AI Model Governance for Regulated Industries

AI governance requirements for regulated industries are rapidly evolving, moving away from voluntary guidelines toward mandated risk management, transparency, and fairness obligations. These requirements are primarily driven by the high-risk AI poses to public health, financial stability, and fundamental rights.

The requirements are generally built upon the principles of established frameworks like the NIST AI Risk Management Framework (AI RMF) and are often codified by sector-specific regulators (like the FDA, CFPB) or major legislation (like the EU’s AI Act).

AI Model Governance Mandates Across All Regulated Industries

Regardless of the industry (Healthcare, Finance, Defense), companies using AI must implement a governance structure that addresses these five pillars:

• Risk Management System (RMS): Establish and maintain a formalized, documented Risk Management System throughout the AI lifecycle, aligned with the NIST AI RMF (Govern, Map, Measure, Manage).
• Transparency and Explainability: Document data provenance, model logic, and provide explainability for decisions that affect people (e.g., loan denial, diagnosis).
• Fairness and Bias Mitigation: Audit AI systems to prevent discriminatory or biased outcomes, using fairness metrics and representative training data.
• Human Oversight and Accountability: Include human-in-the-loop or on-the-loop oversight for high-stakes decisions.
• Data Governance and Quality: Enforce policies to ensure datasets are representative, accurate, and compliant with privacy laws like HIPAA, GDPR, or CCPA.

Industry-Specific High-Risk Applications

Guiding Frameworks and Legislation

• NIST AI RMF: A voluntary U.S. framework with the four functions (Govern, Map, Measure, Manage).
• EU AI Act: Tiered regulation with categories:
  • Unacceptable Risk: Banned uses (e.g., social scoring).
  • High Risk: Strict conformity assessments, high standards for data, oversight, and records.
  • Limited Risk: Transparency only (e.g., chatbots).

Who Should Audit AI Governance?

A. Internal Audit and Risk Management

Internal audit and Model Risk Management teams ensure compliance with internal policies, test for bias, and validate models against NIST AI RMF.

B. Specialized Third-Party Auditors

CPA firms and AI auditing firms provide independent assurance, validating frameworks like ISO 42001. External auditors are expected to become mandatory under the EU AI Act.

C. Government Regulators

Agencies like the FDA, CFPB, and Federal Reserve conduct regulatory oversight and enforce violations, focusing on data quality, bias, and system robustness.

How Should AI Governance Be Audited and Reported?

A. The Audit Methodology

Audit processes follow NIST AI RMF:

1. Govern: Verify policies, roles, and ethics committees.
2. Map: Validate AI system inventory and written risk assessments.
3. Measure: Technical testing (bias, robustness, drift monitoring).
4. Manage: Verify controls such as encryption, oversight protocols, and incident response plans.

B. Reporting Requirements

• Risk Classification Report: Categorizes AI systems by risk level.
• Algorithmic Impact Assessment (AIA): Identifies potential rights impacts and mitigation steps.
• Model Documentation: Technical details (code, data, architecture, validation results).
• Executive Attestation: Senior management confirms compliance and accountability.

How Can RSI Assurance and RSI Security Help Your Organization?

RSI Assurance and RSI Security provide advisory, implementation, and certification services to help companies establish governance frameworks, achieve compliance, and prepare for audits.

Establishing the Governance Framework

Industry-Specific and Assurance Support

• Compliance & Certification: HIPAA, HITRUST, SOC 2, CMMC, ISO 42001 certification.
• Technical Testing: Penetration testing, threat management, ransomware preparedness.
• vCISO Services: Governance program oversight, internal audits, model documentation readiness.