A Data Governance Framework for Private AI Deployments
A private AI data governance framework defines what data may enter your on-prem AI systems, who may query it, how outputs are controlled, and how everything is logged - before the first model is deployed. Without it, an on-prem LLM becomes an uncontrolled aggregation point: RAG pipelines happily index ITAR drawings next to HR files, and any employee with chat access can query both. For manufacturers under CMMC 2.0, DFARS 252.204-7012, or AS9100D quality regimes, governance is what turns 'we run AI locally' into a defensible compliance position. This framework covers classification, ingestion gates, access control, output handling, retention, and audit.
Classify Data Before It Touches the AI Stack
Every document entering an ingestion pipeline needs a classification label the AI stack can enforce. A workable manufacturing taxonomy has four tiers: Public, Internal, Confidential (pricing, contracts, financials), and Controlled (CUI, ITAR technical data, export-controlled drawings). The label must travel as machine-readable metadata - a field on the vector record and document store entry, not a footer in a Word file. Practically, this means the ingestion pipeline rejects unlabeled documents by default and derives labels from source systems: SyteLine document types, Infor Document Management attributes, SharePoint sensitivity labels via Microsoft Purview, or PLM item classifications from Teamcenter or Windchill. Classification-at-ingestion is the single control that prevents the most expensive governance failures downstream.
- Adopt a four-tier taxonomy: Public, Internal, Confidential, Controlled (CUI/ITAR) with machine-readable labels
- Reject unlabeled documents at the ingestion gate - default-deny beats cleanup after a spill
- Derive labels automatically from ERP document types, Purview sensitivity labels, and PLM classifications
- Store the label on every vector record so retrieval filters can enforce it at query time
Access Control: Retrieval Must Respect Source Permissions
The defining governance risk of RAG is permission collapse: documents with different ACLs merge into one index that answers to anyone. The fix is query-time permission trimming - every retrieval call carries the user's identity, and the vector database filters results to documents that user could open in the source system. Implement this with security groups synced from Entra ID or Active Directory into vector metadata, refreshed at least daily. For ITAR data, add a citizenship/authorization attribute check so foreign-person accounts can never retrieve Controlled-tier content, satisfying the ITAR deemed-export rule. Test with adversarial queries quarterly: a shop-floor account asking for executive compensation or program pricing should return nothing, and that test result belongs in your audit file.
Output Controls, Retention, and Prompt Logging
Governance does not stop at retrieval. Model outputs inherit the classification of the most sensitive input that produced them, so a completion built on Controlled documents is itself Controlled - your UI should label it and restrict copy/export accordingly. Log every prompt, retrieved chunk ID, and completion to append-only storage; this satisfies CMMC AU.L2-3.3.1 and gives you forensic reconstruction if a spill is suspected. Set retention deliberately: 12 months of full prompt logs is a common baseline, with Controlled-tier interactions retained per your DoD contract requirements. Decide explicitly whether prompts may be reused for fine-tuning - most regulated shops prohibit training on Controlled-tier interactions entirely, and that prohibition should be written policy, not tribal knowledge.
- Treat outputs as inheriting the highest classification of their inputs, and label them in the UI
- Log prompts, retrieved chunk IDs, and completions to immutable storage mapped to CMMC AU.L2 practices
- Retain interaction logs 12 months minimum; align Controlled-tier retention with contract clauses
- Put fine-tuning data reuse rules in written policy - default: never train on Controlled-tier interactions
Governance Roles and the AI Review Board
Frameworks fail without owners. Establish three roles: a data steward per business domain who approves what sources feed the AI stack, a platform owner in IT who enforces technical controls, and a compliance reviewer (often the FSO or CMMC lead) who signs off on new use cases touching Controlled data. A lightweight AI review board - these three roles meeting monthly - approves new ingestion sources and use cases with a one-page intake form: data sources, classification tiers, user population, and retention. This takes under two hours a month and produces exactly the paper trail a C3PAO assessor or DCMA auditor wants to see. Avoid heavyweight committee structures; governance that adds weeks of delay gets bypassed, and bypassed governance is worse than none.
How Netray Implements Governance Alongside Deployment
Netray builds governance into every private AI deployment rather than bolting it on after. Our engagements deliver the classification taxonomy wired into your ingestion pipeline, permission-trimmed retrieval synced with Entra ID, immutable prompt logging, and a control matrix pre-mapped to CMMC 2.0 and NIST SP 800-171 for your SSP. Netray AI agents operate within these guardrails - an agent automating SyteLine quote responses can only retrieve documents its invoking user could access. Clients typically reach a governance posture that passes internal audit in 4-6 weeks, and one defense supplier cleared their C3PAO assessment with zero AI-related findings using the control documentation Netray delivered.
Frequently Asked Questions
What is data governance for private AI?
Data governance for private AI is the set of policies and technical controls that determine what data can be ingested into on-prem AI systems, who can query it, how outputs are classified and retained, and how all activity is logged. Core components are a classification taxonomy enforced at ingestion, query-time permission trimming in retrieval, output labeling, immutable prompt logging, and named owners who approve new data sources and use cases.
How do I stop a RAG system from leaking sensitive documents?
Enforce permissions at query time, not just at ingestion. Store each document's access control list and classification label as metadata on its vector records, pass the user's identity with every retrieval call, and filter results to documents the user could open in the source system. Sync security groups from Active Directory or Entra ID daily, add citizenship checks for ITAR content, and run quarterly adversarial tests to prove the trimming works.
Does CMMC apply to on-prem AI systems?
Yes - if an on-prem AI system stores, processes, or transmits CUI, it is in scope for CMMC 2.0 and must meet the applicable NIST SP 800-171 controls. That includes access control (AC.L2), audit logging (AU.L2), and boundary protection (SC.L2) for the GPU servers, vector database, and applications. Keeping the AI stack inside your existing CUI enclave, with documented controls in your SSP, is the cleanest path to assessment.
Key Takeaways
- 1Classify Data Before It Touches the AI Stack: Every document entering an ingestion pipeline needs a classification label the AI stack can enforce. A workable manufacturing taxonomy has four tiers: Public, Internal, Confidential (pricing, contracts, financials), and Controlled (CUI, ITAR technical data, export-controlled drawings).
- 2Access Control: Retrieval Must Respect Source Permissions: The defining governance risk of RAG is permission collapse: documents with different ACLs merge into one index that answers to anyone. The fix is query-time permission trimming - every retrieval call carries the user's identity, and the vector database filters results to documents that user could open in the source system.
- 3Output Controls, Retention, and Prompt Logging: Governance does not stop at retrieval. Model outputs inherit the classification of the most sensitive input that produced them, so a completion built on Controlled documents is itself Controlled - your UI should label it and restrict copy/export accordingly.
Put this into numbers
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