Chapter 7 – Governance, Ethics, and Compliance
7.1 Ethical Foundations
- Bias & Fairness – ML models can inherit biases from training data. Use balanced datasets and audit for disparate impact.
- Transparency – Provide clear explanations for AI decisions (risk scores, suggested actions).
- Accountability – Maintain an audit trail of all AI‑generated actions and analyst overrides.
- Privacy – Ensure that personal data in logs is anonymized before feeding it to models.
7.2 Regulatory Landscape
| Regulation | Key Requirements | AI‑Specific Considerations |
|---|---|---|
| GDPR | Data minimization, right to explanation | Models must provide explainable decisions for personal data. |
| CCPA | Consumer consent, data deletion | Ensure logs containing consumer data are handled per consent. |
| PCI‑DSS | Secure cardholder data | AI models must not expose card data; use tokenization. |
| NIST CSF | Identify, Protect, Detect, Respond, Recover | AI can support each function but must be documented. |
| ISO/IEC 27001 | Information security management | AI processes must be part of the ISMS. |
7.3 Governance Framework
- Policy Definition – Draft AI usage policies covering data handling, model lifecycle, and incident response.
- Model Governance Board – Include data scientists, security analysts, and legal counsel.
- Version Control – Store model artifacts in a registry (MLflow, DVC) with metadata.
- Change Management – Treat model updates as software releases; require sign‑off.
- Risk Assessment – Perform a risk assessment for each model (data, algorithm, deployment).
7.4 Auditing and Monitoring
- Model Drift Detection – Monitor input distribution and performance metrics; trigger retraining if drift exceeds thresholds.
- Explainability Audits – Periodically review SHAP/LIME explanations for a sample of alerts.
- Compliance Audits – Use automated tools to scan logs for policy violations (e.g., PII exposure).
- Incident Logging – Log every AI decision, including confidence scores and the user who approved it.
7.5 Data Governance
- Data Catalog – Maintain a catalog of all data sources, retention policies, and access controls.
- Consent Management – Track user consent for data usage; enforce opt‑out.
- Data Retention – Align with regulatory retention periods; purge data securely.
- Encryption – Encrypt data at rest (AES‑256) and in transit (TLS 1.3).
7.6 Human‑in‑the‑Loop (HITL)
- Override Mechanism – Analysts can override AI suggestions; the system logs the rationale.
- Feedback Loop – Overrides are fed back into the training pipeline to reduce future false positives.
- Training – Provide analysts with training on AI capabilities, limitations, and how to interpret explanations.
7.7 Future‑Proofing
- Model Agility – Design models to be modular; swap components (e.g., LLM, RL policy) without breaking the pipeline.
- Regulatory Watch – Monitor emerging AI regulations (e.g., EU AI Act) and update policies accordingly.
- Ethical AI Roadmap – Publish a roadmap for responsible AI adoption, including milestones for bias mitigation and transparency.
This chapter equips readers with the principles, policies, and practical steps needed to responsibly govern AI in a security context, ensuring compliance with current regulations and preparing for future legal developments.