---

name: ai-governance description: AI governance and compliance guidance covering EU AI Act risk classification, NIST AI RMF, responsible AI principles, AI ethics review, and regulatory compliance for AI systems. allowed-tools: Read, Glob, Grep, Task

AI Governance

Comprehensive guidance for AI governance, regulatory compliance, and responsible AI practices, including EU AI Act and NIST AI Risk Management Framework.

When to Use This Skill

• Classifying AI systems under EU AI Act risk categories
• Conducting AI risk assessments using NIST AI RMF
• Implementing responsible AI principles
• Preparing for AI compliance audits
• Creating AI system documentation and model cards
• Establishing AI governance frameworks
• Conducting AI ethics reviews

Quick Reference

EU AI Act Risk Classification

Risk Level	Description	Examples	Requirements
Unacceptable	Prohibited practices	Social scoring, subliminal manipulation, exploitation of vulnerabilities	Banned outright
High-Risk	Safety/rights impact	Employment AI, credit scoring, biometric ID, critical infrastructure	Strict compliance
Limited Risk	Transparency needed	Chatbots, emotion recognition, deepfakes	Disclosure required
Minimal Risk	Low/no regulation	Spam filters, game AI, recommendation systems	Voluntary codes

NIST AI RMF Functions

Function	Purpose	Key Activities
Govern	Cultivate risk culture	Policies, accountability, governance structures
Map	Understand context	Stakeholders, impacts, constraints, requirements
Measure	Assess and track	Risk metrics, testing, monitoring, evaluation
Manage	Prioritize and act	Mitigations, responses, documentation

Responsible AI Principles

Principle	Description	Implementation
Fairness	Equitable treatment, bias mitigation	Fairness metrics, bias testing, diverse data
Transparency	Explainable decisions	XAI methods, model cards, documentation
Accountability	Clear ownership and oversight	Governance roles, audit trails, escalation
Privacy	Data protection, consent	PII handling, anonymization, consent management
Safety	Reliable, secure operation	Testing, monitoring, incident response
Human Oversight	Meaningful human control	HITL design, override mechanisms, review

EU AI Act Compliance

Prohibited AI Practices (Article 5)

prohibited_practices:
  social_scoring:
    description: "General-purpose social credit systems"
    applies_to: "Public authorities scoring citizens"
    prohibition: "Absolute - no exceptions"

  subliminal_manipulation:
    description: "AI exploiting subconscious to cause harm"
    applies_to: "Systems using techniques beyond awareness"
    prohibition: "Absolute - no exceptions"

  vulnerability_exploitation:
    description: "AI exploiting age, disability, social situation"
    applies_to: "Systems targeting vulnerable groups"
    prohibition: "Absolute - no exceptions"

  real_time_biometric_identification:
    description: "Remote biometric ID in public spaces"
    applies_to: "Law enforcement use"
    exceptions:
      - "Search for missing children"
      - "Prevention of terrorist attack"
      - "Identification of criminal suspects"
    authorization: "Prior judicial or administrative approval required"

  emotion_inference_workplace:
    description: "Emotion recognition in workplace/education"
    applies_to: "Employee/student monitoring"
    exceptions:
      - "Medical or safety purposes"

  predictive_policing:
    description: "Individual crime risk based solely on profiling"
    applies_to: "Law enforcement prediction"
    prohibition: "Absolute when based solely on profiling/traits"

  facial_recognition_scraping:
    description: "Untargeted facial image collection"
    applies_to: "Databases built from internet/CCTV scraping"
    prohibition: "Absolute - no exceptions"

High-Risk AI Classification (Annex III)

high_risk_categories:
  biometrics:
    - "Remote biometric identification systems"
    - "Biometric categorization (race, political, religion)"
    - "Emotion recognition systems"

  critical_infrastructure:
    - "Safety components in road traffic"
    - "Water, gas, heating, electricity management"
    - "Digital infrastructure safety components"

  education_training:
    - "Educational/vocational access decisions"
    - "Exam evaluation (learning outcomes)"
    - "Behavior assessment in institutions"

  employment:
    - "Recruitment and candidate filtering"
    - "Job advertisement targeting"
    - "Application evaluation"
    - "Promotion/termination decisions"
    - "Task allocation based on behavior/traits"
    - "Performance monitoring"

  essential_services:
    - "Credit scoring and creditworthiness"
    - "Risk assessment in life/health insurance"
    - "Emergency services dispatch prioritization"

  law_enforcement:
    - "Individual risk assessment (re-offending)"
    - "Polygraph and similar tools"
    - "Evidence reliability assessment"
    - "Crime prediction for individuals/groups"
    - "Profiling during investigations"

  migration_asylum:
    - "Polygraphs and similar at borders"
    - "Risk assessment (security, health, irregular entry)"
    - "Verification of travel document authenticity"
    - "Asylum/visa/residence application processing"

  justice_democracy:
    - "AI assisting judicial research/interpretation"
    - "AI assisting application of law to facts"
    - "Alternative dispute resolution"
    - "Election/referendum influence"

High-Risk AI Requirements

namespace Security.AIGovernance;

/// <summary>
/// EU AI Act high-risk AI system requirements.
/// </summary>
public static class HighRiskRequirements
{
    /// <summary>
    /// Risk management system requirements (Article 9).
    /// </summary>
    public static readonly RiskManagementRequirements RiskManagement = new(
        ContinuousProcess: true,
        IdentifyKnownRisks: true,
        EstimateRiskLevels: true,
        EvaluateEmergingRisks: true,
        AdoptMitigations: true,
        DocumentDecisions: true,
        TestingRequirements: [
            "Testing against defined metrics",
            "Testing with representative data",
            "Testing for foreseeable misuse",
            "Testing by independent parties where appropriate"
        ]
    );

    /// <summary>
    /// Data and data governance requirements (Article 10).
    /// </summary>
    public static readonly DataGovernanceRequirements DataGovernance = new(
        TrainingDataDocumentation: true,
        DataQualityManagement: true,
        BiasExamination: true,
        RelevanceVerification: true,
        RepresentativenessCheck: true,
        SpecialCategoryDataHandling: [
            "Strictly necessary for bias detection",
            "Subject to appropriate safeguards",
            "Not used for other purposes"
        ]
    );

    /// <summary>
    /// Technical documentation requirements (Article 11).
    /// </summary>
    public static readonly TechnicalDocumentationRequirements Documentation = new(
        GeneralDescription: true,
        IntendedPurpose: true,
        DesignSpecifications: true,
        SystemArchitecture: true,
        DataRequirements: true,
        TrainingMethodologies: true,
        ValidationProcedures: true,
        PerformanceMetrics: true,
        RiskManagementSystem: true,
        Cybersecurity: true,
        ModificationLog: true
    );

    /// <summary>
    /// Record-keeping requirements (Article 12).
    /// </summary>
    public static readonly RecordKeepingRequirements RecordKeeping = new(
        AutomaticLogging: true,
        OperationalLogs: true,
        IdentityOfUsers: true,
        DateTimeOfUse: true,
        ReferenceInputData: true,
        OutputData: true,
        RetentionPeriod: "Appropriate to intended purpose"
    );

    /// <summary>
    /// Transparency requirements (Article 13).
    /// </summary>
    public static readonly TransparencyRequirements Transparency = new(
        ClearInstructions: true,
        ProviderIdentity: true,
        SystemCapabilities: true,
        SystemLimitations: true,
        AccuracyLevels: true,
        ForeseeableRisks: true,
        HumanOversightMeasures: true,
        MaintenanceRequirements: true
    );

    /// <summary>
    /// Human oversight requirements (Article 14).
    /// </summary>
    public static readonly HumanOversightRequirements HumanOversight = new(
        DesignedForOversight: true,
        OperatorTools: [
            "Understand system capabilities and limitations",
            "Monitor operation correctly",
            "Detect automation bias",
            "Interpret outputs correctly",
            "Override or interrupt system",
            "Decide not to use or disregard output"
        ],
        Proportionate: "To risks and autonomy level"
    );
}

public sealed record RiskManagementRequirements(
    bool ContinuousProcess,
    bool IdentifyKnownRisks,
    bool EstimateRiskLevels,
    bool EvaluateEmergingRisks,
    bool AdoptMitigations,
    bool DocumentDecisions,
    string[] TestingRequirements);

public sealed record DataGovernanceRequirements(
    bool TrainingDataDocumentation,
    bool DataQualityManagement,
    bool BiasExamination,
    bool RelevanceVerification,
    bool RepresentativenessCheck,
    string[] SpecialCategoryDataHandling);

public sealed record TechnicalDocumentationRequirements(
    bool GeneralDescription,
    bool IntendedPurpose,
    bool DesignSpecifications,
    bool SystemArchitecture,
    bool DataRequirements,
    bool TrainingMethodologies,
    bool ValidationProcedures,
    bool PerformanceMetrics,
    bool RiskManagementSystem,
    bool Cybersecurity,
    bool ModificationLog);

public sealed record RecordKeepingRequirements(
    bool AutomaticLogging,
    bool OperationalLogs,
    bool IdentityOfUsers,
    bool DateTimeOfUse,
    bool ReferenceInputData,
    bool OutputData,
    string RetentionPeriod);

public sealed record TransparencyRequirements(
    bool ClearInstructions,
    bool ProviderIdentity,
    bool SystemCapabilities,
    bool SystemLimitations,
    bool AccuracyLevels,
    bool ForeseeableRisks,
    bool HumanOversightMeasures,
    bool MaintenanceRequirements);

public sealed record HumanOversightRequirements(
    bool DesignedForOversight,
    string[] OperatorTools,
    string Proportionate);

NIST AI Risk Management Framework

Govern Function

govern_function:
  description: "Cultivate a culture of risk management"

  govern_1:
    name: "Policies and Procedures"
    activities:
      - "Establish AI governance policies"
      - "Define AI risk tolerances"
      - "Create AI development standards"
      - "Document ethical guidelines"
    outputs:
      - "AI governance policy"
      - "Risk appetite statement"
      - "Development standards"

  govern_2:
    name: "Accountability Structures"
    activities:
      - "Define AI ownership roles"
      - "Establish oversight committees"
      - "Create escalation paths"
      - "Assign compliance responsibilities"
    outputs:
      - "RACI matrix for AI systems"
      - "Governance org chart"
      - "Escalation procedures"

  govern_3:
    name: "Workforce Diversity"
    activities:
      - "Diverse team composition"
      - "Inclusive development practices"
      - "Bias awareness training"
      - "Cross-functional collaboration"
    outputs:
      - "Diversity metrics"
      - "Training records"
      - "Team composition reports"

  govern_4:
    name: "Organizational Culture"
    activities:
      - "Promote responsible AI values"
      - "Encourage ethical considerations"
      - "Support risk identification"
      - "Foster transparency"
    outputs:
      - "Culture assessment results"
      - "Ethics training completion"
      - "Feedback mechanisms"

  govern_5:
    name: "Stakeholder Engagement"
    activities:
      - "Identify affected stakeholders"
      - "Establish feedback channels"
      - "Incorporate stakeholder input"
      - "Communicate AI decisions"
    outputs:
      - "Stakeholder registry"
      - "Engagement records"
      - "Communication plan"

  govern_6:
    name: "Legal Compliance"
    activities:
      - "Map regulatory requirements"
      - "Monitor regulatory changes"
      - "Ensure compliance verification"
      - "Maintain audit readiness"
    outputs:
      - "Compliance matrix"
      - "Regulatory tracker"
      - "Audit schedules"

Map Function

map_function:
  description: "Understand the context and impacts"

  map_1:
    name: "Intended Purpose"
    activities:
      - "Document business objectives"
      - "Define use case boundaries"
      - "Identify target users"
      - "Specify deployment context"
    outputs:
      - "Use case specification"
      - "User personas"
      - "Deployment plan"

  map_2:
    name: "Categorization"
    activities:
      - "Classify AI system type"
      - "Determine risk category"
      - "Identify regulatory applicability"
      - "Assess criticality level"
    outputs:
      - "Risk classification"
      - "Regulatory mapping"
      - "Criticality assessment"

  map_3:
    name: "Impacts and Affected Parties"
    activities:
      - "Identify potential harms"
      - "Map affected populations"
      - "Assess differential impacts"
      - "Consider cumulative effects"
    outputs:
      - "Impact assessment"
      - "Affected party analysis"
      - "Equity considerations"

  map_4:
    name: "Dependencies"
    activities:
      - "Document data sources"
      - "Identify third-party components"
      - "Map system integrations"
      - "Assess supply chain risks"
    outputs:
      - "Dependency inventory"
      - "Third-party risk assessment"
      - "Integration diagram"

  map_5:
    name: "Risk Identification"
    activities:
      - "Enumerate potential risks"
      - "Consider failure modes"
      - "Assess adversarial threats"
      - "Evaluate misuse potential"
    outputs:
      - "Risk register"
      - "Threat model"
      - "Misuse scenarios"

Measure Function

measure_function:
  description: "Assess and track risks"

  measure_1:
    name: "Risk Metrics"
    activities:
      - "Define risk indicators"
      - "Establish measurement methods"
      - "Set thresholds and tolerances"
      - "Create monitoring dashboards"
    outputs:
      - "KRI definitions"
      - "Measurement protocols"
      - "Threshold documentation"

  measure_2:
    name: "Testing and Evaluation"
    activities:
      - "Conduct bias testing"
      - "Evaluate model performance"
      - "Test edge cases"
      - "Assess robustness"
    outputs:
      - "Test results"
      - "Performance metrics"
      - "Robustness report"

  measure_3:
    name: "Continuous Monitoring"
    activities:
      - "Monitor model drift"
      - "Track performance degradation"
      - "Detect anomalies"
      - "Log incidents"
    outputs:
      - "Monitoring reports"
      - "Drift analysis"
      - "Incident logs"

  measure_4:
    name: "Independent Assessment"
    activities:
      - "Conduct internal audits"
      - "Engage external reviewers"
      - "Facilitate red teaming"
      - "Perform algorithmic audits"
    outputs:
      - "Audit reports"
      - "External review findings"
      - "Red team results"

Manage Function

manage_function:
  description: "Prioritize and respond to risks"

  manage_1:
    name: "Risk Prioritization"
    activities:
      - "Rank risks by severity"
      - "Assess likelihood and impact"
      - "Prioritize mitigation efforts"
      - "Allocate resources"
    outputs:
      - "Prioritized risk register"
      - "Resource allocation plan"
      - "Mitigation roadmap"

  manage_2:
    name: "Risk Response"
    activities:
      - "Implement mitigations"
      - "Develop contingency plans"
      - "Create rollback procedures"
      - "Document decisions"
    outputs:
      - "Mitigation implementations"
      - "Contingency plans"
      - "Rollback procedures"

  manage_3:
    name: "Residual Risk"
    activities:
      - "Assess remaining risks"
      - "Obtain risk acceptance"
      - "Document limitations"
      - "Communicate constraints"
    outputs:
      - "Residual risk assessment"
      - "Risk acceptance records"
      - "Limitation documentation"

  manage_4:
    name: "Documentation and Communication"
    activities:
      - "Maintain risk documentation"
      - "Report to stakeholders"
      - "Share lessons learned"
      - "Update governance artifacts"
    outputs:
      - "Risk documentation"
      - "Stakeholder reports"
      - "Lessons learned"

AI Risk Assessment

Risk Classification Model

namespace Security.AIGovernance;

/// <summary>
/// AI system risk classification and assessment.
/// </summary>
public sealed class AIRiskAssessment
{
    /// <summary>
    /// Classify AI system risk level based on characteristics.
    /// </summary>
    public static RiskClassification ClassifyRisk(AISystemCharacteristics system)
    {
        // Check for prohibited practices first
        if (IsProhibited(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Unacceptable,
                Reasoning: "System falls under EU AI Act prohibited practices",
                Requirements: ["System must not be deployed"],
                ComplianceActions: ["Discontinue development", "Review for alternative approaches"]);
        }

        // Check for high-risk categories
        if (IsHighRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.High,
                Reasoning: "System falls under EU AI Act Annex III high-risk categories",
                Requirements: [
                    "Implement risk management system",
                    "Ensure data governance",
                    "Create technical documentation",
                    "Implement logging and record-keeping",
                    "Ensure transparency to users",
                    "Enable human oversight",
                    "Ensure accuracy, robustness, cybersecurity",
                    "Conduct conformity assessment"
                ],
                ComplianceActions: GetHighRiskActions(system));
        }

        // Check for limited risk (transparency obligations)
        if (IsLimitedRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Limited,
                Reasoning: "System has transparency obligations",
                Requirements: [
                    "Disclose AI interaction to users",
                    "Label AI-generated content where applicable",
                    "Inform about emotion recognition/biometric categorization"
                ],
                ComplianceActions: ["Implement disclosure mechanisms", "Update user interfaces"]);
        }

        // Minimal/no risk
        return new RiskClassification(
            Level: RiskLevel.Minimal,
            Reasoning: "System does not fall under regulated categories",
            Requirements: ["Consider voluntary codes of conduct"],
            ComplianceActions: ["Document risk assessment decision", "Monitor for regulatory changes"]);
    }

    private static bool IsProhibited(AISystemCharacteristics system)
    {
        return system.UseCase switch
        {
            AIUseCase.SocialScoring => system.DeployedBy == DeploymentContext.PublicAuthority,
            AIUseCase.SubliminalManipulation => true,
            AIUseCase.VulnerabilityExploitation => true,
            AIUseCase.FacialRecognitionScraping => true,
            AIUseCase.PredictivePolicing => system.BasedSolelyOnProfiling,
            AIUseCase.EmotionRecognition => system.Context is DeploymentContext.Workplace or DeploymentContext.Education
                                            && !system.ForMedicalOrSafetyPurposes,
            _ => false
        };
    }

    private static bool IsHighRisk(AISystemCharacteristics system)
    {
        return system.Category is
            AICategory.Biometrics or
            AICategory.CriticalInfrastructure or
            AICategory.Education or
            AICategory.Employment or
            AICategory.EssentialServices or
            AICategory.LawEnforcement or
            AICategory.MigrationAsylum or
            AICategory.JusticeDemocracy;
    }

    private static bool IsLimitedRisk(AISystemCharacteristics system)
    {
        return system.UseCase is
            AIUseCase.Chatbot or
            AIUseCase.EmotionRecognition or
            AIUseCase.DeepfakeGeneration or
            AIUseCase.ContentGeneration;
    }

    private static string[] GetHighRiskActions(AISystemCharacteristics system)
    {
        var actions = new List<string>
        {
            "Establish risk management system",
            "Document training data governance",
            "Create technical documentation per Annex IV",
            "Implement automatic logging",
            "Create instructions for use",
            "Design for human oversight"
        };

        if (system.Category == AICategory.Biometrics)
        {
            actions.Add("Conduct fundamental rights impact assessment");
            actions.Add("Register in EU AI database");
        }

        return [.. actions];
    }
}

public sealed record AISystemCharacteristics(
    AICategory Category,
    AIUseCase UseCase,
    DeploymentContext Context,
    DeploymentContext? DeployedBy = null,
    bool BasedSolelyOnProfiling = false,
    bool ForMedicalOrSafetyPurposes = false);

public sealed record RiskClassification(
    RiskLevel Level,
    string Reasoning,
    string[] Requirements,
    string[] ComplianceActions);

public enum RiskLevel { Minimal, Limited, High, Unacceptable }

public enum AICategory
{
    Biometrics,
    CriticalInfrastructure,
    Education,
    Employment,
    EssentialServices,
    LawEnforcement,
    MigrationAsylum,
    JusticeDemocracy,
    General
}

public enum AIUseCase
{
    SocialScoring,
    SubliminalManipulation,
    VulnerabilityExploitation,
    FacialRecognitionScraping,
    PredictivePolicing,
    EmotionRecognition,
    BiometricIdentification,
    CreditScoring,
    RecruitmentScreening,
    PerformanceMonitoring,
    Chatbot,
    DeepfakeGeneration,
    ContentGeneration,
    RecommendationSystem,
    GameAI,
    SpamFilter,
    Other
}

public enum DeploymentContext
{
    PublicAuthority,
    PrivateSector,
    Workplace,
    Education,
    Healthcare,
    LawEnforcement,
    General
}

Model Cards and Documentation

Model Card Template

model_card_template:
  model_details:
    name: ""
    version: ""
    type: "" # Classification, regression, generation, etc.
    developer: ""
    license: ""
    release_date: ""

  intended_use:
    primary_use_cases: []
    intended_users: []
    out_of_scope_uses: []

  factors:
    relevant_factors: []
    evaluation_factors: []

  metrics:
    performance_measures: []
    decision_thresholds: []
    variation_approaches: []

  evaluation_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  training_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  quantitative_analyses:
    unitary_results: []
    intersectional_results: []

  ethical_considerations:
    sensitive_use_cases: []
    known_limitations: []
    bias_mitigations: []

  caveats_recommendations:
    known_issues: []
    recommendations: []
    additional_testing: []

AI System Documentation

namespace Security.AIGovernance;

/// <summary>
/// AI system documentation for compliance and transparency.
/// </summary>
public sealed record AISystemDocumentation
{
    // General Description
    public required string SystemName { get; init; }
    public required string Version { get; init; }
    public required string Description { get; init; }
    public required string IntendedPurpose { get; init; }
    public required RiskLevel RiskClassification { get; init; }
    public required DateTimeOffset DocumentDate { get; init; }

    // Provider Information
    public required OrganizationInfo Provider { get; init; }
    public required ContactInfo TechnicalContact { get; init; }
    public required ContactInfo ComplianceContact { get; init; }

    // Technical Specifications
    public required SystemArchitecture Architecture { get; init; }
    public required ModelSpecification Model { get; init; }
    public required DataSpecification TrainingData { get; init; }
    public required PerformanceMetrics Performance { get; init; }

    // Risk Management
    public required RiskAssessment Risks { get; init; }
    public required MitigationMeasures Mitigations { get; init; }
    public required HumanOversightDesign HumanOversight { get; init; }

    // Compliance
    public required ComplianceStatus Compliance { get; init; }
    public required List<AuditRecord> AuditHistory { get; init; }
    public required List<string> ApplicableRegulations { get; init; }
}

public sealed record OrganizationInfo(
    string Name,
    string Address,
    string Country,
    string RegistrationNumber);

public sealed record ContactInfo(
    string Name,
    string Email,
    string Phone);

public sealed record SystemArchitecture(
    string Description,
    List<string> Components,
    List<string> ExternalDependencies,
    List<string> IntegrationPoints);

public sealed record ModelSpecification(
    string ModelType,
    string Algorithm,
    string Framework,
    string TrainingApproach,
    DateTimeOffset LastTrainingDate);

public sealed record DataSpecification(
    string DataSources,
    long RecordCount,
    string DataTypes,
    string QualityMeasures,
    string BiasAssessment,
    bool ContainsSensitiveData,
    string SensitiveDataHandling);

public sealed record PerformanceMetrics(
    Dictionary<string, double> Metrics,
    string EvaluationMethodology,
    string LimitationsAndFailureModes);

public sealed record RiskAssessment(
    List<IdentifiedRisk> Risks,
    string OverallRiskLevel,
    DateTimeOffset AssessmentDate);

public sealed record IdentifiedRisk(
    string Description,
    string Likelihood,
    string Impact,
    string MitigationStatus);

public sealed record MitigationMeasures(
    List<string> TechnicalMeasures,
    List<string> OrganizationalMeasures,
    List<string> MonitoringMeasures);

public sealed record HumanOversightDesign(
    string OversightModel, // Human-in-the-loop, human-on-the-loop, human-in-command
    List<string> OversightMechanisms,
    List<string> OverrideCapabilities,
    string TrainingRequirements);

public sealed record ComplianceStatus(
    bool EUAIActCompliant,
    string ConformityAssessmentStatus,
    string CertificationStatus,
    DateTimeOffset LastComplianceReview);

public sealed record AuditRecord(
    DateTimeOffset Date,
    string AuditType,
    string Auditor,
    string Findings,
    string CorrectiveActions);

Compliance Checklists

EU AI Act High-Risk Compliance Checklist

eu_ai_act_high_risk_checklist:
  risk_management:
    - task: "Establish risk management system"
      status: "pending"
      evidence: ""
    - task: "Document known and foreseeable risks"
      status: "pending"
      evidence: ""
    - task: "Implement risk mitigation measures"
      status: "pending"
      evidence: ""
    - task: "Conduct testing for risk assessment"
      status: "pending"
      evidence: ""

  data_governance:
    - task: "Document training data sources"
      status: "pending"
      evidence: ""
    - task: "Implement data quality management"
      status: "pending"
      evidence: ""
    - task: "Conduct bias examination"
      status: "pending"
      evidence: ""
    - task: "Verify data representativeness"
      status: "pending"
      evidence: ""

  technical_documentation:
    - task: "Create Annex IV compliant documentation"
      status: "pending"
      evidence: ""
    - task: "Document system architecture"
      status: "pending"
      evidence: ""
    - task: "Document training methodology"
      status: "pending"
      evidence: ""
    - task: "Document performance metrics"
      status: "pending"
      evidence: ""

  record_keeping:
    - task: "Implement automatic logging"
      status: "pending"
      evidence: ""
    - task: "Log user interactions"
      status: "pending"
      evidence: ""
    - task: "Define retention periods"
      status: "pending"
      evidence: ""

  transparency:
    - task: "Create instructions for use"
      status: "pending"
      evidence: ""
    - task: "Document capabilities and limitations"
      status: "pending"
      evidence: ""
    - task: "Specify accuracy levels"
      status: "pending"
      evidence: ""

  human_oversight:
    - task: "Design oversight mechanisms"
      status: "pending"
      evidence: ""
    - task: "Implement override capabilities"
      status: "pending"
      evidence: ""
    - task: "Define operator training requirements"
      status: "pending"
      evidence: ""

  accuracy_robustness_cybersecurity:
    - task: "Validate performance metrics"
      status: "pending"
      evidence: ""
    - task: "Test for robustness"
      status: "pending"
      evidence: ""
    - task: "Conduct security assessment"
      status: "pending"
      evidence: ""

  conformity_assessment:
    - task: "Complete self-assessment or third-party assessment"
      status: "pending"
      evidence: ""
    - task: "Prepare EU declaration of conformity"
      status: "pending"
      evidence: ""
    - task: "Register in EU database (if applicable)"
      status: "pending"
      evidence: ""

NIST AI RMF Implementation Checklist

nist_ai_rmf_checklist:
  govern:
    - task: "Establish AI governance policies"
      status: "pending"
    - task: "Define accountability structures"
      status: "pending"
    - task: "Create risk management procedures"
      status: "pending"
    - task: "Establish stakeholder engagement processes"
      status: "pending"
    - task: "Map legal and regulatory requirements"
      status: "pending"

  map:
    - task: "Document intended purpose and use cases"
      status: "pending"
    - task: "Classify AI system by risk category"
      status: "pending"
    - task: "Identify potential impacts and affected parties"
      status: "pending"
    - task: "Document data and model dependencies"
      status: "pending"
    - task: "Identify and enumerate risks"
      status: "pending"

  measure:
    - task: "Define risk metrics and indicators"
      status: "pending"
    - task: "Conduct bias and fairness testing"
      status: "pending"
    - task: "Evaluate model performance"
      status: "pending"
    - task: "Implement continuous monitoring"
      status: "pending"
    - task: "Schedule independent assessments"
      status: "pending"

  manage:
    - task: "Prioritize risks by severity"
      status: "pending"
    - task: "Implement risk mitigations"
      status: "pending"
    - task: "Develop contingency and rollback plans"
      status: "pending"
    - task: "Document residual risks and acceptances"
      status: "pending"
    - task: "Establish ongoing communication processes"
      status: "pending"

References

• EU AI Act Details: See references/eu-ai-act-requirements.md for full regulatory text mapping
• NIST AI RMF: See references/nist-ai-rmf-profiles.md for sector-specific profiles
• Model Cards: See references/model-card-examples.md for completed examples

Related Skills

• threat-modeling - Security threat analysis for AI systems
• devsecops-practices - Integrating AI governance into pipelines
• vulnerability-management - Managing AI system vulnerabilities

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Last Updated: 2025-12-26