Description

This curriculum spans the design and operational enforcement of data governance across AI, ML, and RPA systems, comparable in scope to a multi-phase internal capability program that integrates policy, technical controls, and cross-functional workflows seen in enterprise-scale AI governance rollouts.

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

Determine which data assets feeding AI/ML models require governance oversight based on risk exposure and regulatory impact.
Establish criteria for classifying data as high-risk (e.g., PII, credit decisions) versus low-risk (e.g., anonymized usage logs) within AI pipelines.
Negotiate governance authority over third-party data vendors supplying training data for machine learning models.
Decide whether shadow AI models developed in business units fall under enterprise governance mandates.
Define ownership of model input data versus model output data in cross-functional AI projects.
Align governance scope with existing enterprise data catalogs and metadata repositories to avoid duplication.
Assess the feasibility of extending governance to unstructured data sources such as chat logs used in NLP models.
Resolve conflicts between data science agility and governance control in experimental versus production AI environments.

Module 2: Establishing Ethical Principles and Operationalizing Them in Governance Frameworks

Select ethical principles (e.g., fairness, transparency, accountability) based on industry-specific regulatory and reputational risks.
Translate abstract ethical principles into measurable data quality and model performance benchmarks.
Implement bias detection thresholds in training data preprocessing workflows based on demographic parity or equal opportunity metrics.
Define escalation paths when model behavior violates stated ethical guidelines during validation or production.
Integrate ethical review checklists into model deployment gates within CI/CD pipelines.
Document trade-offs between model accuracy and fairness when optimization objectives conflict.
Assign responsibility for ethical compliance between data stewards, model validators, and legal teams.
Conduct retrospective ethical impact assessments after model incidents involving biased outcomes.

Module 3: Regulatory Compliance Mapping Across Jurisdictions and AI Use Cases

Map GDPR data subject rights (e.g., right to explanation) to technical capabilities in black-box AI models.
Identify which RPA bots handling personal data must comply with CCPA disclosure requirements.
Implement data lineage tracking to support audit requests under Brazil’s LGPD or Canada’s PIPEDA.
Classify AI models as high-risk under EU AI Act and apply mandatory data governance documentation.
Coordinate with legal to interpret evolving regulations like the U.S. AI Executive Order in data handling practices.
Design data retention policies for model training sets that align with sector-specific rules (e.g., HIPAA in healthcare).
Conduct gap analyses between current data governance practices and regulatory expectations in cross-border AI deployments.
Implement consent management workflows for data used in AI training where opt-in requirements apply.

Module 4: Designing Data Lineage and Provenance for Model Transparency

Select tools to capture end-to-end lineage from raw data sources through feature engineering to model inference.
Define metadata standards for tracking data transformations applied in ML feature pipelines.
Integrate lineage capture into automated model retraining workflows to maintain up-to-date audit trails.
Balance granularity of lineage data against storage and performance costs in real-time AI systems.
Expose lineage information to auditors while restricting access to sensitive source systems for security reasons.
Validate lineage completeness during model certification before production deployment.
Implement automated alerts when data sources in the lineage chain are modified or deprecated.
Use lineage data to reconstruct model behavior during incident investigations or regulatory inquiries.

Module 5: Managing Consent and Data Subject Rights in Automated Systems

Implement data tagging to track consent status across multiple processing purposes in AI training datasets.
Design mechanisms to exclude data subjects who withdraw consent from future model retraining cycles.
Enable data subject access requests (DSARs) for personal data used in model development and inference logs.
Develop processes to delete or anonymize personal data from historical training sets upon request.
Coordinate with RPA teams to ensure bots do not process data when consent has expired or been revoked.
Assess technical feasibility of providing meaningful explanations for automated decisions under GDPR Article 22.
Log all data subject rights fulfillment actions for audit and compliance reporting.
Integrate consent status checks into real-time inference APIs to prevent unauthorized processing.

Module 6: Governing Data Quality for Model Reliability and Fairness

Define data quality rules specific to model inputs, such as completeness thresholds for critical features.
Monitor for data drift by comparing statistical properties of training versus inference data in production.
Implement automated data profiling at ingestion points feeding into ML pipelines.
Address missing data patterns that may introduce bias, particularly in underrepresented populations.
Validate representativeness of training data against real-world population distributions.
Set up alerting when data quality metrics fall below thresholds before model retraining.
Document data quality exceptions approved for model training with risk assessments and approvals.
Coordinate data cleansing efforts between data engineering and domain experts for high-impact features.

Module 7: Cross-Functional Governance Roles and Accountability Models

Assign formal data stewardship roles for AI training datasets with documented responsibilities and escalation paths.
Establish a governance council with representation from legal, compliance, data science, and business units.
Define RACI matrices for data decisions in AI projects, including model validation and deployment approvals.
Implement sign-offs for data usage in high-risk AI applications by data owners and legal advisors.
Resolve disputes between data scientists seeking broad access and stewards enforcing data minimization.
Train data stewards on AI-specific risks such as proxy variables introducing indirect bias.
Document decision logs for contested data usage cases to support audit and regulatory review.
Integrate governance responsibilities into performance evaluations for data and AI roles.

Module 8: Auditing and Monitoring AI Systems for Ethical and Governance Compliance

Design audit trails that capture model version, training data snapshot, and parameter configuration at deployment.
Implement continuous monitoring for fairness metrics across demographic groups in production models.
Conduct periodic audits of RPA bots to verify they operate within authorized data access boundaries.
Use synthetic test datasets to evaluate model behavior under edge-case ethical scenarios.
Generate governance dashboards showing compliance status across all active AI and RPA systems.
Perform root cause analysis when monitoring detects deviations from ethical or data quality standards.
Coordinate external audit access to governance artifacts while protecting intellectual property.
Update monitoring rules based on new regulatory requirements or past model incidents.

Module 9: Governance of Third-Party and Open-Source AI Components

Assess data governance risks in pre-trained models sourced from external providers or open repositories.
Verify data provenance and licensing terms for open-source datasets used in model training.
Implement contractual clauses requiring third-party vendors to disclose data sources and bias testing results.
Scan third-party models for embedded PII or sensitive attributes learned during training.
Conduct due diligence on data practices of API providers used in composite AI systems.
Establish approval workflows for incorporating external models into regulated business processes.
Monitor for updates in open-source model versions that may affect data governance compliance.
Isolate third-party model data flows in secure environments to limit unauthorized data propagation.

Module 10: Incident Response and Remediation in AI-Driven Data Breaches or Ethical Failures

Define escalation protocols for data governance incidents involving biased model outcomes or unauthorized data use.
Conduct forensic analysis to trace whether flawed data inputs contributed to a model failure.
Implement rollback procedures to revert to previous model versions when data integrity is compromised.
Notify affected stakeholders when data misuse in AI systems results in harm or regulatory violations.
Update governance policies based on root causes identified in post-incident reviews.
Preserve data and model artifacts for legal discovery during investigations.
Coordinate with cybersecurity teams when data exfiltration occurs via compromised RPA bots.
Document remediation actions taken to prevent recurrence of data governance failures.