# ITAR AI Workload Compliance Assessment for Defense Manufacturers > Source: https://netray.co/tools/itar-ai-workload-compliance-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your AI deployments across eight dimensions of ITAR exposure, from technical data classification and US persons access control to technology control plan coverage. ## Category aerospace-defense ## Tool **Kind:** assessment **Questions:** #### Have you formally classified whether any data feeding your AI systems constitutes ITAR-controlled technical data? **Help:** Technical data under ITAR includes information required for the design, development, production, or use of a defense article, which reaches far beyond drawings marked classified. **Options:** ##### Yes, every AI data source has been classified and documented **Score:** 3 ##### Yes, for our primary systems, with some sources not yet reviewed **Score:** 2 ##### We have discussed it informally but never documented a classification **Score:** 1 ##### No classification has ever been performed **Score:** 0 #### Does your AI infrastructure run entirely within a US-persons-controlled environment, with no foreign national administrative access? **Options:** ##### Yes, verified and documented, including vendor support staff **Score:** 3 ##### Yes for our own staff, but vendor or support access has not been verified **Score:** 2 ##### Uncertain; we have not specifically checked **Score:** 1 ##### No, or we know foreign national access exists somewhere in the chain **Score:** 0 #### Is your AI infrastructure physically and logically isolated from cloud regions or providers that could expose data outside US control? **Options:** ##### Yes, fully on-prem or in a dedicated GovCloud-equivalent environment **Score:** 3 ##### Mostly, with a documented and restricted cloud configuration **Score:** 2 ##### Using standard commercial cloud with region settings but no ITAR-specific controls **Score:** 1 ##### No isolation; standard multi-tenant commercial infrastructure **Score:** 0 #### Do you restrict AI model access by citizenship or export authorization at the identity layer, not just by network location? **Options:** ##### Yes, identity-based access control enforces citizenship status directly **Score:** 3 ##### Partially, network segmentation exists but identity checks do not verify citizenship **Score:** 2 ##### No specific control beyond general corporate access management **Score:** 1 ##### We have not considered this distinction **Score:** 0 #### Have you evaluated whether fine-tuning a model on ITAR technical data constitutes an export of that data to the model provider? **Help:** Sending technical data to a third-party model provider for fine-tuning or embedding, even a US-based one, raises real questions about who can access it and where processing occurs. **Options:** ##### Yes, formally reviewed with legal or export control counsel **Score:** 3 ##### Yes, informally considered but not formally documented **Score:** 2 ##### No, we have not specifically evaluated this scenario **Score:** 0 ##### We are actively fine-tuning on this data without having considered it **Score:** 0 #### Is your GPU infrastructure and any remote vendor support covered by appropriate export-compliant agreements? **Options:** ##### Yes, all infrastructure and support agreements are reviewed and compliant **Score:** 3 ##### Mostly, with a few vendor relationships not yet formally reviewed **Score:** 2 ##### Uncertain; agreements exist but have not been reviewed for this purpose **Score:** 1 ##### No review has been performed **Score:** 0 #### Do you maintain a technology control plan that explicitly addresses AI and ML systems? **Options:** ##### Yes, our TCP explicitly covers AI systems, data flows, and access controls **Score:** 3 ##### Yes, a TCP exists but does not specifically address AI systems **Score:** 2 ##### A TCP is in development **Score:** 1 ##### No technology control plan exists **Score:** 0 #### Can you produce an audit trail proving no ITAR-controlled data left the authorized boundary through an AI system? **Options:** ##### Yes, complete logging ties every AI data flow to an authorized boundary **Score:** 3 ##### Partially, some logging exists but coverage has gaps **Score:** 2 ##### We believe this is true but could not prove it quickly **Score:** 1 ##### No, we could not produce this evidence **Score:** 0 **Bands:** #### Significant ITAR exposure: treat this as urgent **Min:** 0 **Max:** 29 **Verdict:** You have not classified whether ITAR technical data is flowing through your AI systems, and you could not currently prove it is not. For a defense manufacturer, an unreviewed AI workload touching technical data is a genuine export control violation risk, not a theoretical one, and it can jeopardize existing contracts and future eligibility. **Recommendations:** - Freeze any AI workload with potential access to technical data until a formal classification is completed. - Engage export control counsel to review your current AI data flows this month. - Inventory every AI system, including embedded copilots in commercial software, that could touch technical data. - Begin drafting a technology control plan addendum that explicitly names your AI systems. - Move any confirmed ITAR-adjacent AI workload to infrastructure you fully control while the review completes. #### Partial controls, meaningful gaps remain **Min:** 30 **Max:** 54 **Verdict:** Some classification and access control work has been done, but coverage is incomplete, particularly around vendor and support staff access, and around whether fine-tuning constitutes a data export. These are exactly the gaps assessors and primes probe for. **Recommendations:** - Extend citizenship-based access control from network segmentation to the identity layer. - Formally document whether fine-tuning or embedding pipelines constitute an export to your model provider. - Review every vendor and support contract touching AI infrastructure for export compliance. - Close the audit trail gap so you can prove boundary containment quickly if asked. - Update your technology control plan to explicitly name AI and ML systems rather than treating them as covered by inference. #### Reasonably compliant, tighten evidence and edge cases **Min:** 55 **Max:** 79 **Verdict:** Your core controls, infrastructure isolation, TCP coverage, and access restriction, are in reasonably good shape. The remaining gap is usually audit evidence speed and coverage of edge cases like vendor support access and newer AI tools added after the original review. **Recommendations:** - Automate audit trail generation so boundary-containment evidence does not require manual reconstruction. - Re-review vendor support access whenever a support contract renews or changes hands. - Extend the same classification discipline to any new AI tool before it is adopted, not after. - Run a mock assessment against your own TCP to find gaps before an actual assessor does. - Confirm identity-layer citizenship controls cover every AI system, including newly added ones. #### Strong ITAR compliance posture for AI workloads **Min:** 80 **Max:** 100 **Verdict:** You have classified your data, isolated your infrastructure, controlled access at the identity layer, documented your technology control plan for AI, and can produce audit evidence quickly. This is a defensible posture that will hold up under a DDTC compliance review or a prime contractor's supplier assessment. **Recommendations:** - Keep the technology control plan current as new AI capabilities and vendors are added. - Periodically re-verify vendor support access, since staffing and subcontracting arrangements change. - Use your compliance posture as a differentiator when competing for contracts with strict flow-down requirements. - Extend classification discipline to agentic AI systems, which create new data flows an older review may not have anticipated. - Share your audit evidence process across programs so it is not dependent on a single person's knowledge. ## Content **Intro:** This free ITAR AI workload compliance assessment scores your AI deployments across eight dimensions specific to defense manufacturers operating under export control obligations. It is built for facility security officers, export control compliance leads, and IT directors at companies handling technical data related to defense articles. Answer eight questions covering data classification, US persons access, infrastructure isolation, fine-tuning implications, and technology control plan coverage, and get a scored risk band with prioritized remediation steps. AI adoption is outpacing export control review at most defense manufacturers, and an unreviewed copilot or fine-tuning pipeline touching technical data is a real, not theoretical, compliance gap. **Sections:** #### Why AI creates a new category of ITAR exposure **Body:** Technical data under ITAR covers information required for the design, development, production, or use of a defense article, a definition broad enough to include engineering drawings, test data, source code for controlled systems, and manufacturing process specifications. AI systems ingest exactly this kind of content for retrieval-augmented generation, fine-tuning, and embedding, often through pipelines built by engineering teams focused on functionality rather than export control. A cloud AI API processing that content, even briefly, can constitute a deemed export if the provider's infrastructure or support staff include non-US persons, regardless of intent. **Bullets:** - Fine-tuning on technical data sends that data to the model provider's training infrastructure, which may not be US-persons-only. - Cloud AI support and infrastructure staff are rarely vetted for ITAR purposes by default. - RAG systems that index engineering documentation create a new, often unreviewed, data flow. - Agentic AI systems that call external APIs can inadvertently transmit technical data as part of tool use. #### The eight risks this assessment measures **Body:** Each question maps to a specific ITAR and CMMC control area: data classification, US persons infrastructure access, physical and logical isolation, identity-layer citizenship enforcement, fine-tuning export analysis, vendor agreement compliance, technology control plan coverage, and audit evidence production. These map closely to the questions a DDTC compliance review or a prime contractor's supplier security assessment will ask, and answering them proactively is dramatically cheaper than answering them during an investigation. #### How to read your score **Body:** Data classification is the foundation every other control depends on, so a low score there should be the first thing you fix regardless of your overall band. You cannot correctly scope infrastructure isolation or access control until you know which data flows are actually ITAR-controlled. The fine-tuning question deserves particular attention because it is the newest and least understood risk area: many teams have not considered that sending technical data to a third-party model provider for fine-tuning, even a well-regarded US company, raises the same export questions as sending it to any other subcontractor. #### How Netray builds ITAR-compliant AI infrastructure **Body:** Netray designs on-prem AI infrastructure specifically for aerospace and defense manufacturers where ITAR and CMMC compliance is a condition of doing business, not an afterthought. We deploy open-weight models entirely inside your authorized boundary, with no data ever reaching a third-party model provider, build identity-layer access controls tied to citizenship and export authorization, and produce the audit evidence your compliance team and your customers' supplier security teams will actually ask for. We also help update technology control plans to explicitly cover AI systems, closing a gap most defense manufacturers currently have. **Cta:** Get an ITAR compliance review of your AI workloads and a remediation plan mapped to your technology control plan. **Faq:** #### Does using a US-based AI vendor automatically satisfy ITAR requirements? **Answer:** No. A vendor being US-based does not guarantee their infrastructure, support staff, or subcontractors are entirely US persons, and it does not guarantee your data stays within the specific authorized boundary your program requires. You need contractual and technical verification of infrastructure location, support staff citizenship, and subprocessor use, the same diligence you would apply to any other subcontractor touching technical data. #### Is fine-tuning a model on our own technical data considered an export? **Answer:** It can be, depending on where the fine-tuning occurs and who can access the data during that process. Sending technical data to a third-party model provider's training infrastructure, even a domestic one, may expose it to personnel or systems outside your authorized boundary. This is an area where formal review with export control counsel is warranted before starting a fine-tuning project on technical data, rather than assuming a US vendor makes the question moot. #### Do we need a separate technology control plan for AI systems? **Answer:** Not necessarily a separate plan, but your existing technology control plan should explicitly address AI and ML systems as a named category, covering data flows, access controls, and boundary definitions specific to how AI ingests and processes technical data. Many technology control plans predate AI adoption and implicitly assume data flows that no longer match reality once a RAG system or copilot has been added to the environment. #### What is the fastest way to reduce ITAR exposure in an existing AI deployment? **Answer:** Start by classifying exactly which data sources feed your AI systems and whether any constitute technical data. That classification determines everything else: whether infrastructure isolation is required, whether identity-layer citizenship controls are needed, and whether fine-tuning pipelines need to move on-prem. Skipping classification and jumping straight to infrastructure changes often means redoing the work once the actual data flows are understood. ## Search terms ITAR AI compliance assessment, ITAR AI workloads, CMMC AI compliance, export controlled AI data, US persons AI access control, defense contractor AI compliance, technology control plan AI, ITAR technical data AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json