverse populations)

• Measurement bias (how success is defined)

4.3 Bias Testing & Audit Procedures

• Fairness audit planning and scope

• Demographic group definition

• Statistical testing procedures

• Bias quantification and reporting

• Root cause analysis

4.4 Bias Remediation Strategies

• Pre-training mitigation (data collection and balancing)

• In-training mitigation (fairness constraints during training)

• Post-training mitigation (threshold adjustment, human review)

• Ongoing monitoring (continuous fairness tracking)

• Remediation effectiveness measurement

4.5 Discrimination Risk Monitoring

• Outcome disparities tracking (approval rates, rejection rates)

• Group performance monitoring

• Trend analysis (is bias getting better or worse?)

• Threshold-based alerts (when to escalate)

• Remediation tracking and effectiveness

4.6 Legal & Compliance Alignment

• Regulatory fairness requirements (EU AI Act, EEOC, FCRA)

• Litigation prevention (discrimination lawsuit risk)

• Documentation for legal defense

• Third-party fairness audits

• Regulatory inquiry preparation

5.1 Privacy Regulation Compliance

• GDPR requirements (access, deletion, explanation rights)

• CCPA compliance (know, delete, opt-out rights)

• Other regional regulations (UK ICO, Brazil LGPD, China)

• Sectoral privacy requirements (HIPAA, FERPA, financial)

• Privacy policy and consent frameworks

5.2 Data Security for AI Systems

• Data encryption (in transit and at rest)

• Access controls (who can access AI data/models?)

• Data minimization (collect only necessary data)

• Secure data handling procedures

• Data breach response protocols

5.3 AI-Specific Data Risks

• Training data security (protect against poisoning)

• Model security (prevent stealing/reverse-engineering)

• Inference data security (protect predictions)

• Model privacy (prevent training data extraction)

• Adversarial robustness

5.4 Privacy-Preserving AI Techniques

• Differential privacy implementation

• Federated learning approaches

• Data anonymization and de-identification

• Synthetic data generation

• Privacy-utility tradeoffs

5.5 Data Governance for Privacy

• Data inventory and categorization

• Data classification (by sensitivity)

• Data retention policies

• Data destruction procedures

• Third-party data handling

5.6 Privacy Impact Assessment

• Privacy risk identification

• Privacy risk quantification

• Mitigation planning

• Ongoing monitoring

• Incident response procedures

6.1 Regulatory Compliance Framework

• Applicable regulations identification

• Compliance requirements mapping

• Compliance gap analysis

• Remediation planning

• Compliance monitoring

6.2 EU AI Act Compliance

• Risk tier classification (prohibited, high-risk, limited-risk, minimal)

• High-risk AI system compliance requirements

• Documentation and testing obligations

• Conformity assessment procedures

• CE marking and notified bodies

6.3 Sectoral Regulatory Compliance

• Financial services regulation (model risk management)

• Healthcare regulation (FDA, clinical validation)

• Employment regulation (hiring AI fairness)

• Government regulation (AI procurement requirements)

• Data protection (GDPR, CCPA, sectoral)

6.4 Internal Audit Procedures

• AI system audit planning and scoping

• Audit procedures and testing

• Documentation review

• Compliance findings identification

• Remediation tracking

6.5 External Audit & Third-Party Review

• Independent fairness audits

• Security audits and penetration testing

• Regulatory audits and inspections

• Third-party certifications (ISO 42001)

• Audit report management

6.6 Regulatory Engagement & Response

• Regulatory inquiry handling procedures

• Information gathering and documentation

• Response communication

• Remediation and corrective actions

• Ongoing regulator relationships

6.7 Governance Integration

• AI risk integration into enterprise risk management

• Board reporting on AI risks

• Risk committee oversight

• Compliance committee coordination

• Internal audit plans for AI