This framework has been created by Principal Ai Consultants, 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.
Watchman: Capability Assurance for Responsible AI
The RAI Framework defines what responsible AI deployment looks like. Watchman makes it verifiable. Every quantized model deployed through our platform receives a Watchman capability audit — a verified, auditable report proving which capabilities are preserved, at risk, or degraded.
Together, the RAI Framework and Watchman give regulated organisations:
- Auditable capability preservation certificates for every compressed model
- Compliance-ready evidence derived from model internals, not benchmark runs
- Continuous capability monitoring that satisfies regulatory requirements
- Verifiable proof that fine-tuned capabilities are protected through compression
Executive Vision & Scope
Governance & Organizational Structure
Risk Classification & Taxonomy
The Responsible AI Lifecycle (Process Controls)
Generative AI & LLM Specifics
Third-Party Procurement & Supply Chain
Culture, Training & Adoption
Appendices & Toolkits
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 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
Foundation
Establish governance structure, appoint CAIO, form AI Ethics Board, complete initial AI inventory
Risk Assessment
Classify all AI systems by risk tier, conduct algorithmic impact assessments, identify shadow AI
Process Implementation
Deploy lifecycle controls, implement guardrails for LLMs, establish monitoring systems
Training & Culture
Roll out workforce training programs, establish feedback mechanisms, launch change management
Optimization & Audit
Conduct internal audits, optimize processes, prepare for external assessments, continuous improvement
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.