If you sell software to the United States federal government, or to any agency that follows CISA guidance, you have already seen the Secure Software Development Attestation Form. It is short in page count and surprisingly heavy in consequence. A CEO or equivalent officer signs a statement that your organization develops software in conformance with specific practices from NIST Special Publication 800-218, the Secure Software Development Framework. A false attestation is a False Claims Act exposure. So the form is not a marketing exercise. It is a formal representation backed by evidence that an agency can demand at any time.
This post is about what that evidence looks like in practice, and about why two different categories of AI-powered security tools produce very different experiences when it comes time to sign. Griffin AI, the compliance-aware agent behind Safeguard, is built to produce the structured output auditors and acquisition officers expect. Mythos-class pure-LLM tools, which rely on a single large model and free-form reasoning, produce chat transcripts. Those two artifacts are not interchangeable.
What the SSDF attestation actually asks
The attestation references four high-level SSDF practice groups: Prepare the Organization, Protect the Software, Produce Well-Secured Software, and Respond to Vulnerabilities. Inside those groups, the specific tasks the form cares about include PO.3 (secure development environments), PS.1 and PS.2 (protecting source code and verifying third-party components), PS.3 (archiving and protecting each release), PW.4 (reusing existing, well-secured software), PW.7 and PW.8 (reviewing and testing code), and RV.1 and RV.2 (identifying and responding to vulnerabilities).
An agency reviewer who wants to verify any of these tasks expects the same shape of evidence every time. They want a named control, a named owner, a repeatable procedure, a dated artifact that the procedure produced, and a traceable link back to the system under attestation. That is it. They are not reading prose. They are reconciling claims to artifacts, and they stop reading the moment the claim stops being reconcilable.
Griffin AI: attestation-shaped output by design
Griffin AI approaches SSDF as a first-class data model, not as a template filled in after the fact. When Safeguard performs a scan against a repository or an SBOM, Griffin produces findings that carry structured metadata explicitly keyed to SSDF tasks. A dependency flagged under a CVE maps to RV.1.1 (gather vulnerability information) and RV.2.1 (analyze and prioritize). A blocked merge that required signed commits maps to PO.3.2. A reproducible build artifact maps to PS.3.1. An SBOM generated at build time maps to PS.3.2.
The practical effect is that the attestation workflow becomes a collation task instead of a creation task. Pull the SSDF report out of Safeguard, and each task line item has an evidence bundle attached: the pipeline job ID, the policy that ran, the policy version hash, the inputs it consumed, the decision it produced, the timestamp, the operator identity, and the cryptographic signature over the record. When the agency asks how you verify third-party components, you hand them the PS.2 bundle. It is a finite, reviewable document.
Griffin is also aware of the common auditor follow-ups. Scope questions, retention questions, and change-control questions are answered by linking the SSDF record to the underlying project, product, and tenant configuration in Safeguard. Nothing has to be reconstructed by a human after the fact.
Mythos-class tools: transcripts, not evidence
Mythos-class tools are built around a single large model that answers questions in natural language. That is a great experience for a developer asking "what is wrong with this package?" It is not a great experience for an auditor asking "show me that you performed PW.7 code review for release 4.2.1 on April 18, 2026, using a reviewer other than the author, with the review content retained."
Ask a pure-LLM tool that question and you get a paragraph. The paragraph may be factually accurate. It may reference the right review. It is still a paragraph, and the paragraph does not carry the artifacts. There is no linked commit hash for the merge. There is no policy version. There is no signed record that the tool itself was run under controlled conditions. There is also nothing stopping the tool from producing a slightly different paragraph the next time the same question is asked, because LLM output is not guaranteed to be deterministic.
This is not a criticism of the models. It is a statement about the artifact type. An attestation workflow wants rows in a table. A chat transcript is prose.
A concrete walk-through: PS.2 third-party verification
Consider SSDF task PS.2.1, which requires that organizations verify that third-party components comply with requirements before they are incorporated. In a Safeguard deployment, verification is a policy gate that runs on every pull request touching a manifest. The gate ingests the manifest, evaluates each component against license, provenance, vulnerability, and known-malicious lists, and emits a structured decision.
The Griffin AI attestation bundle for PS.2.1 therefore includes the repository identifier, the pull request identifier, the manifest hash, the component list, the per-component verdicts, the policy definition, the policy version, and the signed decision record. An auditor can walk from the signed attestation statement down to the individual verdict on a specific package in under a minute.
A Mythos-class tool will tell the auditor, in fluent English, that third-party components were verified. It may even name some components. What it will not do is produce the per-component verdict archive, because it does not have one. The verification is an event that happened in the model's reasoning at query time, not a persisted decision record tied to a specific merge.
Determinism and reproducibility
SSDF does not explicitly require determinism, but several tasks get much harder without it. RV.2.1 asks you to analyze and prioritize vulnerabilities. If you rerun that analysis a week later with the same inputs and get materially different output, an auditor will ask why, and you will not have a good answer. Griffin AI addresses this through policy versioning. The same policy version against the same inputs produces the same decisions, and the policy version is part of the evidence bundle. When the policy changes, the change is tracked, and prior decisions remain valid for the windows in which they were made.
Pure-LLM tools generally do not version their reasoning. Model updates and prompt changes shift outputs in ways that are hard to characterize. That is acceptable for exploratory analysis and unacceptable for attestation-grade evidence.
Preparing for the request letter
The realistic scenario for a vendor is not that an agency rejects the attestation at submission. It is that the attestation is accepted, and then months later a contracting officer sends a letter asking for evidence of conformance to specific tasks, usually after an incident elsewhere has made the topic newsworthy. The organizations that respond well to those letters are the ones whose evidence was captured at the time the work was done, indexed to the right taxonomy, and retained under policy.
Griffin AI produces that kind of evidence on a continuous basis. The attestation is not a special event; it is the system's steady state. Mythos-class tools leave you in a position where, on the day the letter arrives, you have to reconstruct the attestation from logs, interviews, and screenshots. That reconstruction is almost always incomplete, and incomplete reconstructions are what turn an inquiry into a finding.
The practical recommendation
If SSDF attestation matters to your business, choose tooling that treats the framework as a schema, not a summary. Auditors and acquisition officers are not hostile. They simply have a finite amount of time and a well-defined set of boxes to check. Griffin AI fills those boxes with evidence that stands up to scrutiny. Mythos-class tools, however capable they are in conversation, do not produce artifacts in the shape an attestation review needs. The gap is not about intelligence. It is about the form of the output.