Comment on FR Doc # 2026-12205

**Re: Basic Safeguarding of Data within Large Language Model Artificial Intelligence Systems (Clause 552.239-7001) — Docket GSA-GSAR-2026-0331** Ghost Mesh Inc. supports this clause's intent and writes to identify one structural gap — the absence of a verification mechanism — and to offer the architecture that closes it. We build and operate this capability in production today. **Who we are.** Ghost Mesh Inc.'s product, Anchorhold, makes AI system behavior provable and governable: (a) cryptographically signed, offline-verifiable records of every system action; (b) deterministic, non-model policy-enforcement points; (c) a name-constrained certificate hierarchy under which each supply-chain participant signs its own verifiable records; and (d) keep-local, sovereign inference where Government data never leaves the customer's boundary. This is the subject of a U.S. provisional patent application (patent pending; App. No. 64/092,678). **The gap.** The clause defines the right outcomes — data held "eyes off," not used to train other models, held under U.S. jurisdiction, deleted upon termination, free of covert modification — but proves only each one by contractor attestation. Attestation is an unverifiable promise about the past, and it cannot detect the risk in Question 5: a covert model change that alters outputs without changing the contracting entity. A contractor cannot attest to a change it has no means to detect. **Our recommendation.** Require these obligations be demonstrable through tamper-evident, independently verifiable records rather than attestation alone — a built, deployable capability, not a research aspiration: 1. **"Eyes off" → signed access records.** An append-only, signed event log the Contracting Officer can verify offline, without trusting the contractor's live systems — proof of access control that never exposes the data itself. 2. **Prohibited use → deterministic enforcement, not logging.** A record proves what happened; it doesn't prevent it. Non-model enforcement points stop a prohibited use (training, commingling, out-of-scope retention) at the moment it's attempted and emit a signed record that the control fired. 3. **U.S. jurisdiction / no foreign compulsion → keep-local inference, structurally.** If inference runs inside the Government's or contractor's own boundary and never transits a third-party model service, there is no foreign entity in a position to be compelled — the risk is removed by design, not by promise. Many LLM providers commenting on this rule cannot offer this, because their architecture requires data to leave the customer's boundary. 4. **Supply-chain flow-down → a certificate hierarchy, not forwarded promises.** Each participant signs its own records under a name-constrained sub-CA that chains to a common root, giving a prime real proof rather than a forwarded attestation — a design we have already built and operate. 5. **Covert model change (Question 5) and deletion → a signed provenance chain and deletion receipt.** Committing the model, weights, config, prompts, and RAG sources to a signed provenance chain makes covert change detectable on inspection, which is also what makes the clause's 7-day material-change notice achievable — detection must precede disclosure. The same mechanism produces a checkable deletion receipt at termination, replacing "certify deletion in writing." **On Unbiased AI Principles**, we take no position on substance; as an engineering matter, the principle is enforceable only if design inputs — training-data selection, fine-tuning, system prompts, RAG sources, configuration changes — are on the record and inspectable. **On feasibility and neutrality.** We raise our own architecture to show a "provable compliance" requirement is buildable — not to seek a sole-source outcome. We recommend GSA specify the property (tamper-evident, independently verifiable, boundary-resident where feasible) rather than any product, so multiple vendors can compete under an open specification. We would welcome the opportunity to contribute a reference specification. We appreciate the opportunity to comment and would value the opportunity to provide a technical briefing to GSA staff and to participate in the July 14 listening session. Respectfully submitted, Miguel Fernandez President & CEO, Ghost Mesh Inc. · Anchorhold miguel@anchorhold.io · 954-669-3067