AI Governance · Case Study
AI governance for a CUI-handling SaaS environment
From shadow AI across product teams to a documented use-case register, NIST AI RMF-aligned policy, and dev-tool guardrails that withstood AO sign-off.
Federal SaaS provider · ~120 staff · CUI-handling workload supporting multiple agency tenants
Challenge
Product and engineering had quietly adopted Copilot, ChatGPT, Cursor, and three internal LLM features. There was no acceptable-use policy, no use-case register, no model card discipline, and no risk documentation. A pending ATO renewal flagged AI as an open governance question. Leadership knew they needed a defensible posture before the next AO interview, without grinding development to a halt.
Approach
- 1
Inventoried every active AI use across product, engineering, and ops, including external tool use and four internal features.
- 2
Drafted a tiered acceptable-use policy distinguishing exploratory, internal, and customer-facing AI uses, with data-handling rules per tier.
- 3
Built a lightweight use-case review workflow (one-page intake, two-person review, register entry) that did not bottleneck development.
- 4
Mapped governance posture to the NIST AI RMF Govern/Map/Measure/Manage functions and to the customer-facing AI sections of the ATO package.
- 5
Stood up an AI risk register and a model-card template for the four internal AI features.
- 6
Prepared the team for the AO interview on AI risk with rehearsals and an answer playbook.
“We thought we needed a year. They did it in nine weeks without grinding our team to a halt.”
VP of Product, Federal SaaS provider · Composite engagement
Outcomes
What the engagement actually shipped.
12
AI use cases documented in the register
0
unapproved AI production paths post-rollout
9 weeks
from kickoff to AO sign-off on AI risk
4
internal AI features with full model cards
Composite case study. Details combine multiple engagements and are anonymized for client confidentiality.
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