Scenario: Regulated Manufacturing
Aerospace, Defense, and High-Consequence Industrial Environments
Environment Characteristics:
Organizations in aerospace, defense, automotive Tier 1, and heavy manufacturing operating under strict regulatory frameworks
(ITAR/EAR, AS9100, ISO TS 16949). Production environments are often air-gapped or heavily restricted.
Failure consequences include physical safety risks, national security implications, or multi-million dollar liability exposure.
Typical Use Cases:
• Manufacturing execution system (MES) LLM integration
• Quality control documentation analysis
• Engineering change order (ECO) processing
• Technical publication generation (classified or export-controlled)
• Predictive maintenance chat interfaces at plant edge
1. DATA LOCALITY / PRIVACY (Critical — 95% Weight)
Why Dominant: ITAR/EAR regulations prohibit data transfer outside controlled boundaries. Classified data (even CUI/FOUO) cannot touch commercial cloud APIs. Export-controlled technical data requires physical isolation.
Implication: API-only models are likely non-starters. Hybrid models require extreme data classification rigor and legal review of vendor contracts for export compliance.
2. GOVERNANCE / AUDITABILITY (Critical — 90% Weight)
Why Dominant: AS9100/ISO certifications require full traceability of data processing. Regulatory audits demand prompt/response logs with retention (often 7+ years). Model versioning must be provable.
Implication: On-premise gives you control over audit logs. API vendors may not provide adequate granularity or retention SLAs. Hybrid requires unified logging across both systems.
3. LATENCY CONTROL (High — 70% Weight)
Why Dominant: Plant-floor systems (MES, SCADA) require sub-second responses. Internet-dependent APIs introduce unacceptable variability in production-critical workflows.
Implication: Edge/on-prem deployment may be mandatory for real-time use cases. API fallback for non-critical workloads possible only if air-gap not required.
4. OPERATIONAL COMPLEXITY (Moderate — 50% Weight)
Why Considered: These environments already operate complex infrastructure (PLCs, MES, QMS). Adding ML operations is an extension, not a paradigm shift.
Implication: Ops complexity is acceptable if it enables compliance. However, under-resourced IT teams remain a risk.
5. COST PREDICTABILITY (Low — 30% Weight)
Why Lower: Regulatory compliance costs dwarf LLM infrastructure costs. A single ITAR violation fine ($500K+) exceeds typical on-prem hardware budgets.
Implication: CapEx is acceptable if it reduces compliance risk. Cost optimization is secondary to control.
1. Accidental Data Exfiltration
Scenario: Developer uses API for "testing" with sanitized data, but data still contains export-controlled metadata or CUI.
Consequence: ITAR violation, potential criminal liability, contract termination.
Mitigation: Network-level egress filtering, code review for API calls, mandatory data classification training.
2. Inadequate Audit Trail
Scenario: LLM system lacks prompt/response logging. During audit, cannot prove compliance with data handling requirements.
Consequence: Certification suspension, contract non-renewal, legal exposure.
Mitigation: Implement structured logging from day 1, test log retrieval before production, define retention policy aligned with regulations.
3. Model Update Breaks Validation
Scenario: On-prem model updated without re-validation. Output format changes break downstream QMS integrations.
Consequence: Production halt, non-conformance reports, customer quality holds.
Mitigation: Treat models as validated software. Require change control, regression testing, and documentation updates before deployment.
4. Insufficient Physical Security
Scenario: On-prem servers not in access-controlled datacenter. Unauthorized personnel gain physical access.
Consequence: Data breach, loss of ITAR compliance, facility security clearance revocation.
Mitigation: Colocate with existing secure infrastructure, implement badge access logs, conduct periodic security audits.
Pre-Deployment Verification Checklist
□ Regulatory Compliance
- Data classification policy covers LLM use cases
- Legal review confirms deployment model meets ITAR/EAR
- Audit log format meets AS9100/ISO requirements
- Data retention policy documented and implemented
□ Physical & Network Security
- Servers located in access-controlled facility
- Network segmentation prevents unauthorized egress
- Encryption at rest verified (FIPS 140-2 if required)
- Intrusion detection system (IDS) covers LLM subnet
□ Operational Readiness
- Change control process includes model updates
- Backup/recovery tested for model and data
- Incident response plan covers AI-specific scenarios
- Staff trained on classification and handling procedures
□ Performance Validation
- Latency tested under production load profiles
- Throughput verified against peak demand scenarios
- Failover behavior documented (if hybrid)
- Output quality validated against acceptance criteria
Based on the dominant constraints in this scenario, the following architectural patterns are most relevant:
- Air-Gapped Isolated Inference — For ITAR/classified environments. No external connectivity. See Architectures →
- Edge/Plant Deployment — For MES/SCADA integration with sub-second latency requirements. See Architectures →
- RAG with Internal-Only Documents — For quality/ECO workflows. Must verify no external embedding API calls. See Architectures →
This is not a recommendation. Based on the constraints typical of this scenario:
On-Premise Only is the most constraint-compatible option when:
• Data is ITAR/EAR-controlled or classified
• Auditors require full traceability and long retention
• Latency requirements are production-critical
• Physical security infrastructure already exists
Hybrid may be viable when:
• Data can be reliably classified (non-export-controlled subset exists)
• Legal confirms API vendor terms meet compliance
• Non-critical workloads can tolerate API latency
• Dual logging/ops complexity is acceptable
API-Only is typically incompatible when:
• Any data is export-controlled or classified
• Audit requirements exceed vendor capabilities
• Internet dependency creates safety/production risk
• Regulatory validation requires on-prem control
→ Your compliance officer and legal team must validate the final decision. This analysis provides constraint visibility only.
DECISION TOOLS FOR THIS SCENARIO