/ Stage 4: Governance & Responsibilty
Stage 4 of 7
Power without guardrails is a liability. This stage equips you to use AI responsibly understanding risks, building safeguards, and staying compliant in a rapidly evolving landscape.
The leading AI labs have developed safety practices that inform how we should all use AI. Understanding these approaches helps you evaluate tools, build safer workflows, and articulate AI risks to stakeholders.
Anthropic's public commitment defining safety standards and AI Safety Levels (ASL) that trigger enhanced safeguards. Foundational for understanding safety-first AI development.
How Claude is trained to be helpful, harmless, and honest. Understanding this helps you understand Claude's behaviors.
Anthropic is one of the first frontier AI labs to achieve ISO 42001 (international AI governance standard). Documents ethical, secure, accountable practices.
Independent assessment of safety practices across six leading AI companies. Useful for evaluating and comparing AI vendors.
Academic research on building safe and trustworthy AI. Good for deeper understanding of the field.
Know what AI is doing and why
Humans remain responsible for AI outputs
Protect data used in AI interactions
Monitor for bias and unequal outcomes
Treat AI systems as attack surfaces
The EU AI Act is the world's first comprehensive AI regulation, affecting any organization with EU customers or operations. Even if you're not directly subject to it, it's setting global standards that others are following.
Comprehensive resource with compliance checkers, summaries, and self-assessment tools. Start here to understand if and how the Act applies to you.
Legal firm's accessible guide to the risk-based framework (unacceptable, high, limited, minimal risk).
Privacy professionals' compliance matrix mapping requirements to business processes.
Official EU explanation of the regulation, timelines, and risk categories.
Social scoring, subliminal manipulation
Hiring, credit, medical, law enforcement
Chatbots, deepfakes
Spam filters, games
You need structured approaches to identify, assess, and mitigate AI risks. These frameworks give you vocabulary and methodology for responsible implementation.
U.S. government's voluntary framework organizing risk management into four functions: GOVERN, MAP, MEASURE, MANAGE. The most widely adopted framework.
Practical playbook with suggested actions for implementing each framework function.
Specialized profile addressing unique risks from generative AI systems.
Adversarial Threat Landscape for AI Systems. Maps 15 tactics and 66 techniques for attacking AI systems. Essential for security teams.
Enterprise perspective on AI risk with governance frameworks.
AI stating false information confidently
Unfair treatment of different groups
Sensitive info exposed through prompts
Malicious inputs manipulating AI
Humans failing to verify outputs
AI introduces new attack surfaces and privacy considerations. Protecting data in AI workflows is both a legal requirement and a trust imperative.
U.S. Cybersecurity & Infrastructure Security Agency's definitive guide with ten specific mitigations.
How GDPR principles (data minimization, transparency, fairness) apply to AI.
Official GDPR authority perspective on AI compliance.
Cybersecurity institute's guide to risk-based AI controls.
Mitigating data poisoning, model extraction, prompt injection.
Never share SSN, passwords, proprietary code in prompts
Especially before external communications
Use AI tools with data protection agreements
Maintain compliance and audit trails
AI security best practices for everyone
Different industries face different regulatory requirements. Find your sector below for targeted guidance.
Comprehensive guide to HIPAA requirements when using AI with Protected Health Information.
Business Associate Agreements, security controls, practical compliance.
Legal analysis of U.S., UK, and EU regulatory approaches for financial AI.
Policy recommendations from respected think tank.
Official American Bar Association guidance on AI ethics for lawyers.
State bar practical guidance for attorneys using generative AI.
How U.S. states are addressing AI ethics in legal practice.
Move from individual compliance to organizational governance. Build AI policies, implement monitoring, and establish accountability structures. For compliance officers, legal teams, security professionals, and leaders responsible for AI governance programs.
Open standard for documenting AI system components. Critical for supply chain transparency and EU AI Act compliance.
Practical guide to creating and implementing AI BOMs.
Open-source toolkit with 70+ fairness metrics and bias mitigation algorithms.
Open-source Python package for evaluating and improving ML fairness.
How to test AI systems for vulnerabilities and unexpected behaviors.
These exercises help you move from understanding AI governance in theory to implementing it in practice.
List every AI tool you use. For each, assess: What data do you share? Is it covered by regulations (GDPR, HIPAA)? Do you have approval? What happens if the output is wrong? Identify gaps needing attention.
Determine if the EU AI Act applies to you. Do you have EU customers? Visit the AI Act Compliance Checker, answer the questions, and note any high-risk applications needing compliance attention.
Draft simple AI usage guidelines: What tools are approved? What data can/cannot be shared? What outputs require human review? Who to contact with concerns? Even informal guidelines reduce risk.
Transparency, accountability, privacy, fairness, and security aren't just buzzwords; they're practical guidelines for safe AI use.
Risk categories, timelines, and whether it applies to you. You can participate in compliance conversations.
NIST AI RMF and MITRE ATLAS give you structured approaches to identifying and mitigating AI risks.
You know what not to share with AI and how to implement basic security practices.
Healthcare, finance, legal --- you know where to find specific compliance guidance for your sector.