An AI Governance Framework for Manufacturers: Practical Controls, Not Paper
An AI governance framework for a manufacturer is the set of policies, technical controls, and audit mechanisms that determine which AI systems may touch which data, who approves their actions, and how their outputs are verified. It matters now because AI-generated content is entering quality records, quotes, and ERP transactions - domains where AS9100D, CMMC 2.0, and customer flow-down clauses already impose traceability obligations. Effective governance is four layers deep: an acceptable-use policy, a data classification gate, human-in-the-loop rules for consequential actions, and logging that survives an auditor. It can be stood up in 60 days, not a year.
Layer 1: Acceptable Use and Data Classification
Start by classifying data into tiers and binding each tier to permitted AI systems. A workable manufacturing scheme has four tiers: public marketing content, internal business data, customer-confidential data under NDA flow-downs, and controlled data - CUI, ITAR technical data, export-controlled drawings. Commercial AI tools may be permitted for tier one, on-prem or FedRAMP systems required for tiers three and four. Write the policy in one page employees actually read, with concrete examples: pasting a customer drawing into a consumer chatbot is a reportable data spill, not a productivity hack. Enforce technically, not just on paper - DLP rules and DNS filtering for unsanctioned AI endpoints on managed devices. Anthropic, OpenAI, and Microsoft all publish enterprise data-handling terms; your policy should name which offerings are sanctioned and for what tier.
Layer 2: Model Risk Controls and Human-in-the-Loop Rules
Not all AI actions carry equal risk, and governance should scale with consequence rather than treating a summarization request like a purchase-order release. Define action classes and bind approval requirements to each - this is the manufacturing equivalent of NIST AI RMF's map-measure-manage cycle, applied at workflow level.
- Read-only actions (status queries, document search): automated, logged, no approval required
- Draft actions (quotes, reports, quality-record text): AI drafts, a named human approves before release
- Transactional actions (ERP writes, PO changes): human approval plus dollar-threshold escalation rules
- Prohibited actions: AI may never alter certified quality records, sign-offs, or export-control markings
Layer 3: Audit Trails That Satisfy AS9100D and CMMC Assessors
Auditors do not accept we use AI carefully; they accept evidence. Every AI interaction touching regulated processes needs an immutable record answering who, what, which model, which sources, and who approved. This aligns with AS9100D clause 7.5 documented-information requirements and the audit control family in NIST SP 800-171.
- Log every prompt, retrieval source, model version, and response with user identity to your SIEM
- Record human approvals on draft and transactional actions with timestamp and approver ID
- Version-pin models and keep evaluation results per version, so you can show output was regression-tested
- Retain AI interaction logs on the same schedule as the quality records they touch - seven-plus years in aerospace
Standing It Up in 60 Days With Netray
Netray implements this framework as part of every AI deployment rather than as a separate consulting exercise. Our on-prem stack ships with the technical layer built in: SSO-enforced access mapped to data tiers, retrieval filtering that respects document classifications and ITAR markings, action-class approval workflows in every agent, and SIEM-integrated logging with model version pinning. On the policy side, we deliver templated acceptable-use policies, data-tier definitions, and the SSP and data-flow artifacts assessors request. Clients have taken AI systems through CMMC 2.0 Level 2 assessments and AS9100D surveillance audits with zero AI-related findings, and governance setup adds roughly two weeks to a deployment - not the six months a standalone governance program consumes.
Frequently Asked Questions
What should an AI governance framework include for a manufacturer?
Four layers: an acceptable-use policy bound to data classification tiers (public, internal, customer-confidential, controlled/CUI); model risk controls that scale approval requirements by action class from read-only queries to ERP transactions; human-in-the-loop rules for consequential outputs like quotes and quality records; and immutable audit logging capturing user, prompt, model version, sources, and approver. Technical enforcement - DLP, SSO, retrieval filtering - matters more than policy documents alone.
Does AS9100D apply to AI-generated content?
AS9100D does not name AI, but its clause 7.5 requirements for documented information apply fully to AI-generated content entering quality records, work instructions, or certifications. If AI drafts text that becomes a quality record, you need traceability: which model produced it, from what sources, and which qualified human reviewed and approved it. Registrars are beginning to probe AI usage in surveillance audits, so approval records and retained logs are the safe posture.
How do you audit AI systems for compliance?
Audit-ready AI systems log every interaction - user identity, prompt, retrieved sources, model version, response, and any human approval - to a tamper-evident store such as a SIEM, retained on the same schedule as the business records involved. Model versions are pinned, and each version has documented evaluation results proving regression testing. For CMMC, these controls map to the NIST SP 800-171 audit and accountability family; assessors want data-flow diagrams showing AI endpoints inside the assessed boundary.
Key Takeaways
- 1Layer 1: Acceptable Use and Data Classification: Start by classifying data into tiers and binding each tier to permitted AI systems. A workable manufacturing scheme has four tiers: public marketing content, internal business data, customer-confidential data under NDA flow-downs, and controlled data - CUI, ITAR technical data, export-controlled drawings.
- 2Layer 2: Model Risk Controls and Human-in-the-Loop Rules: Not all AI actions carry equal risk, and governance should scale with consequence rather than treating a summarization request like a purchase-order release. Define action classes and bind approval requirements to each - this is the manufacturing equivalent of NIST AI RMF's map-measure-manage cycle, applied at workflow level..
- 3Layer 3: Audit Trails That Satisfy AS9100D and CMMC Assessors: Auditors do not accept we use AI carefully; they accept evidence. Every AI interaction touching regulated processes needs an immutable record answering who, what, which model, which sources, and who approved.
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