Governance & Compliance in AI

Why Governance and Compliance Matter

Governance is about managing, optimizing, and scaling AI initiatives inside an organization.

  • It builds trust in AI systems.
  • Ensures responsible and trustworthy practices.
  • Mitigates risks such as bias, privacy violations, or unintended outcomes.
  • Aligns AI systems with legal and regulatory requirements.
  • Protects against legal and reputational risks.
  • Fosters public trust and confidence in AI deployment.

๐Ÿ“Œ Exam tip: Expect questions that connect governance with trust, compliance, and risk management. AWS often tests your understanding of why governance is necessary, not just how.

Governance Framework

A typical governance approach includes:

  1. AI Governance Board / Committee
    • Cross-functional: legal, compliance, data privacy, and AI experts.
  2. Defined Roles and Responsibilities
    • Oversight, policy-making, risk assessments, decision-making.
  3. Policies & Procedures
    • Covering the full AI lifecycle: data management โ†’ training โ†’ deployment โ†’ monitoring.

AWS Governance Tools (likely on exam):

  • AWS Config โ€“ continuous monitoring and compliance tracking.
  • Amazon Inspector โ€“ automated vulnerability management.
  • AWS CloudTrail โ€“ records API calls for auditing.
  • AWS Audit Manager โ€“ helps with compliance evidence collection.
  • AWS Trusted Advisor โ€“ best practice checks (cost, security, performance).

Governance Strategies

  • Policies: Responsible AI guidelines (data handling, training, bias mitigation, IP protection).
  • Review Cadence: Reviews monthly, quarterly, or annually, with technical + legal experts.
  • Review Types:
    • Technical: model performance, data quality, robustness.
    • Non-technical: legal, compliance, ethical considerations.
  • Transparency: Publish model details, training data sources, decisions made, limitations.
  • Team Training: Policies, responsible AI, bias mitigation, cross-functional collaboration.

Data Governance

  • Responsible AI Principles: fairness, accountability, transparency, bias monitoring.
  • Governance Roles:
    • Data Owner: accountable for data.
    • Data Steward: ensures quality, compliance.
    • Data Custodian: manages technical storage/security.
  • Data Sharing: secure sharing agreements, virtualization, federation.
  • Data Culture: encourage data-driven decision-making.

Data Management Concepts

  • Lifecycle: collection โ†’ processing โ†’ storage โ†’ use โ†’ archival.
  • Logging: track inputs, outputs, metrics, events.
  • Residency: where data is stored/processed (important for GDPR & HIPAA).
  • Monitoring: quality, anomalies, drift.
  • Retention: meet regulations and manage storage costs.

Data Lineage

  • Source citation: datasets, licenses, permissions.
  • Origins: collection, cleaning, transformations.
  • Cataloging: organize & document datasets.
  • Provides traceability & accountability.

Security & Privacy for AI

  • Threat Detection: fake content, manipulated data, automated attacks.
  • Vulnerability Management: penetration tests, code reviews, patching.
  • Infrastructure Protection: secure cloud platforms, access controls, encryption, redundancy.
  • Prompt Injection Defense: input sanitization, guardrails.
  • Encryption: always encrypt data at rest & in transit; manage keys securely.

Monitoring AI Systems

  • Model Metrics:
    • Accuracy
    • Precision (true positives / predicted positives)
    • Recall (true positives / actual positives)
    • F1-score (balance between precision & recall)
    • Latency (response time)
  • Infrastructure Monitoring: CPU/GPU, network, storage, logs.
  • Bias & Fairness Monitoring: required for compliance.

AWS Shared Responsibility Model

  • AWS responsibility โ€“ Security of the Cloud
    Infrastructure: hardware, networking, managed services like S3, SageMaker, Bedrock.
  • Customer responsibility โ€“ Security in the Cloud
    Data management, encryption, access controls, guardrails.
  • Shared controls: patch management, configuration management, training.

๐Ÿ“Œ Exam tip: Always remember the โ€œof the cloudโ€ vs. โ€œin the cloudโ€ split.

Secure Data Engineering Best Practices

  • Data Quality: complete, accurate, timely, consistent.
  • Privacy Enhancements: masking, obfuscation, encryption, tokenization.
  • Access Control: RBAC (role-based access), fine-grained permissions, SSO, MFA.
  • Data Integrity: error-free, backed up, lineage maintained, audit trails in place.

Generative AI Security Scoping Matrix

Levels of ownership and security responsibility:

  1. Consumer App โ€“ very low ownership (e.g., using ChatGPT directly).
  2. Enterprise App โ€“ SaaS with GenAI features (e.g., Salesforce GPT).
  3. Pre-trained Models โ€“ use Bedrock base models without training.
  4. Fine-tuned Models โ€“ customize models with your data.
  5. Self-trained Models โ€“ full ownership, trained from scratch.

๐Ÿ“Œ Exam tip: The more control you have โ†’ the more security and compliance responsibility you carry.

MLOps (Machine Learning Operations)

Extension of DevOps for ML:

  • Version Control: data, code, models.
  • Automation: pipelines for ingestion, preprocessing, training.
  • CI/CD: continuous testing and delivery of models.
  • Retraining: incorporate new data.
  • Monitoring: catch drift, ensure fairness and performance.

Example ML pipeline:

  1. Data prep
  2. Build model
  3. Evaluate model
  4. Select best candidate
  5. Deploy to production
  6. Monitor + retrain

๐Ÿ“Œ Exam tip: AWS may test your knowledge of SageMaker pipelines, model registry, and monitoring tools as part of MLOps.

Phases of Machine Learning Project