AI Model Governance: The Compliance Gap Nobody Talks About

Vibe coding gets you to the demo. Governed architecture gets you to defensible.

Most organizations deploying AI in legal and regulated environments can answer the question: are you using AI? What they cannot answer — and what regulators, auditors, and opposing counsel will increasingly ask — is a much harder set of questions.

• Which AI model processed my data?

• Where did that inference run geographically?

• What is the data retention policy for that model?

• When the model version changes, does your compliance documentation update?

Most teams have no answer. That is not a technology gap. That is a governance gap.

Why Model Governance Matters Right Now

Base44 Enterprise now supports manual model selection per workflow. Developers and legal AI implementers can choose between Claude Opus 4.7, Claude Sonnet 4.6, GPT-5.5, and Gemini 2.5 Pro depending on the complexity and sensitivity of each task. That capability is powerful. Without governance documentation, it is also a liability.

Consider who will be asking questions about your AI model decisions:

• FedRAMP assessors will ask which model processed controlled unclassified information (CUI) and whether it was authorized.

• CMMC auditors will ask whether your AI tool appears on the approved products list and whether DFARS flow-down clauses have been honored.

• State bar disciplinary boards will ask whether attorneys knew which AI tool their associates were using and whether it was competently supervised under ABA Rules 1.1 and 5.1.

• In litigation, opposing counsel will ask whether AI-generated work product was reviewed by a human before submission.

Model governance documentation answers all four questions before they are asked.

What an AI Model Registry Documents

For every workflow in a governed Base44 deployment, SavvyLex Consulting produces a formal model registry entry that captures:

• Workflow name and purpose (e.g., contract review — redlining)

• Model assigned and version (e.g., Claude Opus 4.7)

• Selection rationale (e.g., complex multi-step reasoning required for privileged document analysis)

• Data classification touched (attorney-client privileged / CUI / PII)

• Geographic inference location (US default / EU cluster)

• Data retention policy per model provider (Anthropic zero-retention option)

• Version change management process (quarterly review, change control ticket required)

• Fallback model designation and audit log reference

The FedRAMP Roadmap: From Base44 Enterprise to ATO

Base44 Enterprise provides the infrastructure floor. SavvyLex provides the governance layer on top. Together, they form an architecture-level secure deployment — not retrofitted after an incident. The five-phase roadmap from Base44 Enterprise to a FedRAMP Authority to Operate:

• Phase 1 — Document the Baseline (Months 1-2): Deploy on Base44 Enterprise, produce System Security Plan v0.1, produce AI model registry, produce shared responsibility matrix.

• Phase 2 — Gap Analysis (Months 2-3): Map ~325 NIST 800-53 Rev 5 controls, identify gaps in key management, personnel security, supply chain and incident response, produce Plan of Action and Milestones (POA&M).

• Phase 3 — Remediation (Months 3-8): Close policy and technical gaps, deploy AWS GovCloud hybrid layer for FedRAMP High or ITAR requirements, conduct internal readiness review.

• Phase 4 — 3PAO Assessment (Months 8-14): Engage accredited Third Party Assessment Organization, submit Security Assessment Package, remediate findings, receive ATO.

• Phase 5 — Continuous Monitoring (Post-ATO): Monthly vulnerability scanning, annual penetration testing, model version change management, quarterly access reviews, annual SSP reauthorization.

Realistic timeline: 12-18 months for FedRAMP Moderate, 18-24 months for FedRAMP High.

The SavvyLex Advantage

Most organizations start FedRAMP prep after they already built something messy. Reconstruction is expensive, slow, and often incomplete. SavvyLex builds FedRAMP-ready from the first line. When Phase 1 begins, you are documenting what already exists — not rebuilding what was built wrong. The audit becomes a confirmation, not a reconstruction.

If your organization is deploying AI in a regulated legal environment and you cannot answer the four questions at the top of this article, that is where we start.

Book a Strategy Call: https://savvylex-consulting.com/BookACall