# GSA's Draft AI Clause and What It Demands Architecturally

> Source: https://ibl.ai/resources/guides/gsa-ai-clause-552-239-7001-data-rights
> Last updated: 2026-08-19


*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*

Reading time: 13 min read | Difficulty: advanced

**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.**

## How do you gSA's Draft AI Clause and What It Demands Architecturally?

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.

## Prerequisites

- **Know which of your contracts are MAS:** 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.
- **An inventory of AI systems used in performance:** 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.
- **Your current data-handling position in writing:** 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.
- **Model provenance for everything you deploy:** 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.

## Step 1: Map each obligation to the architecture that can satisfy it

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.

- [ ] No training on Government Data — 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.
- [ ] Segregation of Government Data from commercial data — 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.
- [ ] Deletion at contract conclusion, certified in writing — 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.
- [ ] Government ownership of data inputs, outputs and Custom Developments — This sits awkwardly with platforms whose vendor retains all rights. A perpetual source licence resolves it directly.
- [ ] Disclosure of AI systems used, within 30 days of award — Answerable precisely when you hold the source and the model inventory; approximate when the stack is a vendor's black box.
- [ ] Foreign-regulatory modification disclosure and the American AI direction — Requires model provenance you can actually establish rather than infer from a vendor datasheet.

**Tips:**
- The pattern across all six: obligations that are contractual assurances in a hosted deployment become verifiable facts in an owned one.

## Step 2: Enumerate every AI system actually used in performance

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.

- [ ] Direct AI systems delivered under the contract
- [ ] AI features embedded in tools used to perform the work
- [ ] Subcontractor and service-provider AI systems
- [ ] Models reached indirectly through an intermediary API

## Step 3: Test whether you could certify deletion today

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.

- [ ] Identify every store that would hold Government Data — Prompts and completions, retrieval indexes, vector embeddings, application logs, and backups.
- [ ] Establish who has visibility into each store
- [ ] Determine who is contractually able to certify deletion in writing
- [ ] Confirm the retention position for derived artifacts such as embeddings

**Tips:**
- Embeddings derived from Government Data are the store most often forgotten, and they are the hardest to reason about after the fact.

## Step 4: Establish model provenance before you are asked

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.

- [ ] List every model that could serve a request, including fallbacks
- [ ] Record the origin and licence of each model
- [ ] Document any modification made to satisfy a non-US regulatory regime
- [ ] Establish how you would prove each answer if challenged

## Step 5: Comment, and plan for the version that is adopted

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.

- [ ] Track the rulemaking and the eventual MAS Refresh that would carry it
- [ ] Assess exposure against the obligations, not the draft's exact language
- [ ] Prefer architectures that satisfy the requirements by construction — Those positions remain correct regardless of how the final clause is worded.

## Common Mistakes

### Treating the draft clause as already in effect

**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.

### Assuming the disclosure covers only the AI product being sold

**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.

### Relying on a vendor's assurance that Government Data is not used for training

**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.

### Forgetting derived artifacts when planning deletion

**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.

## FAQ

**Q: Is GSAR 552.239-7001 in effect?**

No. GSA published it as a draft clause on March 6, 2026 and extended the comment period to April 3, 2026. If adopted it would be implemented in the GSAR and incorporated into Multiple Award Schedule contracts through a later Refresh.

**Q: What would the GSA AI clause require of contractors?**

As drafted: disclosure of any AI system used in performing the contract within 30 days of award, including whether it was modified for a foreign regulatory framework; government ownership of data inputs, outputs and Custom Developments; a prohibition on using Government Data to train or improve AI systems; and segregation, deletion and written certification of deletion of Government Data.

**Q: Can a managed AI service satisfy the draft's data-rights provisions?**

Some of them, contractually. The difficulty is evidentiary: no-training commitments, segregation in multi-tenant infrastructure, and certified deletion across a vendor's logs, caches, embeddings and backups are assurances the agency relays rather than facts it can verify.

**Q: How does a self-hosted deployment change the analysis?**

It converts the same obligations into verifiable properties. If inference runs inside the agency perimeter, no external party is positioned to train on Government Data, segregation is physical rather than logical, and the agency certifies deletion from its own systems.

**Q: What does the American AI direction mean in practice?**

It makes model provenance a contract requirement rather than an architectural preference. You need to know the origin and licence of every model that could serve a request, including fallbacks, and to document any modification made for a non-US regulatory regime.

**Q: How does ibl.ai fit in?**

ibl.ai runs entirely inside the agency perimeter — on-premise, in GovCloud, or fully air-gapped — and ships with the full source code under a perpetual licence. Government Data never reaches a vendor, so no external party can train on it, segregation is physical, the agency certifies its own deletion, and Custom Developments stay with the agency because you own all the code and the data.


## Can you do this on infrastructure you own?

**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.**

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** 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.