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Enterprise Responsible AI Framework

A comprehensive step-by-step implementation guide for AI executives building ethical, compliant, and trustworthy AI systems

8 Chapters 5 Appendices 100+ Implementation Steps Global Compliance Ready

61%

Orgs at Strategic RAI Stage

$35M

Max EU AI Act Fine

40%

Higher ROI with RAI

73%

Apps with Prompt Injection Risk

How to Use This Framework

This framework has been created by Trevor Kennedy, is opensourced, so use freely and at your discretion. It is designed to be modular and scalable. Start with Chapter 1 to establish your vision and principles, then progress through governance (Chapter 2), risk classification (Chapter 3), and the AI lifecycle (Chapter 4). Chapters 5-7 address specialized topics, while Chapter 8 provides ready-to-use templates and tools.

Executive Vision & Scope

1.1 The Business Case for Responsible AI 1.2 Core Ethical Principles (The North Star) 1.3 Scope of Application

Governance & Organizational Structure

2.1 The "Three Lines of Defense" Model 2.2 Roles & Responsibilities (RACI Matrix) 2.3 Escalation Pathways

Risk Classification & Taxonomy

3.1 The AI Risk Tiering System 3.2 Impact Assessment Methodology

The Responsible AI Lifecycle (Process Controls)

4.1 Phase 1: Ideation & Design 4.2 Phase 2: Data Curation & Management 4.3 Phase 3: Model Development & Training 4.4 Phase 4: Testing & Validation 4.5 Phase 5: Deployment & Release 4.6 Phase 6: Monitoring & Maintenance

Generative AI & LLM Specifics

5.1 LLM Guardrails 5.2 Content Governance

Third-Party Procurement & Supply Chain

6.1 Vendor Due Diligence Checklist 6.2 AI Bill of Materials (AI BOM)

Culture, Training & Adoption

7.1 Workforce Enablement 7.2 Change Management

Appendices & Toolkits

A Algorithmic Impact Assessment Template B Vendor AI Security Questionnaire C Model Card Template D Risk Scoring Matrix E Glossary of AI Ethics Terms

Key Regulatory Frameworks Referenced

EU AI Act

World's first comprehensive AI regulation with risk-based classification. High-risk rules effective August 2026, GPAI obligations from August 2025.

US Executive Orders

Federal AI policy framework seeking uniform national standards. FTC oversight on deceptive AI practices.

NIST

NIST AI RMF

Voluntary risk management framework with Govern, Map, Measure, Manage functions. Gen AI Profile released July 2024.

GDPR

Data protection requirements integral to AI systems processing personal data. DPIAs required for high-risk processing.

Implementation Timeline Overview

Month 1-2

Foundation

Establish governance structure, appoint CAIO, form AI Ethics Board, complete initial AI inventory

Month 3-4

Risk Assessment

Classify all AI systems by risk tier, conduct algorithmic impact assessments, identify shadow AI

Month 5-6

Process Implementation

Deploy lifecycle controls, implement guardrails for LLMs, establish monitoring systems

Month 7-8

Training & Culture

Roll out workforce training programs, establish feedback mechanisms, launch change management

Month 9-12

Optimization & Audit

Conduct internal audits, optimize processes, prepare for external assessments, continuous improvement

Getting Started

Begin your implementation journey with Section 1.1: The Business Case for Responsible AI to understand the strategic imperatives driving responsible AI adoption, then proceed to establish your Core Ethical Principles.