Record integrity, codified
Part 11
21 CFR Part 11 and EU Annex 11 require electronic-record integrity, attribution, and an audit trail, with CRLs and clinical holds as the documented enforcement.
21 CFR Part 11 · EU Annex 11 ecfr.govQuality Brief · For the Chief Quality & Regulatory Officer
Inspection-ready by construction.
Your sign-off is the attestation an auditor relies on. Evidence infrastructure, powered by the Provenance Neural Engine (PNE), makes the evidence chain structural, so every AI-touched record carries the attribution and audit trail Part 11 and ALCOA+ ask for, without a reconstruction scramble the week before an inspection.
Quality & regulatory · GxP data integrity
This is no longer hypothetical: in April 2026 the FDA issued its first warning letter citing AI as a CGMP violation: a firm let AI-generated specs and SOPs into operations with no human review. AI doesn't remove the data-integrity burden; it industrializes it, and you can't attest to a distribution of model behaviors with a point-in-time validation. The winning move is provenance: a signed, inspectable chain from every AI-touched record back to source and the human who stands behind it. That is what evidence infrastructure is built to produce.
Three exposures a more accurate model won't close.
The audit trail an AI can't produce
The exposure
21 CFR Part 11 and EU Annex 11 require you to prove electronic-record integrity, system validation, and user attribution. An AI decision no one can reconstruct is a finding waiting to happen: a clinical hold, a Complete Response Letter, or invalidated trial data.
How evidence infrastructure answers
At its heart, the PNE binds every AI-touched assertion to a signed, inspectable chain back to source at a specific version, with the attesting human bound to it. The audit trail is structural: built as the work happens, not rebuilt by hand the week before an inspection.
ALCOA+ at machine scale
The exposure
When a medical writer's agent drafts regulated artifacts in an afternoon, ALCOA+ (attributable, legible, contemporaneous, original, accurate) has to hold for every one, or "everyone's a builder" quietly becomes manufacture-your-own-liability at machine scale.
How evidence infrastructure answers
Evidence infrastructure makes each output attributable, contemporaneous, and traceable to original source by default: the ALCOA+ properties become a property of the foundation, inherited for free by every agent built on it, not re-litigated tool by tool.
You can't qualify a distribution with a point-in-time test
The exposure
A validation regime built for deterministic systems can't sign off a probabilistic model's range of behaviors. The EMA's draft Annex 22 would restrict critical GMP AI to static, deterministic models, and the Jan-2026 FDA/EMA joint principles call for human-centric design, data governance, and documentation across the AI life cycle.
How evidence infrastructure answers
Evidence infrastructure is ScientixAI's proposed architecture for that boundary: generative reasoning on top, every critical assertion locked, provenanced, and human-attested underneath. You validate the gate, not the distribution. The draft's own scope is narrower and separate: it keeps generative models out of critical GMP applications altogether.
What's codified, and now enforced.
The data-integrity bar
ALCOA+
Every regulated record must be attributable, legible, contemporaneous, original, and accurate. Evidence infrastructure makes those properties structural, not manual.
FDA/MHRA data-integrity guidance fda.govFirst FDA AI warning letter
Apr 2026
Purolea Cosmetics Lab: AI-generated specs and records let into operations with no Quality-Unit review, cited under 21 CFR 211.22(c) and 211.100.
FDA Warning Letter 320-26-58 fda.govProvenance Neural Engine™ is a trademark of Scientix.ai Inc. References: 21 CFR Part 11 and EU Annex 11; the EMA draft Annex 22 (reassessment 30 Jun–1 Jul 2026, final pending); ALCOA+ data-integrity principles per FDA and MHRA guidance; the FDA/EMA joint Good AI Practice principles (Jan 2026); FDA Warning Letter 320-26-58 (MARCS-CMS 722591, Purolea Cosmetics Lab, 2 Apr 2026), the first citing AI as a CGMP deficiency; and Pharmaceutical Technology, "Don't Make AI Smarter. Make It Traceable." (2026). Exposure is stated as a regulatory consequence, not a dollar figure.
A log is not an audit trail.
"An inspector doesn't fail your AI because it's wrong: they fail it because it's unaccountable" (Pharmaceutical Technology, 2026). A runtime guardrail logs what the AI did; it can't reconstruct why the record is true or bind the human who stands behind it. Defensibility is a design choice, not a model choice. The PNE traces the evidence, not just the transaction, which is the difference between passing an inspection and surviving one.
The next step.
Bring us one inspection-critical workflow. We'll show you every AI-touched record traced to source, attributed, and inspection-ready: the audit trail you stop rebuilding by hand, and the finding you never get.