Data-Savvy Lawyers as Startup Guides to U.S. AI Policy, Export Controls, and Sanctions

National-security-driven AI regulation is operational, not abstract. In practice, “export” can mean granting repo access, sharing weights, or letting an overseas contractor see technical details — not just shipping hardware. Sanctions risk can show up in self-serve signups, support tickets, and reseller channels, where “services” may be the regulated activity. If you’re advising a startup, your job is to translate these rules into product and workflow controls that engineers can actually run every day.

Who this is for: AI founders and product leads building and shipping models, plus in-house or fractional counsel who need a pragmatic way to triage cross-border risk.

What you’ll get: a risk map, lightweight checklists, and contract/program building blocks you can implement quickly (and align with broader governance efforts like this AI governance playbook).

Scope note: this is for risk-spotting and program design; formal export classification and licensing decisions require tailored counsel.

TL;DR: The 6 decisions that keep startups out of national-security trouble

• Define what’s “exportable” in your AI stack. Inventory artifacts that can move across borders (weights, source code, fine-tuning data, eval suites, deployment tooling) and treat that list as a controlled asset register.
• Control and log access. Decide who can touch each artifact (including foreign nationals and offshore contractors), enforce least privilege, and keep audit-grade evidence (SSO/MFA, repo ACLs, access logs).
• Choose where you sell, support, and host. Set territory rules up front (geo-blocking, IP allow/deny lists, cloud region choices) and build reseller controls so third parties can’t route you into restricted markets.
• Operationalize screening. Screen customers/partners at signup, contracting, and meaningful support events; define what happens on a hit (pause, escalate, document, resolve).
• Flow compliance down to vendors. Put export/sanctions and access-location obligations into MSAs/subprocessor terms, plus audit/cooperation rights and suspension triggers.
• Assign an owner and produce evidence. Name a responsible lead, set review gates, and maintain a simple evidence folder (policies, logs, training) aligned with your broader governance stack (see AI governance playbook).

Build a simple “national-security risk map” of your AI system (what data-savvy lawyers do differently)

Start with artifacts, not statutes. List what exists (and where it lives): base weights, fine-tunes, training data, prompts/embeddings, source code, evals, MLOps pipelines, and deployment endpoints. Then sketch a single data-flow: collection → labeling → training → evaluation → deployment → monitoring, noting each storage location and system of record.

Next, highlight cross-border touchpoints: overseas engineers, offshore vendors, foreign customers, cloud regions, and any open-source or public releases. This is the lawyer’s shortcut to spotting export-control and sanctions exposure before a release, support engagement, or new hire turns into an “export.”

Example: a US startup hires an overseas contractor who needs access to fine-tuning code. Capture (i) the person’s role/citizenship/location, (ii) exact repos/branches, (iii) what artifacts are reachable, and (iv) access logs. Add controls: least-privilege permissions, segmented repos, time-bound access, and documented approvals.

Deliverable: a 1-page architecture + data-flow diagram that becomes your compliance backbone (and aligns with “Map” in NIST’s AI RMF).

Export controls in practice: spot the triggers before you ship code, weights, or know-how

Under the Export Administration Regulations (EAR), “export” is broader than shipping boxes. It can include releasing or transferring “technology” or source code to a foreign person in the U.S. (a “deemed export”). See 15 CFR § 734.13. BIS also summarizes this plainly: a deemed export is the release of controlled technology or source code to a foreign person within the United States.

• Common startup triggers: publishing weights/capabilities; hands-on technical assistance tied to sensitive end uses; cross-border R&D (accelerators, partners, offshore dev); and “cloud access” that effectively provides controlled software/technology to non-U.S. persons.
• Evidence to request: repo map, artifact inventory, ACLs, onboarding/offboarding workflow, change logs, and customer use-case/end-user statements.
• Open-source/foreign subsidiary scenario (pause/triage): What artifact is leaving? Who will access it (countries/citizenship)? Is it source code/technology? Is there a sensitive end use/end user? If any answer is “unclear,” pause release and escalate for classification/licensing advice.
• Practical controls: an export-control review gate in release management; repo segmentation + feature flags; and an end-use/end-user questionnaire for higher-risk enterprise deals.

Sanctions and restricted parties: design screening into sales, onboarding, and support

OFAC sanctions risk isn’t limited to “shipping.” For AI startups, the risky activity is often providing services — API access, hosted inference, model updates, or hands-on support — to blocked persons or sanctioned territories. Treat screening as a product and revenue workflow, not a one-time legal check.

• Where startups get caught: self-serve signups masked by VPNs; resellers/integrators that quietly route you to restricted end users; and developer communities issuing API keys/SDK access without identity checks.
• Minimal workflow: screen at lead, contract, renewal, and major support events. Screen the customer (and, where practical, beneficial owners and key counterparties). On a potential hit: pause service, escalate to a named reviewer, document the rationale, and only resume with a clear resolution.
• Example: parent company is clean, but a subsidiary operates in a sanctioned region. Options include denying service to that subsidiary, technically segmenting access (separate tenant, geo-blocking, no support), or escalating for legal review before any enablement.
• Recordkeeping: keep screening dates/results, match-resolution notes, who approved, any geo/IP signals, reseller certifications, and support-ticket logs tied to restricted-access decisions.

Aligning with U.S. government AI initiatives: become “procurement-ready” without enterprise bureaucracy

Federal AI expectations increasingly revolve around risk management, testing/evals, documentation, and incident response. Even if you never sell to an agency, enterprise customers (and primes) often mirror these requirements in security reviews and procurement checklists — so building the artifacts early reduces sales friction.

• Model documentation: intended use, prohibited uses, known limitations, eval results, and change history (versioned release notes).
• Data governance: data sources and rights, retention/deletion rules, and who can access training and fine-tuning data.
• Security baseline: MFA/SSO, logging, key management, and basic vendor/subprocessor risk review.
• Human oversight: escalation paths and review requirements for high-impact or sensitive use cases.

Example: a government-adjacent prime asks for a model card, eval summary, incident-response contacts, access-control evidence, and a subprocessor list. Assemble these quickly by maintaining a lightweight “procurement packet” tied to your internal AI governance docs (see AI governance playbook) and a shared legal/engineering vocabulary grounded in data science for lawyers.

Contract and product “guardrails” lawyers can implement quickly (clauses + product knobs)

Use contracts to force decisions the product can enforce. A fast pattern is: (1) clear compliance promises, (2) operational cooperation, and (3) a “stop-the-line” right when risk appears.

• Export/sanctions reps & covenants: each party will comply with U.S. export controls (EAR) and OFAC sanctions; no dealings with blocked persons/sanctioned territories; and cooperation on screening and licensing questions (OFAC programs overview: Treasury/OFAC).
• Restricted territory/end-use + notice: customer must not use the service for prohibited end uses or in restricted locations and must notify you if downstream use changes.
• Audit + suspension/termination: right to request certifications/logs and to suspend access immediately on a credible compliance concern (with cure/escalation mechanics).
• Subprocessor flow-down: vendors/subprocessors must accept equivalent export/sanctions, access-location, and confidentiality obligations, plus evidence production on request.
• Incident/notice: prompt notice of screening hits, law-enforcement/government inquiries, or suspected diversion.

Product knobs: geo-blocking/IP intelligence; KYC tiers for higher-risk features; RBAC + immutable logs for weights/code; API key issuance, rotation, anomaly detection.

Vendor fine-tuning scenario: limit vendor access to a segregated repo/workspace, prohibit offshoring without approval, require named personnel, and require delivery of access logs and subprocessor lists on demand.

A lightweight compliance program for AI startups (what to build in 30 days)

A “minimum viable” national-security compliance program is about clear ownership, repeatable gates, and evidence — not binders.

• Ownership: name a single accountable lead (often Legal/Ops), define an escalation path to outside counsel, and set a weekly 15-minute review cadence.
• Policies (MVS): (1) export-control review for releases/sharing (code, weights, technical help), (2) sanctions/restricted-party screening workflow, (3) vendor onboarding/subprocessor approval, and (4) data access + retention/deletion rules.
• Training: drill scenarios by function — Sales (resellers/territories), Support (service to restricted users), ML/Eng (repo access, open-source, cloud regions). Keep it short and scenario-based.
• Monitoring: quarterly checks of access logs, screening logs, and release gates; spot-check high-risk deals and vendor changes.
• Investor readiness: expect diligence on cross-border ops, foreign investors/board rights, sensitive customers, and whether screening/release gates are documented.

Series A scenario: diligence finds inconsistent screening. Remediate with a corrective action plan: freeze new high-risk onboarding, back-screen active customers, document exceptions, update the playbook, retrain the go-to-market team, and start an audit-ready log for every “hit/clear” decision.

Actionable Next Steps (do these this week)

• Create a 1-page AI system risk map. List the core artifacts (weights, code, datasets, evals) and mark every cross-border touchpoint (people, vendors, customers, cloud regions).
• Add a release gate. Before publishing weights, open-sourcing code, enabling a high-risk capability, or granting new access, require a short export-controls triage and sign-off.
• Turn on sanctions screening at key moments. Screen at signup (or API key issuance), at contract signature, and at renewal; define a “pause + escalate + document” playbook for potential hits.
• Update templates. Refresh your MSA and vendor/subprocessor terms with export/sanctions compliance clauses, restricted territory/end-use terms, audit/cooperation rights, and access/location restrictions.
• Stand up a 30-day evidence folder. Save your risk map, policies, training attendance, screening logs, release approvals, and access logs — so you can answer customer and investor questions fast.

Want a shortcut? Schedule a consultation with Promise Legal for a fixed-scope national-security AI compliance sprint to help you implement the gates, templates, and evidence set without slowing product velocity.