Description

This curriculum spans the design and operationalization of AI governance as an extension of enterprise data governance, comparable in scope to a multi-phase advisory engagement that integrates policy, risk, and technical controls across the AI lifecycle.

Module 1: Defining the Scope and Boundaries of AI Governance within Data Governance

Determine whether AI governance falls under existing data governance frameworks or requires a parallel structure with shared oversight.
Decide which AI use cases (e.g., predictive analytics, NLP, computer vision) are in scope based on risk exposure and data dependency.
Establish ownership of AI models: assign accountability to data stewards, ML engineers, or a centralized AI governance office.
Map data lineage from source systems through preprocessing pipelines to AI model inputs to assess governance coverage gaps.
Define thresholds for model complexity that trigger mandatory governance review (e.g., models with >30 features or ensemble architectures).
Integrate AI asset inventories with existing data catalog practices, including model versioning and dependency tracking.
Assess regulatory overlap between data privacy laws (e.g., GDPR) and AI-specific regulations (e.g., EU AI Act) to avoid duplication.
Negotiate authority boundaries between data governance councils and AI ethics review boards when policies conflict.

Module 2: Establishing Roles, Responsibilities, and Decision Rights

Assign model validation responsibilities: determine whether internal audit, data science leads, or third parties conduct pre-deployment reviews.
Define escalation paths for model drift detection, including thresholds for retraining and stakeholder notification.
Specify who has authority to override model outputs in production (e.g., clinicians in healthcare AI, underwriters in insurance).
Implement dual control for model deployment: require sign-off from both data governance and model risk management teams.
Clarify whether data stewards have veto power over training data selection when bias risks are identified.
Designate a model owner responsible for ongoing monitoring, documentation updates, and compliance with retention policies.
Coordinate cross-functional RACI matrices covering data engineers, ML ops, legal, and compliance for AI lifecycle stages.
Formalize escalation procedures when model behavior conflicts with enterprise data quality standards.

Module 3: Integrating AI Risk Management into Data Risk Frameworks

Classify AI models using risk tiers (low, medium, high) based on impact, autonomy, and data sensitivity to prioritize governance effort.
Embed model risk assessments into existing data risk registers, including failure modes like data poisoning or concept drift.
Require data provenance verification for all training datasets, especially third-party or crowdsourced data.
Implement mandatory adversarial testing for high-risk models before production deployment.
Define incident response protocols for AI-related data breaches, including model inversion or membership inference attacks.
Set thresholds for acceptable false positive/negative rates in regulated domains (e.g., credit scoring, medical diagnosis).
Conduct periodic red team exercises to simulate data manipulation attacks on model inputs.
Link model risk ratings to data classification levels, requiring stricter controls for models using PII or protected attributes.

Module 4: Data Quality and Integrity for AI Systems

Define data quality rules specific to AI, such as feature completeness, label consistency, and absence of leakage indicators.
Implement automated checks for training-serving skew by comparing real-time input distributions to training data profiles.
Establish data drift detection thresholds that trigger model retraining or manual review.
Enforce schema validation at ingestion points to prevent silent data type mismatches in feature pipelines.
Document data transformation logic in feature stores to ensure reproducibility and auditability.
Apply outlier detection on input data streams to flag potential data integrity issues before model inference.
Require versioned datasets for model training to support reproducibility during audits or incident investigations.
Monitor for silent data corruption in distributed storage systems that could affect model training integrity.

Module 5: Bias, Fairness, and Ethical Model Development

Select fairness metrics (e.g., demographic parity, equalized odds) based on use case and regulatory context.
Implement pre-processing bias mitigation techniques, such as reweighting or adversarial debiasing, in data pipelines.
Conduct stratified testing across protected attributes during model validation, even when those attributes are excluded from modeling.
Document known biases in training data and their potential impact on model outcomes for audit purposes.
Establish thresholds for disparate impact that require model redesign or stakeholder consultation.
Require fairness testing across multiple model versions to detect regression in ethical performance.
Design feedback loops to capture real-world outcomes by demographic group for post-deployment fairness monitoring.
Balance fairness objectives against predictive performance when trade-offs are unavoidable, with documented justification.

Module 6: Model Documentation, Transparency, and Explainability

Standardize model cards that include data sources, evaluation metrics, known limitations, and intended use cases.
Implement automated generation of partial dependence plots and SHAP values for high-risk models.
Define minimum explainability requirements based on risk tier (e.g., full interpretability for credit denial models).
Store model documentation in version-controlled repositories linked to model deployment artifacts.
Require data lineage tracing from raw inputs to final model features for auditability.
Develop user-facing explanations that are meaningful to non-technical stakeholders without oversimplifying risk.
Balance transparency requirements with intellectual property protection for proprietary algorithms.
Validate that explanation methods do not introduce new biases or misrepresent model behavior.

Module 7: Regulatory Compliance and Audit Readiness

Map AI governance controls to specific regulatory requirements (e.g., SR 11-7, GDPR Article 22, NYDFS 500).
Maintain audit trails for model changes, including who approved updates and what testing was performed.
Prepare model risk assessment packages for external auditors, including validation reports and governance approvals.
Implement data retention policies for model artifacts, training data snapshots, and inference logs.
Conduct mock audits to test readiness for regulatory inquiries on high-risk AI systems.
Document decisions to use non-auditable third-party models, including risk acceptance justifications.
Ensure logging mechanisms capture sufficient detail to reconstruct model decisions during investigations.
Coordinate with legal to interpret evolving AI regulations and update governance policies accordingly.

Module 8: Monitoring, Validation, and Continuous Governance

Deploy automated monitoring for model performance decay, including accuracy, precision, and recall degradation.
Set up alerts for distributional shifts in input features that exceed predefined stability thresholds.
Implement A/B testing frameworks to compare new model versions against baselines before full rollout.
Conduct periodic model validation cycles, with frequency based on risk tier and usage volume.
Track model usage patterns to detect unauthorized or unintended deployment across business units.
Integrate model monitoring dashboards with enterprise data quality and incident management systems.
Define retraining triggers based on performance decay, data drift, or business requirement changes.
Enforce model retirement procedures, including data deletion and stakeholder notification.

Module 9: Cross-System Integration and Technology Alignment

Integrate model metadata into enterprise data catalogs using standardized schemas (e.g., OpenMetadata, DCAT).
Enforce API contracts between data platforms and model serving environments to ensure schema compatibility.
Implement centralized feature stores with access controls aligned to data governance policies.
Align model registry practices with data versioning tools (e.g., DVC, Delta Lake) for end-to-end traceability.
Secure model inference endpoints using the same authentication and authorization frameworks as data APIs.
Ensure logging from ML pipelines feeds into centralized SIEM systems for security monitoring.
Coordinate data masking rules between training environments and production inference systems.
Standardize data format and serialization protocols (e.g., Parquet, Protobuf) across AI and data infrastructure.

Module 10: Change Management and Organizational Adoption

Develop playbooks for decommissioning legacy models that lack governance controls.
Conduct impact assessments before introducing new governance requirements that affect model development timelines.
Train data scientists on governance workflows, including documentation standards and approval processes.
Implement governance checkpoints in CI/CD pipelines for ML models (e.g., automated policy checks).
Address resistance from technical teams by aligning governance requirements with operational efficiency goals.
Establish feedback mechanisms for data stewards to report governance gaps observed in production models.
Measure adoption of governance practices through compliance audit results and policy exception rates.
Iterate on governance processes based on post-mortem reviews of model failures or compliance incidents.