Why I built this
Throughout my career, I've been drawn to one challenge: turning governance principles into practical
execution. As I moved into data science and AI, I found myself asking the same question in a new context:
how do we turn responsible AI guidance into everyday practice?
AI doesn't have a guidance problem — we have strong foundations in NIST, ISO/IEC, OWASP, the EU AI
Act, HIPAA, GDPR, OECD, and more. The challenge is translating that guidance into requirements, risk
assessments, design reviews, testing, deployment gates, and monitoring that teams can actually use. This
playbook is my contribution to closing that gap — curating, connecting, and translating
existing guidance into practical delivery practices, while crediting the organizations behind it.
Responsible AI shouldn't live only in policy documents — it should be woven into everyday
practice.
Use: This public prototype is shared for demonstration, feedback, and discussion. Selected examples
illustrate the approach; implementation materials, customization, and broader toolkit access are not
granted through publication of this site.
Put the Playbook to Work
Every organization has a different AI risk profile, operating model, regulatory environment, and level of
maturity. The public playbook demonstrates the framework and selected examples; implementation can be
adapted to the context of a specific organization or AI initiative.
- Apply the framework to an AI initiative or portfolio
- Assess gaps in an existing AI delivery lifecycle
- Tailor governance, risk, evidence, and decision controls
- Develop fit-for-purpose implementation artifacts and operating practices
- Facilitate AI delivery and governance working sessions
Interested in exploring how the playbook could apply in your environment?
Let's Talk →
Or reach out directly: mfebus@gmail.com
