GSAR 552.239-7001 is still a draft — but its data-rights provisions are answerable by architecture, and most managed AI offerings cannot answer them
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
Last updated:
On March 6, 2026, GSA published a draft contract clause — GSAR 552.239-7001, "Basic Safeguarding of Artificial Intelligence Systems" — that would impose wide-ranging obligations on contractors providing AI to the federal government.
It is important to be precise about status: this is a proposed clause released for comment, with the comment period extended to April 3, 2026. It is not in force. If adopted it would be implemented in the GSAR and incorporated into Multiple Award Schedule contracts through a later Refresh. Industry response has been substantial, including a formal submission from the U.S. Chamber of Commerce, and at least one analysis characterised the draft as governance by sledgehammer.
What makes it worth reading now, whatever its final form, is that its central provisions are not procedural. They are architectural.
The draft would give the government expansive ownership of data inputs, outputs, and any Custom Developments; prohibit contractors and service providers from using Government Data to train or improve their AI systems; require Government Data to be segregated from commercial data, deleted at contract conclusion, and certified as deleted in writing; and require disclosure within 30 days of award of any AI system used in performance, including whether it was modified to comply with a foreign government or commercial regulatory framework.
Each of those is easy to satisfy on infrastructure the agency controls and difficult to satisfy on infrastructure it does not. This guide maps the obligations to the deployment architectures that can actually meet them.
The draft clause is aimed at Multiple Award Schedule contracts and would arrive through a Refresh. Identify which vehicles would be affected before assessing exposure.
The disclosure obligation is broad — any AI system used in performing the contract. Subcontractor and embedded-feature usage counts, and most organizations have not enumerated it.
Where Government Data is processed, how it is segregated, how long it is retained, and who could certify deletion. If any answer is 'the vendor decides', that is the exposure.
The draft includes an American AI direction and a foreign-regulatory-modification disclosure. You cannot answer either without knowing what models and components are actually in the stack.
This is the analysis most contractors have not done. Every provision in the draft resolves differently depending on where inference runs and who controls the environment — and the differences are not close.
In managed SaaS this is a contractual promise you cannot inspect. When the model runs inside the agency perimeter there is no second party in a position to train on it.
In multi-tenant infrastructure this is enforced by application logic. In a single-tenant or self-hosted deployment the segregation is physical, because no other tenant exists.
Certification is only as good as visibility into the vendor's storage and backups. When the data never left agency systems, the agency certifies its own deletion.
This sits awkwardly with platforms whose vendor retains all rights. A perpetual source licence resolves it directly.
Answerable precisely when you hold the source and the model inventory; approximate when the stack is a vendor's black box.
Requires model provenance you can actually establish rather than infer from a vendor datasheet.
The disclosure obligation is broader than the AI product being sold. Embedded AI features in ordinary tooling, subcontractor systems, and models called indirectly through an API all sit inside a plain reading of the draft.
Written deletion certification is the provision most likely to be difficult in practice, because it requires knowing about every copy — including logs, caches, embeddings, and backups.
Prompts and completions, retrieval indexes, vector embeddings, application logs, and backups.
Both the American AI direction and the foreign-regulatory-modification disclosure require knowing what is in the stack. That is a supply-chain question, not a compliance form.
The draft is not final, and industry response has been substantial. Prepare for the obligations rather than for the current text, because the architectural questions survive redrafting even where specific wording does not.
Those positions remain correct regardless of how the final clause is worded.
Every core provision — no training on Government Data, segregation, certified deletion, ownership of outputs and Custom Developments — is a promise in a managed deployment and a verifiable property in a self-hosted one. That distinction is the whole compliance story.
A draft that assigns the government ownership of custom developments is difficult to reconcile with platforms whose vendor retains all rights to everything built on them. A perpetual source licence removes the conflict rather than negotiating it.
Certifying deletion in writing requires knowing about every copy, including logs, caches, embeddings and backups. Agencies can certify their own systems; they can only relay assurances about someone else's.
Any AI system used in performing the contract plausibly includes embedded features and subcontractor systems, which is where inadvertent non-compliance is most likely.
The specific wording will change. The architectural questions the draft raises — where data is processed, who can certify its deletion, who owns the outputs — will not.
Independent review of the inventory against tooling actually used on the contract
Trace a test record through prompts, retrieval indexes, embeddings, logs and backups
Confirm whether inference occurs inside the agency perimeter
Provenance record reviewed against the deployed model inventory including fallbacks
Consequence: Renegotiating contracts against language that will change, and alarming programme stakeholders unnecessarily.
Prevention: State the status accurately: proposed on March 6, 2026, comment period extended to April 3, not in force, and to arrive via a later MAS Refresh if adopted.
Consequence: An incomplete disclosure that omits embedded AI features and subcontractor systems.
Prevention: Enumerate every AI system used in performance, including tools used to do the work.
Consequence: An obligation you cannot verify and cannot evidence if challenged.
Prevention: Prefer an architecture in which no external party is in a position to train on the data at all.
Consequence: Certifying deletion while embeddings derived from Government Data persist in a retrieval index.
Prevention: Include vector stores, caches, logs and backups in the deletion inventory from the outset.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.