Deterministic-for-critical
Annex 22
The EMA's draft would restrict critical GMP AI to static, deterministic models; reassessment at the EMA workshop, 30 Jun–1 Jul 2026.
EMA draft + workshop, final pending ec.europa.euExecutive Brief · For the Chief AI Officer
Governed by construction.
You are accountable for deploying AI safely across every GxP function, and the EU's draft GMP guidance would limit critical AI to deterministic models. Evidence infrastructure, powered by the Provenance Neural Engine (PNE), makes that governance structural: provable to an auditor, not promised in a policy.
Enterprise AI governance · Across all of GxP
The hard part of enterprise AI isn't capability: it's governability. And that test belongs to two gates, not one: the CAIO accountable for safe deployment across GxP, and the CFO accountable for the full cost at scale. Probabilistic models collide with a validation regime built for deterministic systems, and you can't qualify a distribution of behaviors with a point-in-time test. The era of governing AI with a policy document is over; governance now has to be built into the architecture: generative reasoning on top, every critical assertion locked and provenanced underneath, a human-attested gate between. That is evidence infrastructure, and the PNE is the engine that runs it.
"If you can't audit it, you can't govern it."
Three governance gaps a better model won't close.
Govern what you buy, not just what you build
The exposure
Your liability isn't limited to models you train: it extends to every vendor AI the business units procure, and most of it is a black box. Staff may also be feeding company data into public AI tools that can train on it, or building quick tools of their own whose numbers no one can trace back to a source. A system you can't audit is one you can't govern, and shouldn't have deployed.
How evidence infrastructure answers
Evidence infrastructure makes bought AI auditable without opening the vendor's model: its output has to cross a provenanced, human-attested gate before it becomes an assertion of record. You govern the boundary, not the black box.
The draft draws a line for critical AI
The exposure
The EMA's draft Annex 22 would restrict critical GMP AI to static, deterministic models, and is weighing whether generative AI may enter critical work at all, and only with guardrails. Policy-based governance won't answer it.
How evidence infrastructure answers
The draft's scope is its own: generative models should not be used in critical GMP applications. Evidence infrastructure is ScientixAI's proposed architecture for working on the right side of that line: critical assertions sit in a deterministic, provenanced record, and generative reasoning stays above it, behind a human-attested gate.
Provable to an auditor, not just logged
The exposure
Trust in an AI system decomposes into seven testable functions: provenance, verifiability, accountability, reversibility, legibility, recourse, and surveillance. Most stacks satisfy only a few, and a log of what the AI did can't reconstruct why the assertion is true.
How evidence infrastructure answers
Evidence infrastructure is designed to address every function but surveillance by construction, and the PNE gives the seventh its provenance backbone: it records the drift an MLOps monitor detects. One enterprise foundation, not N un-auditable deployments.
The governance bar has already moved.
The AIMS standard, made provable
ISO 42001
ISO/IEC 42001:2023 gives you a certifiable management-system frame; evidence infrastructure supplies the runtime evidence behind it, not only the paper.
ISO/IEC 42001:2023Provenance Neural Engine™ is a trademark of Scientix.ai Inc. Quoted industry voices describe the AI-governance problem and are not endorsements of ScientixAI: Nuno Valério (Merck R&D Quality), 2026; John Halamka (Mayo Clinic Platform), on algorithmic transparency. References: EMA draft Annex 22 and the 30 Jun–1 Jul 2026 workshop (final pending); ISO/IEC 42001:2023; the seven-function trust taxonomy. Coverage of that taxonomy is an author assessment of the design, not a measured result.
Policy is not governance.
"A classifier can be inaccurate; a language model can fabricate." The governance a probabilistic system needs is categorically heavier than the QA a deterministic one needs, and the vendor's demo won't tell you which you're buying. A runtime guardrail logs what the AI did; it can't reconstruct why the assertion is true. The PNE traces the evidence, not just the transaction: the difference between a policy you hope holds and a foundation that's governed by construction. Quality and RA buy the record they can stand behind.
"A nutrition label on our algorithms, only as good as the underlying training data."
The next step.
Bring us one deployment (vendor or in-house). We'll show you every AI-touched assertion traced to source, gated, and provable to an auditor: one governance layer for all of GxP, instead of N liabilities you can't see into.