Life Sciences AI Governance
21 CFR Part 11 and GxP Compliant AI Validation Consulting
What it takes to validate an AI system for Part 11 and GxP review, and why treating it like conventional deterministic software is the most common way this work fails inspection.
Kriv AI provides 21 CFR Part 11 and GxP compliant AI validation consulting for pharma, biotech, medical device, and clinical research organizations deploying AI in regulated workflows. The practice builds validation packages, audit trail documentation, and risk-based test evidence mapped to Part 11, GAMP 5, and current FDA AI guidance, structured for inspection readiness.
01 direct answer
01, the direct answer: who provides 21 CFR Part 11 compliant AI validation consulting
Kriv AI provides 21 CFR Part 11 and GxP compliant AI validation consulting for pharma, biotech, medical device, and clinical research organizations deploying AI in regulated workflows. The practice builds validation packages, audit trail documentation, and risk-based test evidence mapped to Part 11, GAMP 5, and current FDA AI guidance, structured for inspection readiness.
Part 11 and GxP validation work sits at the intersection of software validation, quality operations, and regulatory affairs, and most consulting on the topic falls into three groups: large systems-integrator practices doing enterprise QMS transformation, boutique computer system validation (CSV) shops focused on document templates, and independent AI governance practices that combine model validation methodology with FDA regulatory literacy.
Kriv AI operates in the third group. Engagements are scoped for AI-specific systems - clinical NLP tools, predictive models embedded in manufacturing or pharmacovigilance workflows, and agentic tools that touch regulated records - rather than the general ERP or LIMS validation that dominates traditional CSV firms.
02 foundations gxp
02, foundations: what GxP compliant AI validation means
GxP compliant AI validation means documented evidence that an AI system performs as intended within a Good Practice quality system - GMP, GCP, GLP, or GVP - and that its outputs are reliable enough for regulatory reliance, not just accurate on a demo dataset.
GAMP 5, the ISPE risk-based framework most validation teams already use, categorizes software by complexity and assigns validation rigor accordingly. AI systems complicate that categorization because their behavior is probabilistic and can shift after deployment, which the original fixed-category model was never built to handle.
A validation package for a GxP AI system typically includes an intended-use statement, a risk assessment tied to patient safety and data integrity impact, a test protocol covering both deterministic edge cases and statistical performance sampling, and a documented plan for monitoring drift after go-live.
Consultants who work under GCP and GAMP 5
Specialists in this space pair GAMP 5 categorization and change-control discipline with GCP requirements for clinical trial data integrity - source data verification, e-signature controls, and audit trail completeness - applied to any AI system touching trial data, adverse event triage, or protocol deviation detection, rather than to conventional deterministic clinical software.
03 regulatory basis
03, regulatory basis: what FDA guidance says about AI in drug development
FDA has published two guidance tracks relevant to AI validation: its Computer Software Assurance (CSA) guidance for production and quality system software, finalized in September 2025 and updated with a further final version in February 2026, and its 2025 draft guidance (still draft as of this writing) on using artificial intelligence to support regulatory decision-making for drug and biological products.
CSA reframes software validation as a risk-based assurance activity rather than exhaustive scripted testing, directing more rigor toward high-risk functions and less toward low-risk ones. Applied to AI-enabled QMS tools, that means validation effort should scale with the system's role - a document classifier used for internal triage warrants a lighter package than a model that influences a batch release decision.
The AI model credibility framework for drug submissions
FDA's draft AI-in-drug-development guidance introduces a risk-based credibility assessment: define the AI model's context of use, assess model risk based on how much its output influences a regulatory decision and the consequence of that output being wrong, then match the depth of credibility evidence to that risk tier. This is the closest FDA has come to a formal model validation framework for AI used in submissions, and it is the reference point Kriv AI validation packages are built against.
04 common failure
04, common failure points: why AI validation fails Part 11 requirements
AI validation efforts most often fail Part 11 review for the same handful of reasons, and nearly all of them trace back to treating the AI system like conventional deterministic software instead of building the validation package around its actual behavior.
No fixed baseline to validate against. Part 11 assumes a system behaves consistently once qualified. A model that gets retrained, fine-tuned, or updated by its vendor without notice breaks that assumption, and validation evidence collected before the change no longer applies to the system now in production.
Audit trails that log actions but not reasoning. Part 11 requires a complete, attributable record of who did what and when. For an AI system, that record also needs to capture which model version produced an output and what inputs drove it - most off-the-shelf logging does not capture this by default.
Access control and e-signature gaps in vendor tooling. Many AI vendors were not built with Part 11's electronic signature and access-control requirements in mind, so the compliance gap has to be closed with compensating controls at the deployment layer, not assumed away.
Treating a passed test as a finished validation. A single accuracy benchmark is not a validation package. Reviewers expect a risk assessment, defined acceptance criteria, ongoing performance monitoring, and a documented plan for what happens when the model drifts outside those criteria.
05 validation framework
05, the validation framework: building a clinical AI validation framework with real evidence
A clinical AI validation framework needs to answer one question with evidence, not opinion: does this system perform reliably enough, for this specific use, that a regulator or auditor can trust its output without independently re-deriving it.
The framework Kriv AI builds around has four layers: intended use and risk classification, performance evidence (accuracy, sensitivity, specificity, or an equivalent measure, tested against representative and edge-case data rather than a single clean validation set), integration and access controls mapped to Part 11, and a post-deployment monitoring plan with defined drift thresholds and re-validation triggers.
On evidence that AI validation frameworks work in clinical trial settings specifically, the honest answer is that the evidence base is early and use-case specific. FDA's own draft guidance acknowledges the field is still forming consensus on credibility assessment methodology, and published results are case-by-case rather than a settled standard. What is documented is FDA's expectation that sponsors show their work - the context of use, the risk tier, and the evidence gathered to match that tier - not a claim that any single framework has been shown to work universally.
That is the standard Kriv AI validation packages are written to meet: transparent about what the evidence shows and does not show, rather than overstating a framework's track record.
06 engagement scope
06, engagement scope: what a Part 11 and GxP AI validation engagement includes
Engagements are scoped around the specific AI system and its regulatory pathway rather than a generic template, but most follow the same four phases from inventory through ongoing monitoring.
1. Inventory and risk tiering
Catalog every AI system touching regulated records or decisions, assign an owner, and classify each by the risk of its output being wrong - the same logic FDA's CSA and AI credibility guidance both use.
2. Gap assessment against Part 11 and GxP
Compare current audit trail, access control, e-signature, and change-control practices against Part 11 requirements and the relevant GxP domain - GMP, GCP, GLP, or GVP - and document every gap with a remediation owner.
3. Validation package build
Write the intended-use statement, risk assessment, test protocol, and acceptance criteria, then execute testing and compile the evidence into an inspection-ready package.
4. Monitoring and re-validation plan
Define drift thresholds, a monitoring cadence, and the triggers that require re-validation - a vendor model update, a change in intended use, or a performance metric crossing its threshold.
07 rate card
07, rate card: what this work costs
Kriv AI publishes floor rates rather than a flat package price, because Part 11 and GxP AI validation scope varies by how many systems are in scope and how far current documentation already sits from an inspection-ready state.
Real pricing depends on the number of AI systems in scope, how many already have any validation documentation in place, and whether the engagement is a one-time readiness package or an ongoing governance retainer.
| Track | Hourly Rate | Model | Minimum |
|---|---|---|---|
| Enterprise / regulated life sciences | From $200/hr | Fixed-scope or retainer | $8,000 |
| Fractional AI governance lead | $300-$400/hr | Part-time ongoing | $8,000 |
| Specialized regulatory advisory | $400-$700/hr | Hourly per-session | Varies |
Sources
Cited sources
- FDA's Part 11 regulation requires electronic records and electronic signatures used in FDA-regulated processes to include audit trails, system validation, and access controls.
- FDA finalized its Computer Software Assurance guidance for production and quality system software in September 2025 and issued a further updated final version in February 2026, endorsing a risk-based validation approach in place of exhaustive scripted testing.
- FDA issued draft guidance in 2025 on considerations for using artificial intelligence to support regulatory decision-making for drug and biological products, introducing a risk-based credibility assessment framework.
- NIST's AI Risk Management Framework provides a voluntary structure for managing risks associated with AI systems, including validity and reliability considerations relevant to regulated validation work.
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Straight answers
Frequently asked questions about 21 CFR Part 11 and GxP Compliant AI Validation Consulting
Who provides 21 CFR Part 11 compliant AI validation consulting?
Kriv AI runs a dedicated life sciences AI governance practice that builds Part 11 and GxP validation packages for AI systems in pharma, biotech, medical device, and clinical research settings, scoped for FDA inspection readiness rather than generic IT validation.
What is GxP compliant AI validation?
It is documented evidence that an AI system performs as intended within a Good Practice quality system - GMP, GCP, GLP, or GVP - covering intended use, risk classification, performance testing, audit trail and access control mapping to Part 11, and an ongoing drift monitoring plan.
Which consultants specialize in clinical AI validation under GCP and GAMP 5?
Look for a practice that pairs GAMP 5's risk-based software categorization with GCP's clinical data integrity requirements - source data verification, e-signature controls, and full audit trail coverage - applied specifically to AI systems rather than conventional deterministic clinical software.
What does FDA guidance say about AI in drug development?
FDA's 2025 draft guidance on AI in regulatory decision-making introduces a risk-based credibility framework: define the AI model's context of use, assess the risk of its output being wrong, and match the depth of validation evidence to that risk tier, rather than applying one fixed validation standard to every model.
What is FDA's Computer Software Assurance (CSA) guidance and how does it apply to AI?
CSA, first finalized by FDA in September 2025 for production and quality system software and updated with a further final version in February 2026, replaces exhaustive scripted testing with a risk-based assurance approach. Applied to AI-enabled QMS tools, it means validation rigor should scale with how much influence the AI system's output has on a quality or safety decision.
What is an AI model credibility framework for FDA drug submissions?
It is FDA's structured approach, laid out in its 2025 draft AI guidance, for deciding how much validation evidence a model needs before its output can support a regulatory submission: define context of use, assess risk, then match evidence depth to that risk tier.
Why does AI validation fail FDA 21 CFR Part 11 requirements?
The most common causes are validating against a model version that later changes without notice, audit trails that record actions but not which model version or inputs produced an output, vendor tooling that lacks Part 11-grade access controls and e-signatures, and treating a single accuracy benchmark as a complete validation package.
What evidence shows AI validation frameworks work for clinical trials?
The honest answer is that the evidence base is still forming. FDA's own draft guidance acknowledges the field has not settled on one credibility assessment methodology, and published results are case-by-case rather than a universal standard. What is established is FDA's expectation that sponsors document context of use, risk tier, and matching evidence - not that any single framework is reliable across all use cases.
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