AI Governance Consulting

An AI governance framework covers the policies, roles, controls and technical measures that manage AI risk across a model’s full lifecycle - from data sourcing and training through deployment, monitoring and retirement. It defines who approves model changes, how bias is measured, what audit trails are maintained and how the organization demonstrates compliance to regulators or auditors.

Engagements map to the EU AI Act (risk tiers, conformity assessments and post-market monitoring obligations), NIST AI RMF (Govern, Map, Measure and Manage functions), ISO/IEC 42001 (AI management system standard) and sector-specific overlays including HIPAA for health AI and SR 11-7 / SS1/23 for financial model risk management.

Bias detection applies statistical fairness metrics - demographic parity, equalized odds and calibration - across protected attribute groups defined by your jurisdiction. Pharos builds automated bias evaluation pipelines that run on each model version and produce structured reports suitable for internal review boards and regulatory submissions.

Explainability techniques - SHAP, LIME, attention visualization and counterfactual explanations - make a model’s predictions interpretable to human reviewers. The EU AI Act requires explanations for high-risk AI decisions affecting individuals (credit, hiring, medical diagnosis). GDPR Article 22 grants individuals the right to a meaningful explanation of automated decisions.

Data privacy controls span pseudonymization and anonymization of training sets, differential privacy for sensitive datasets, data minimization reviews to remove unnecessary PII and contractual data processing agreements with model API providers. For inference, PII detection layers can redact sensitive fields before data reaches a third-party model endpoint.

Audit trails record model version, training data lineage, evaluation metrics at release, changes to prompts or hyperparameters, production inference logs and human override events. Retention periods depend on regulation - the EU AI Act mandates at least 10 years for high-risk systems; financial sector requirements typically follow SR 11-7 model inventory rules.

A focused gap assessment against NIST AI RMF or EU AI Act takes 4 to 6 weeks and delivers a prioritized remediation roadmap. Full framework implementation - policy authoring, tooling integration, staff training and audit-ready documentation - typically runs 12 to 20 weeks depending on the number of models in scope and existing data governance maturity.