AI Risk Registers: A Practical Starting Point
Use one entry per concrete failure scenario. Record the affected workflow, consequence, existing control, evidence, owner, and next review date.
Federal AI Governance
Good governance does not slow AI programs down. It gives agencies a practical approval path for use cases, data boundaries, auditability, and operating ownership so teams can move without improvising risk decisions every time.
- Governance has to exist inside delivery, not just inside policy documents.
- Low-risk and high-risk use cases need different paths.
- A working 90-day operating model is more useful than a perfect framework that nobody uses.
Federal agencies are under pressure from two directions at once. Leadership wants working AI capabilities, while risk, privacy, security, and procurement teams need assurance that those capabilities can be reviewed, governed, and defended. The agencies that move fastest are not the ones that ignore governance. They are the ones that turn governance into an operational path instead of a policy binder.
That is the real job of federal AI governance. It is not to slow teams down. It is to create a repeatable way to approve use cases, control data access, document decisions, and keep deployments inside acceptable mission and compliance boundaries.
What Federal AI Governance Has to Solve
In practice, agency governance programs have to answer a small set of concrete questions.
Inventory
What AI tools, copilots, automations, and experimental workflows are already in use across mission and back-office teams?
Risk Classification
Which use cases are low-risk internal assistance, and which ones shape decisions, touch sensitive data, or create public-trust consequences?
Authority
Who can approve, pause, or change the system, and what evidence is required at each stage of the lifecycle?
Governance also has to connect AI usage to identity, access rules, records boundaries, approved hosting environments, and audit expectations. Agencies do not need abstract frameworks alone. They need a delivery model that security, privacy, procurement, and mission owners can all work with.
Why Governance Efforts Stall
Most governance programs stall for organizational reasons, not technical ones.
- Governance is separated from the platform. Policies live in slide decks while the actual system has no embedded controls for access, logs, or review.
- Everything is treated as high risk. Low-risk internal use cases get forced through the same path as high-impact systems, so teams either avoid the process or stop proposing useful ideas.
- No single operating owner exists. Several offices have a stake, but nobody owns the end-to-end motion from review to live deployment.
- The program starts too late. Teams wait until the pilot is already underway, then try to bolt governance onto a design that was made without it.
The fastest agencies are usually the ones with the clearest approval path, not the loosest controls.
A Practical 90-Day Roadmap
The goal of a first governance phase is not perfection. It is a working operating model that real teams can understand and use.
30
Days 1–30: Inventory, Scope, and Risk Classification
Identify what is already in play, then separate low-risk internal assistance from systems that influence decisions, touch sensitive data, or create mission consequences.
60
Days 31–60: Define the Operating Model
Decide who reviews what, what evidence is required, how approvals and exceptions are handled, and which technical controls are mandatory at each risk tier.
90
Days 61–90: Put Governance Into Delivery
Run real use cases through the framework, refine templates, tighten controls, and remove unnecessary friction so the process becomes credible through use.
This is also when agencies should confirm the platform requirements that support governance: role-based access control, audit logging, model version traceability, approval workflows, and reporting that leadership can actually use.
What to Ask Before You Buy or Build
Whether an agency is evaluating internal development, a prime-led effort, or a commercial platform, the same questions matter:
- Can the system enforce role-based access control at the workflow level?
- Can every prompt, source retrieval, model response, and configuration change be audited?
- How are use cases classified, approved, and reviewed over time?
- What happens when a model changes, a connector fails, or source permissions shift?
- Who owns the operating model after the initial deployment?
Procurement teams should care about these questions as much as technical teams do. They are what separate a credible implementation path from another AI experiment that never survives internal review.
