AI Risk Register
Document Owner: Risk Management Officer
Review Cadence: Monthly
Last Updated: 2025-11-26
Next Review: 2025-12-26
Executive Summary
This risk register identifies, assesses, and provides mitigation strategies for all AI and ML-related risks within Mavaro Systems. All risks are actively monitored with clear ownership, review schedules, and evidence-based controls.
AI and ML are assistive, not authoritative; manual override is required.
Risk Assessment Framework
Risk Rating Methodology
Likelihood Scale:
- Very Low (1): Less than 5% probability
- Low (2): 5-15% probability
- Medium (3): 15-50% probability
- High (4): 50-85% probability
- Very High (5): Greater than 85% probability
Impact Scale:
- Minimal (1): No significant impact on operations
- Minor (2): Minor operational disruption
- Moderate (3): Noticeable operational impact
- Major (4): Significant operational disruption
- Severe (5): Critical operational impact
Risk Score Calculation: Likelihood × Impact
- Low Risk: Score 1-6
- Medium Risk: Score 7-12
- High Risk: Score 13-19
- Critical Risk: Score 20-25
AI Risk Register
1. Human Dependency Risks
1.1 Cognitive Dependency Risk
Risk Description: Users become dependent on AI for cognitive functions, leading to skill atrophy and reduced mental capabilities including memory, problem-solving, learning, and critical thinking.
Current State Assessment:
- Risk Level: High (Score: 15)
- Likelihood: High (4) - Common with AI assistance tools
- Impact: Major (4) - Can significantly impact user capability
Risk Indicators:
- Frequent consultation of AI before attempting any task
- Decreased performance on tasks when AI is unavailable
- Reduced ability to explain reasoning without AI
- Preference for AI-assisted solutions over independent thinking
Mitigation Strategies:
- Cognitive preservation system implementation
- Skill retention tracking and measurement
- Progressive AI assistance reduction
- Mandatory "AI fasting" periods
- Regular cognitive exercise programs
Risk Owner: Chief Technology Officer
Review Cadence: Monthly
Evidence References:
- Cognitive capability assessment reports
- AI usage pattern analysis
- Skill retention tracking data
- Cognitive exercise completion records
Current Controls:
- AI usage monitoring and limits
- Manual skill assessment programs
- Cognitive challenge implementations
- User feedback collection
1.2 Decision Dependency Risk
Risk Description: Users become unable to make decisions without AI input, losing confidence in independent decision-making abilities.
Current State Assessment:
- Risk Level: High (Score: 16)
- Likelihood: High (4) - Natural progression with AI assistance
- Impact: Major (4) - Critical for user autonomy
Mitigation Strategies:
- Progressive decision-making assistance reduction
- Low-stakes decision practice programs
- Decision confidence building exercises
- Manual fallback capabilities for all decisions
- Decision explanation requirements
1.3 Creative Dependency Risk
Risk Description: Users lose creative abilities and original thinking capacity, becoming dependent on AI for creative output.
Current State Assessment:
- Risk Level: Medium (Score: 12)
- Likelihood: Medium (3) - Growing concern with AI creativity tools
- Impact: Major (4) - Impacts human innovation and expression
Mitigation Strategies:
- Original creation requirements with human input
- AI as collaboration partner, not replacement
- Creative challenge programs with human evaluation
- Innovation incentives for human-first solutions
- Safe spaces for human-only creative exploration
2. Model Performance Risks
2.1 Model Drift Risk
Risk Description: AI model performance degrades over time as the statistical properties of the input data change, leading to reduced accuracy and potentially harmful decisions.
Current State Assessment:
- Risk Level: Medium (Score: 12)
- Likelihood: Medium (3) - Expected as data patterns evolve
- Impact: Major (4) - Can lead to incorrect decisions
Mitigation Strategies:
- Continuous model performance monitoring
- Regular retraining with human oversight
- Automated drift detection systems
- Manual override activation when drift detected
- Fallback to previous model versions
Risk Owner: Data Science Lead
Review Cadence: Monthly
Evidence References:
- Model performance monitoring dashboards
- Drift detection system logs
- Monthly drift assessment reports
- Retraining validation records
Current Controls:
- Weekly model performance reviews
- Automated alerting for performance degradation
- Manual review of model outputs
- Version control for model iterations
Evidence of Control Effectiveness:
- Model performance maintenance above 95% accuracy
- Drift incidents detected within 24 hours average
- No production decisions made on degraded models
- Regular retraining schedule maintained
2. Hallucination Risk
Risk Description: AI systems generate plausible but incorrect information, creating false content that may influence human decisions or damage credibility.
Current State Assessment:
- Risk Level: High (Score: 15)
- Likelihood: Medium (3) - Inherent to current AI technology
- Impact: Major (5) - Can cause significant harm if undetected
Mitigation Strategies:
- Source verification requirements for all AI outputs
- Human review mandatory for all AI-generated content
- Cross-validation with known data sources
- Confidence scoring for all AI outputs
- Clear labeling of AI-generated vs verified content
Risk Owner: Content Quality Manager
Review Cadence: Weekly
Evidence References:
- Content verification logs
- Human review process documentation
- Confidence score tracking reports
- Source validation procedure records
Current Controls:
- Manual review of all AI-generated content
- Source requirement for AI outputs
- Confidence threshold enforcement
- Clear AI content labeling
Evidence of Control Effectiveness:
- 100% human review rate for AI content
- Zero unverified AI content in production
- Source verification compliance: 100%
- User feedback on content accuracy: >90%
3. Bias Risk
Risk Description: AI systems produce discriminatory outcomes against certain demographic groups due to biased training data or algorithmic design.
Current State Assessment:
- Risk Level: High (Score: 16)
- Likelihood: High (4) - Common in AI systems
- Impact: Major (4) - Legal, reputational, and ethical concerns
Mitigation Strategies:
- Comprehensive bias testing before deployment
- Regular demographic parity assessments
- Diverse training data requirements
- Fairness auditing and monitoring
- Bias mitigation techniques implementation
Risk Owner: Ethics Officer
Review Cadence: Quarterly
Evidence References:
- Quarterly bias testing reports
- Demographic performance analysis
- Fairness audit results
- Bias mitigation implementation records
Current Controls:
- Pre-deployment bias testing required
- Quarterly demographic performance reviews
- Fairness metrics monitoring
- Diverse training dataset requirements
Evidence of Control Effectiveness:
- Demographic parity ratios maintained within 0.8-1.25 range
- Quarterly bias testing reports completed
- Zero bias-related user complaints
- Fairness audit compliance: 100%
4. Over-Dependency Risk
Risk Description: Users become overly reliant on AI systems, leading to degradation of human skills and inability to function without AI assistance.
Current State Assessment:
- Risk Level: Medium (Score: 10)
- Likelihood: Medium (3) - Gradual dependency development
- Impact: Major (3) - Long-term operational resilience concerns
Mitigation Strategies:
- Regular human skill competency assessments
- Mandatory manual procedure training
- Human override usage monitoring and encouragement
- Skill rotation to prevent dependency
- Manual backup procedure maintenance
Risk Owner: CTO
Review Cadence: Monthly
Evidence References:
- Monthly skill competency assessment reports
- Manual override usage statistics
- Training completion records
- Dependency risk assessment logs
Current Controls:
- Manual skill competency testing
- User training on manual alternatives
- Override accessibility monitoring
- Regular skill assessments
Evidence of Control Effectiveness:
- Manual override usage maintained above 5%
- Competency assessment scores stable or improving
- 100% completion of manual procedure training
- No reported skill degradation incidents
5. Security Vulnerability Risk
Risk Description: AI systems are vulnerable to adversarial attacks, data poisoning, or model exploitation that could compromise system integrity.
Current State Assessment:
- Risk Level: Medium (Score: 9)
- Likelihood: Low (2) - Specialized attacks required
- Impact: Major (5) - Can compromise entire systems
Mitigation Strategies:
- Regular security testing and vulnerability assessments
- Input validation and sanitization
- Adversarial training for robustness
- Access control and monitoring
- Incident response procedures
Risk Owner: Security Team Lead
Review Cadence: Monthly
Evidence References:
- Security testing reports
- Vulnerability assessment results
- Incident response logs
- Security control implementation records
Current Controls:
- Regular security assessments
- Input validation systems
- Access control enforcement
- Security monitoring and alerting
Evidence of Control Effectiveness:
- Monthly security assessments completed
- Zero successful adversarial attacks
- Security incident response time: under 2 hours
- Vulnerability remediation: 100% within SLA
6. Data Privacy Risk
Risk Description: AI systems inadvertently expose sensitive data or fail to comply with data protection regulations during processing.
Current State Assessment:
- Risk Level: High (Score: 14)
- Likelihood: Medium (3) - Complex data handling
- Impact: Major (5) - Legal compliance and reputation
Mitigation Strategies:
- Data minimization practices
- Encryption of sensitive data in AI processing
- Privacy impact assessments for AI systems
- User consent management
- Data retention and deletion controls
Risk Owner: Data Protection Officer
Review Cadence: Monthly
Evidence References:
- Privacy impact assessments
- Data processing audit logs
- Consent management records
- Data retention compliance reports
Current Controls:
- Data classification requirements
- Encryption for sensitive data
- Consent tracking systems
- Data retention policy enforcement
Evidence of Control Effectiveness:
- 100% privacy impact assessment coverage
- Zero data privacy incidents
- Consent compliance rate: 100%
- Data retention policy adherence: 100%
7. Transparency and Explainability Risk
Risk Description: AI systems make decisions that cannot be explained to users, auditors, or regulators, creating accountability and trust issues.
Current State Assessment:
- Risk Level: Medium (Score: 8)
- Likelihood: Medium (3) - Complex AI models
- Impact: Minor (3) - Limited immediate operational impact
Mitigation Strategies:
- Explainable AI model selection and design
- User-friendly explanation interfaces
- Decision rationale documentation
- Regular explainability testing
- User feedback collection and response
Risk Owner: Product Manager
Review Cadence: Quarterly
Evidence References:
- Explainability test results
- User feedback on AI explanations
- Decision rationale documentation
- Explainability audit reports
Current Controls:
- Model explanation requirements
- User-friendly explanation interfaces
- Decision documentation practices
- User feedback monitoring
Evidence of Control Effectiveness:
- AI explanation comprehension rate: >75%
- User satisfaction with explanations: >85%
- 100% decision rationale documentation
- Regular user feedback incorporation
8. Compliance and Regulatory Risk
Risk Description: AI systems fail to comply with evolving AI regulations, industry standards, or internal policies, leading to legal and operational consequences.
Current State Assessment:
- Risk Level: Medium (Score: 12)
- Likelihood: Medium (3) - Regulatory landscape evolving
- Impact: Major (4) - Can result in significant penalties
Mitigation Strategies:
- Regular regulatory compliance monitoring
- AI governance framework implementation
- Legal and compliance review processes
- Industry standard alignment
- Regular compliance audits
Risk Owner: Legal Counsel
Review Cadence: Quarterly
Evidence References:
- Compliance audit reports
- Regulatory monitoring updates
- Legal review documentation
- Industry standard alignment assessments
Current Controls:
- Legal review for AI deployments
- Compliance monitoring processes
- Industry standard alignment
- Regular policy updates
Evidence of Control Effectiveness:
- Zero compliance violations
- 100% legal review completion
- Regular compliance audits conducted
- Proactive regulatory monitoring
Risk Monitoring and Reporting
Monthly Risk Dashboard
Key Risk Indicators:
- High/Critical risk count and trending
- Risk mitigation effectiveness scores
- Incident frequency and severity
- Control effectiveness metrics
Risk Reporting Schedule:
- Weekly risk status updates to leadership
- Monthly comprehensive risk reports
- Quarterly risk assessment reviews
- Annual risk strategy reassessment
Incident Response
Risk Event Response:
- Immediate risk owner notification
- Impact assessment and escalation
- Incident documentation and logging
- Response plan activation
- Post-incident review and lessons learned
Evidence Collection Requirements:
- Incident timeline documentation
- Response effectiveness analysis
- Control failure identification
- Improvement action planning
Continuous Improvement
Risk Register Maintenance
Regular Updates:
- Monthly risk score recalculation
- Quarterly risk landscape assessment
- Annual risk register comprehensive review
- New risk identification and assessment
Improvement Process:
- Risk mitigation effectiveness evaluation
- Control enhancement identification
- Process improvement implementation
- Training and awareness updates
Conclusion
This AI Risk Register provides comprehensive coverage of AI-related risks within Mavaro Systems. Each risk is actively monitored with clear ownership and evidence-based controls, ensuring responsible AI deployment while maintaining user safety and organizational resilience.
AI and ML are assistive, not authoritative; manual override is required.
Document Control:
- Version: 1.0
- Effective Date: 2025-11-26
- Supersedes: N/A (New Document)
- Next Review: 2025-12-26
- Owner Approval: Pending
- Security Review: Pending