AI Governance Framework
A comprehensive, enterprise-ready framework for establishing responsible AI governance — designed to manage AI risks, ensure regulatory compliance, embed ethical principles, and enable safe AI adoption at scale.
Request a Consultation →Structured AI Governance for Your Organization
Our AI Governance Framework provides the structures, policies, processes, and controls needed to govern artificial intelligence responsibly — from strategy and oversight through to operational risk management and regulatory compliance.
AI Governance Committee & Accountability Structure
Establish clear ownership, decision-making authority, and oversight mechanisms for AI across the enterprise.
Committee Charter
Terms of reference, membership structure, decision protocols, and escalation pathways to board level
Role Definitions
RACI matrix defining accountability for AI development, deployment, risk management, and compliance
Meeting Cadence
Regular governance forums, standing agenda items, and documentation requirements
Reporting Frameworks
Board-level AI risk dashboards, KPIs, and management information templates
AI Risk Management Framework
Systematic identification, assessment, and mitigation of AI-specific risks including bias, opacity, security, and compliance risks.
Risk Taxonomy
Comprehensive classification of AI risks — bias, fairness, transparency, privacy, security, and operational risks
Risk Assessment
Methodologies for evaluating AI risk likelihood, impact, and velocity across the model lifecycle
Risk Appetite
Board-approved appetite statements defining acceptable AI risk levels by risk category
Control Design
Technical and procedural controls mapped to AI risks, including testing and validation protocols
AI Ethics Principles & Responsible AI Standards
Define organizational values and ethical guardrails for AI development, deployment, and use.
Ethics Principles
Organizational AI ethics charter covering fairness, transparency, accountability, and human oversight
Bias Assessment
Frameworks for detecting, measuring, and mitigating bias in training data and model outputs
Explainability Standards
Requirements for model transparency, decision explainability, and human-understandable outputs
Ethical Review
Ethics review board procedures for high-risk AI applications and use cases
AI Policy Suite
Comprehensive policy framework governing AI acquisition, development, deployment, and ongoing management.
AI Acceptable Use Policy
Defines approved AI use cases, prohibited applications, and user responsibilities across the organization
Model Development Lifecycle Policy
Standards for AI/ML model design, development, testing, validation, and approval processes
AI Procurement & Vendor Management Policy
Requirements for third-party AI evaluation, due diligence, contractual controls, and ongoing monitoring
Data Governance for AI Policy
Data quality, lineage, privacy, and protection standards specific to AI training and operations
AI Incident Response Policy
Procedures for detecting, escalating, investigating, and remediating AI-related incidents
Model Retirement & Decommissioning Policy
Standards for AI model sunset, data retention, and knowledge preservation
AI Model Lifecycle Management
End-to-end governance across model development, deployment, monitoring, and retirement.
Model Inventory & Registry
Centralized catalog of all AI models with metadata, risk classification, and ownership
Development Workflows
Stage-gate approval processes from concept through testing, validation, and production deployment
Performance Monitoring
KPIs, KRIs, and automated monitoring for model accuracy, drift, and operational performance
Change Management
Controlled procedures for model updates, retraining, and version control
Regulatory Compliance & Assurance
Ensure compliance with AI regulations including the EU AI Act and sector-specific requirements.
EU AI Act Compliance
Risk classification mapping, conformity assessment procedures, and documentation requirements
Regulatory Horizon Scanning
Ongoing monitoring of emerging AI regulations, standards, and guidance globally
Compliance Assessment
Tools and frameworks for evaluating AI systems against regulatory requirements
Audit & Assurance
Internal audit programs, external assurance engagement, and regulatory reporting templates
Framework Deliverables
Working documents, templates, and frameworks ready to implement in your organization
Committee Charter
Terms of reference, membership criteria, and decision-making protocols
Risk Register
AI risk taxonomy, assessment templates, and control library
Ethics Framework
Principles, bias assessment tools, and ethical review procedures
Policy Suite
Complete set of AI governance policies ready for customization
Workflow Templates
Model approval workflows, change processes, and lifecycle management
Compliance Toolkit
EU AI Act mapping, audit checklists, and reporting templates
Implement AI Governance in Your Organization
Work with our advisors to design, customize, and implement this framework for your organization — tailored to your industry, risk appetite, and regulatory environment.
