What Is AI Governance, and Why Does It Need a Mixing Board?
an introduction to AI governance through the lens of a recording studio's mixing board, and why every organization running AI needs someone with a hand on the faders
Part 1
Part 2
The EU AI Act in Plain English: The Venue's Noise Ordinance
breaking down the EU AI Act's risk-based structure in plain language, using the metaphor of a city's noise ordinance for live venues
Part 3
NIST AI RMF and Other Frameworks: House Rules for the Studio
how voluntary frameworks like the NIST AI Risk Management Framework function as house rules that guide responsible AI practice even without the force of law
Part 4
Risk-Tiering AI Systems: Which Channels Need a Limiter
a practical approach to sorting AI systems into risk tiers, and why not every channel on the board needs the same level of control
Part 5
Model Cards and Documentation: The Engineer's Channel Notes
why model cards function like an engineer's channel notes, capturing exactly what a system was built to do and where its limits are
Part 6
AI Governance Committees: Building the Production Team
why AI governance needs a cross-functional production team, not a single engineer working alone, and how to structure one
Part 7
Guardrails and Limiters: Keeping AI From Clipping
how technical guardrails function like compressors and limiters, catching an AI system's output before it distorts into something harmful
Part 8
Third-Party and Vendor AI Risk: Someone Else's Mix Feeding Your Board
why AI systems built by vendors and third parties introduce governance risk that an organization can't fully see or control
Part 9
AI Incident Response: Pulling the Fader Down Fast
why AI systems need a rehearsed incident response process, the way an engineer needs a reflex for pulling a fader down the instant something clips
Part 10
Auditing AI Systems: The Sound Check Before Showtime
why regular, structured audits function as the sound check that catches problems before an AI system ever faces a live audience
Part 11
Human-in-the-Loop: Who's Allowed to Touch the Faders
why keeping a human hand on the fader remains essential even as automated mixing tools get more capable
Part 12
AI Governance for Generative AI and LLMs: A New Instrument in the Mix
why large language models introduce governance challenges the mixing board's existing controls weren't originally built to handle
Part 13
Shadow AI: The Unauthorized Mic Someone Plugged In
why unsanctioned AI tools quietly adopted by individual teams create governance blind spots nobody signed off on
Part 14
Vendor AI Contracts: What to Put in the Rider
what specific terms belong in an AI vendor contract, treating it like the technical rider that sets expectations before a touring act arrives
Part 15
AI Governance Metrics: How Do You Know the Mix Is Balanced
what metrics actually indicate a healthy AI governance program, beyond simply feeling like things are under control
Part 16
Global AI Regulation: Touring Venues With Different Rules
how AI regulation varies meaningfully across countries, and what that means for organizations operating internationally
Part 17
Building an AI Governance Program From Scratch
a practical starting sequence for building an AI governance program when nothing formal exists yet
Part 18
AI Governance for Smaller Teams: A One-Person Booth
how a small team or solo founder can practice real AI governance without the resources of a large enterprise program
Part 19
Common AI Governance Failures (and the Feedback Squeals That Follow)
the recurring patterns behind AI governance breakdowns, and the predictable warning signs that show up before things go wrong
Part 20
The Future of AI Governance: Toward Automated Mixing
how AI governance itself is starting to be automated, and what that means for the human role at the board going forward