AI Governance & Compliance: A Practical Guide

The New Regulatory Landscape

2025 saw unprecedented regulatory activity around artificial intelligence. Organizations deploying AI systems must now navigate a complex web of regulations and standards.

Key Regulatory Frameworks

EU AI Act

The EU AI Act establishes a risk-based approach to AI regulation:

• Prohibited AI: Systems deemed unacceptably risky
• High-Risk AI: Systems in critical sectors with strict requirements
• Limited Risk: Systems with transparency obligations
• Minimal Risk: No restrictions

NIST AI Risk Management Framework

Provides a voluntary framework for managing AI risks throughout the lifecycle, emphasizing governance, measurement, transparency, and human oversight. For a complete implementation guide, see our detailed CISO AI Risk Management Framework.

NIST AI Risk Management Framework: Implementation Guide

The NIST AI Risk Management Framework (AI RMF 1.0) provides organizations with a structured approach to managing AI risks throughout the entire lifecycle. Released by the National Institute of Standards and Technology, this voluntary framework is designed for any organization that develops, deploys, or uses AI systems, from technology companies building foundation models to financial institutions using AI for credit decisions. The framework organizes AI risk management into four core functions: Govern, Map, Measure, and Manage. Govern establishes organizational policies and accountability structures. Map identifies and contextualizes risks specific to each AI system. Measure applies quantitative and qualitative tools to assess risk severity. Manage prioritizes and implements risk mitigation actions. In practice, organizations use the NIST AI RMF alongside existing security frameworks like ISO 27001 and NIST CSF, treating it as an extension rather than a replacement. For Singapore-based organizations, the framework aligns well with MAS TRM expectations around technology risk governance.

Core Function 1: Govern

Govern establishes the foundation for AI risk management by creating organizational culture, processes, and documentation that enable responsible AI development and deployment.

Implementation Steps:

• Establish AI Governance Structure: Create a cross-functional AI governance committee with representation from legal, compliance, security, engineering, and business units
• Define Risk Tolerance: Document your organization's acceptable risk thresholds for different AI applications and use cases
• Create AI Policies: Develop comprehensive policies covering AI development, procurement, deployment, and monitoring
• Assign Accountability: Designate responsible individuals for AI system lifecycle management and risk mitigation
• Establish Review Processes: Implement periodic review cycles for AI systems, policies, and risk assessments

Security Considerations:

• Integrate AI governance with existing information security governance structures
• Ensure AI risk management aligns with your enterprise risk management framework
• Establish clear escalation paths for AI-related security incidents
• Document decision-making authority and approval workflows for AI systems
• Maintain audit trails of governance decisions and policy changes

Core Function 2: Map

Map provides context for understanding AI risks by identifying potential harms, benefits, and impacts throughout the AI system lifecycle.

Implementation Steps:

• AI System Inventory: Maintain a comprehensive registry of all AI systems, including third-party tools and embedded AI features
• Use Case Analysis: Document intended purposes, expected users, deployment contexts, and success metrics for each system
• Stakeholder Mapping: Identify all internal and external stakeholders affected by AI systems
• Impact Assessment: Conduct systematic assessments of potential benefits and harms across different stakeholder groups
• Dependency Analysis: Map data sources, model dependencies, and integration points with existing systems

Security Considerations:

• Identify data flows and classify data sensitivity throughout the AI lifecycle
• Map attack surfaces including APIs, model interfaces, and data pipelines
• Document supply chain risks for third-party AI components and models
• Assess compliance requirements across jurisdictions, with specific attention to MAS TRM guidelines for financial institutions in Singapore where AI systems operate
• Evaluate potential for adversarial exploitation of identified use cases

Core Function 3: Measure

Measure employs quantitative, qualitative, and mixed-method tools to analyze and track AI risks throughout the lifecycle.

Implementation Steps:

• Define Metrics: Establish key performance indicators (KPIs) and risk metrics for each AI system
• Baseline Assessment: Document initial system performance, fairness metrics, and risk levels
• Continuous Monitoring: Implement automated monitoring for model performance, data drift, and emerging risks
• Testing Protocols: Develop standardized testing procedures for accuracy, robustness, fairness, and security
• Documentation Standards: Create model cards, data sheets, and system documentation following transparency best practices

Security Considerations:

• Implement automated testing for adversarial robustness and model inversion attacks
• Monitor for data poisoning and training set manipulation
• Track model performance degradation and unexpected behavior patterns
• Measure fairness metrics across protected demographic groups
• Establish incident detection thresholds and automated alerting
• Regular penetration testing of AI systems and APIs

Core Function 4: Manage

Manage involves prioritizing, acting upon, and sustaining risk mitigation strategies throughout the AI lifecycle.

Implementation Steps:

• Risk Prioritization: Use established risk matrices to prioritize mitigation efforts based on impact and likelihood
• Mitigation Planning: Develop specific action plans for identified risks with owners, timelines, and success criteria
• Incident Response: Create AI-specific incident response procedures including containment, investigation, and recovery
• Regular Monitoring: Implement ongoing monitoring systems to track mitigation effectiveness and emerging risks
• Continuous Improvement: Establish feedback loops to incorporate lessons learned into future AI systems

Security Considerations:

• Implement security controls commensurate with AI system risk classification
• Establish rapid response capabilities for AI-specific attacks (prompt injection, model poisoning)
• Maintain backup and rollback procedures for AI models and configurations
• Conduct regular security reviews of AI system architecture and implementations
• Document and communicate security requirements to AI vendors and development teams
• Implement access controls for model training, deployment, and modification

Quick Reference: NIST AI RMF Implementation

Function	Key Activities	Security Priorities	Timeline
Govern	Policy development, accountability structures, risk tolerance definition	Governance integration, clear escalation paths, audit trails	Ongoing foundation
Map	System inventory, use case analysis, stakeholder identification	Attack surface mapping, data classification, supply chain risks	Initial + quarterly updates
Measure	Metrics definition, monitoring implementation, testing protocols	Adversarial testing, performance monitoring, fairness metrics	Continuous monitoring
Manage	Risk prioritization, mitigation planning, incident response	Security controls, rapid response, access controls	Immediate + ongoing

The four core functions of the NIST AI RMF operate as an interconnected cycle rather than a linear checklist. Govern sets the foundation by defining organizational risk tolerance, assigning accountability for AI systems, and establishing policies for responsible AI development and deployment. This function answers the question of who owns AI risk and what standards apply. Map identifies the specific context in which an AI system operates: what data it uses, what decisions it influences, who it affects, and what could go wrong at each stage of the lifecycle. Measure applies analytical tools to quantify mapped risks, including performance testing, bias assessments, and adversarial evaluation. Manage translates those measurements into action by prioritizing risks based on severity and organizational tolerance, then implementing controls, accepting residual risk, or discontinuing the AI system if risk exceeds acceptable thresholds. Organizations should cycle through these functions continuously as AI systems evolve and new risks emerge, rather than treating them as a one-time compliance exercise.

For a deeper dive into implementing these frameworks in your organization, check out our comprehensive guide on CISO AI Risk Management Framework: A Complete Implementation Guide, which includes detailed templates, checklists, and case studies.

Industry-Specific Requirements

Financial Services

Regulators expect AI systems to meet existing model risk management standards, with additional focus on explainability and fairness.

Healthcare

AI medical devices must navigate both device regulations and AI-specific requirements, with emphasis on clinical validation and bias mitigation.

Building a Compliance Program

1. AI Inventory

Maintain a comprehensive inventory of all AI systems, including:

• Purpose and use cases
• Data sources and training methods
• Risk classification
• Compliance requirements

2. Risk Assessment

Conduct regular risk assessments covering:

• Algorithmic bias and fairness
• Privacy and data protection
• Security vulnerabilities
• Operational risks

3. Documentation

Maintain thorough documentation including:

• Model cards and data sheets
• Impact assessments
• Audit trails
• Incident response procedures

4. Monitoring and Auditing

Implement continuous monitoring and regular audits to ensure ongoing compliance.

Looking Ahead

AI regulations will continue to evolve rapidly. Organizations must build flexible governance frameworks that can adapt to new requirements while maintaining operational effectiveness.