● Playbook
Enterprise AI Security Governance Playbook
A step-by-step operating model for standing up AI security governance that engineering can actually implement — inventory first, enforcement at the platform layer, risk-based tiering, and the metrics that prove it works.
This playbook turns AI governance from a document into a control plane. It is the sequence I use when standing up governance from nothing, ordered so that each step delivers visible risk reduction before the next begins.
Who this is for
Security leaders, enterprise architects, and platform owners responsible for making AI adoption safe at scale — particularly those who have a governance policy that nobody implements and want to fix the gap between paper and production.
The operating model in brief
Governance works as three layers. A short policy layer of enforceable constraints; a control layer where each constraint maps to an owned, enforced, measurable control; and a platform layer where controls are implemented as defaults so the secure path is the easy path. Governance that depends on constant human diligence fails at scale. Governance baked into the platform survives turnover and deadlines.
Phase 1 — See the landscape
- Build a live inventory of every model in use: first-party, third-party API, and embedded-in-SaaS, with the data each touches and a named owner.
- Classify the data flowing into AI systems at ingestion. Untagged data is the root cause of most AI data incidents.
- Do not write the policy yet. You cannot govern what you cannot see.
Phase 2 — Stop the worst outcomes
- Implement egress control for sensitive data at the network and gateway layer, so it cannot reach unapproved model endpoints regardless of developer diligence.
- Assign a named, accountable owner to every elevated-risk system.
- Define your risk tiers (low / elevated / high) and the control set each requires.
Phase 3 — Make it durable
- Move controls into the platform as defaults rather than per-team obligations.
- Write the ten enforceable constraints — and only those — now that you can see the landscape.
- Stand up the exception process. A program with zero exceptions is being bypassed, not respected.
Metrics to track
- Inventory coverage of production AI systems (target near 100%).
- Share of elevated-risk systems with named owners passing control checks.
- Violations detected and mean time to remediate.
- Open exceptions and how many are past review date.
How to use it
Run the phases in order. Resist the urge to start with the perfect policy — start with visibility, then enforcement, then durability. Adapt the tier definitions and the ten constraints to your regulatory environment and risk appetite.
Want this tailored to your stack?
I adapt these for specific environments and risk profiles as part of advisory and workshop engagements.