When Your AI Vendor Passes SOC 2 — and Still Fails GxP
On April 27th, 2026, Valkit.ai (originally the developer of the digital validation lifecycle management platform, which has since expanded to incorporate additional products) shipped Valkit MSAT: a purpose-built product for GxP tech transfer and CDMO engagements.
What's worth pausing on is what was in the launch. Alongside the feature list, Valkit's CPO/Co-Founder and Co-Chair of GAMP Americas Stephen Ferrell and his team disclosed an unusually specific set of supplier-qualification artifacts: ISO 27001:2022 certification, CSA STAR Valid-AI-ted Level 1 self-assessment for AI-specific security controls, a written commitment that no customer data is used to train underlying models, and mandatory human-in-the-loop review with electronic signature on every AI-generated record before it can be finalized.
Most readers will skim past that paragraph. Pharma quality teams shouldn't.
Because the question that paragraph is quietly answering is the one most AI vendor relationships in our industry haven't answered at all:
What does AI-specific supplier qualification actually look like, beyond the documents your existing QMS already requires?
Beyond practical considerations like ease of use, sponsors increasingly value alignment with regulatory direction and industry supplier best practices. On April 2, 2026, the FDA issued a warning letter to Purolea Cosmetics Lab. Notably, the FDA cited an existing regulation, 21 CFR 211.22(c), sending a clear message to industry: improper AI use does not require approval of new regulations to be in violation of compliance.
What sponsors should note:
Mandatory HITL on AI-generated GxP content is now an FDA enforcement expectation, not a best practice
AI oversight cannot be delegated to the vendor or the AI platform itself
Quality agreements and quality systems should be updated to address AI tool governance in cGMP operations
This isn't just a regulatory point — it reflects how vendors who've thought about the AI-specific layer are framing the problem. As Stephen Ferrell of Valkit.ai put it, “There's a knowledge deficit between probabilistic RAG-infused AI and HITL-centric, deterministic AI. HITL gives you the opportunity to augment instead of replace."
The regulatory and industry-standard authorities relevant to AI supplier qualification have proliferated over the past eighteen months. Several authorities and industry organizations now address parts of the AI supplier qualification problem. None of them address it completely.
1. GAMP AI Guide (Ch. 7, Appendix M2, July 2025) + GAMP 5 Second Edition (2022)
The GAMP AI Guide addresses General Supplier Good Practices, Data Usage, and AI-Specific Security Measures. Appendix M2, “Supplier Management”, covers Concept, Project, Operation, and Retirement phases. The guide references ISO/IEC 42001:2023 and builds on GAMP 5 Second Edition (2022).
2. FDA CSA Final Guidance (September 2025, February 2026)
The guidance explicitly endorses leveraging vendor evidence, including but not limited to SOC reports, ISO certifications, SDLC artifacts, cybersecurity documentation. The underlying principles are clear: a risk-based approach is expected.
3. ISO/IEC 42001:2023 (December 2023)
This is the world’s first-ever international standard for AI Management Systems, published jointly by ISO and IEC in December of 2023. It specifies requirements for establishing, implementing, maintaining and continually improving an AI management system within an organization. Notably, this certification is the prerequisite for CSA STAR for AI Level 2, suggesting that a vendor with CSA STAR certification is already qualified under ISO 42001.
4. Annex 22 (Draft: July 2025)
The first comprehensive EU regulatory framework specifically for AI in GMP. Critically, it excludes LLMs and probabilistic models from GMP-critical applications, permitting them only in non-critical applications under direct human oversight. For supplier qualification, it makes clear that vendor accountability cannot be outsourced; the regulated user retains responsibility regardless of vendor pre-qualification.
The framework most teams are still using
Walk into a typical AI vendor qualification today and the artifacts on the table are familiar. SOC 2 Type II report. ISO 27001 certificate. A GAMP supplier audit, often built from a Category-4 or Category-5 questionnaire designed in 2008. A quality agreement borrowed from the last LIMS implementation. Maybe a penetration test summary.
All of it is necessary - but none of it is sufficient.
Here, I propose an operational framework for AI Supplier Qualification in non-critical regulated pharma — built on a four-pillar architecture and a five-step decision logic —that applies to probabilistic modalities like LLMs and agentic systems.
The five operational risks traditional QMS misses
1. Training data provenance
Traditional supplier qualification asks where customer data is stored and who can access it. AI supplier qualification has to ask a question that has no equivalent in classical software:
What data was the model trained on?
Who decided it was fit for that purpose?
This is now an explicit regulatory expectation. The draft EU GMP Annex 22, published for consultation in July 2025, sets out specific requirements that test data used to validate AI systems must be kept entirely separate from the data used to train the models, to guard against bias and ensure true predictive power. The ISPE GAMP Guide: Artificial Intelligence — published in July 2025 and now the foundational reference for the industry — devotes entire sections to data and model governance, data usage, and AI-specific security measures within its supplier-activities chapter.
2. Model governance and update cadence
This is the one that catches sponsors off guard most often. In classical computerised systems, change control is the sponsor's lever: you decide when an update lands and you validate it on your timeline. In AI systems built on foundation models, that lever may not exist at all.
A recent analysis of AI wrapper vendors put it plainly: Wrapper vendors typically have no control over model governance. When the foundation model provider updates their model, all customers receive the change simultaneously. There is no version pinning, no controlled rollout, no ability to validate before deployment.
That's a GxP problem hiding inside a procurement decision. If your supplier is reselling capability they don't control, your validated state is at the mercy of an upstream provider's release schedule. Annex 22 anticipates this by restricting GMP-critical applications to static models and deterministic output models, with dynamic models, generative AI/LLM, and probabilistic output models prohibited in critical use. Whatever you think of that line, the regulatory direction is clear: model update behaviour is now a supplier qualification question.
What you should be asking:
Does the vendor pin model versions for regulated customers?
What is the change control process when the underlying model is updated?
Will the vendor commit, in writing, to a controlled rollout window that allows you to revalidate?
3. Inference variance and reproducibility
Classical software gives you the same output for the same input. AI systems often don't: and this isn't a defect, it's the architecture. Temperature settings, sampling, system prompts, retrieval context, even the order of input tokens can produce different outputs from identical-looking inputs.
For GxP purposes this is the heart of the problem. Audit trails assume reproducibility. Deviation investigations assume you can re-run the conditions. Forensic review of an AI output six months after the fact assumes the vendor can tell you what the system actually did at the moment in question.
Annex 22's approach to this is to confine GMP-critical use to deterministic outputs, but even within that envelope, supplier qualification has to verify what controls the vendor has placed on the elements that could introduce variance. System prompts. Temperature. Retrieval indices. Tool-use scaffolding. Most quality agreements don't mention any of these because the standard templates pre-date them.
4. AI-specific security, not just IT security
ISO 27001 covers information security management. SOC 2 covers trust services criteria. Neither was built for prompt injection, model exfiltration, training-data poisoning, or adversarial inputs that exploit how models actually process information.
This is why the Cloud Security Alliance's STAR for AI program — launched in October 2025 with Level 2 added in November — exists in the first place. It builds on ISO/IEC 42001 (the AI management system standard) and adds the AI Controls Matrix specifically because SOC 2 can tell a customer that your controls over security, confidentiality, or privacy exist and are described. It does not automatically tell them how you handle prompt injection, unsafe tool invocation, agent memory poisoning, or model governance.
Pharma quality teams haven't widely adopted ISO 42001 or CSA STAR for AI in their supplier qualification questionnaires yet. That gap is closing fast. Vendors who already hold these certifications are signalling readiness for the supplier qualification questions the next two years will bring.
5. Validation-inheritance boundary
The four risks above all live within a single supplier relationship. The fifth lives in the spaces between them. When a pharma sponsor uses a vendor that uses Anthropic on AWS Bedrock, four parties are involved in the AI's behavior: and current supplier qualification frameworks address none of them as a system.
Where does the foundation model provider's responsibility end and the wrapper vendor's begin? Where does the wrapper vendor's responsibility end and the sponsor's begin? When the sponsor's quality team writes their validation summary, which validation evidence are they entitled to inherit, and which do they have to generate themselves?
The GAMP AI Guide gestures at this in Chapter 7 by treating each supplier relationship as discrete. The FDA CSA final guidance endorses leveraging vendor evidence but doesn't address the layered case. Annex 22 punts: the regulated user retains accountability regardless of how many parties contributed to the AI's behavior.
That sounds clean. In practice it means sponsors are validating systems whose behavior is partially determined by parties they have no contractual relationship with. The IP indemnity from the wrapper vendor doesn't cover the foundation model's training data. The wrapper vendor's SOC 2 doesn't cover the hyperscaler's BAA. The sponsor's validation summary has to either claim coverage of layers they don't control, or carve out exclusions that may or may not satisfy an inspector.
This is the supplier qualification question nobody has fully answered. The 2026 best practice is partial: BAA-eligible hyperscaler tiers (AWS Bedrock, Azure OpenAI, Vertex AI) compress the legal layer, contractual indemnification compresses the IP risk layer, and sponsor-side validation of context-of-use compresses the technical layer. None of those compressions are complete. All of them are negotiated rather than standardized.
This is where the analytical layer becomes operational. The five risks above don't sit in parallel: risks 1 through 4 each surface within a single supplier relationship, and risk 5 emerges only when you map them across the supply chain. That mapping is what VALID Trust does.
VALID TRUST: A Framework for Supplier Qualification
VALID TRUST is the name I use for the supplier qualification control layer architecture. It decomposes any AI vendor into four pillars based on what the AI actually does in the workflow:
Generative AI Validation — applies whenever the system produces novel content (text, structured records, draft documents). The qualification questions concentrate on training data provenance, output reproducibility, and grounding controls.
Agentic AI Governance — applies whenever the system takes autonomous action across multiple steps or tools. The qualification questions concentrate on action scope, tool-use scaffolding, autonomous decision boundaries, and HITL/HOTL design.
Probabilistic Acceptance — applies whenever the system's outputs vary for identical-looking inputs. The qualification questions concentrate on calibration, acceptance criteria, uncertainty quantification, and inference variance controls.
Continuous Monitoring — applies whenever the model, its data, or its operating context can change post-deployment. The qualification questions concentrate on drift detection, model versioning, change control, and revalidation triggers.
Most AI vendors trigger more than one pillar. The framework's job is to make explicit which ones, and to scope the qualification work accordingly.
The decision logic
Step 1 — Map the vendor to the pillars. For the AI vendor in front of you, determine which of the four pillars apply based on what the system actually does. A static classifier triggers Probabilistic Acceptance and Continuous Monitoring but not Generative AI Validation. An LLM-based content authoring tool triggers three pillars. An autonomous trial design agent triggers all four.
Step 2 — Run the pillar-specific questions. For each applicable pillar, work through the qualification questions specific to that AI modality. The questions for Generative AI Validation are not the same as those for Agentic AI Governance — and a single SOC 2 report does not answer either set.
Step 3 — Apply the cross-cutting threads. Across all applicable pillars, run the four cross-cutting threads: security (including AI-specific controls beyond ISO 27001), explainability, stakeholder communications, and supplier qualification meta-questions (certifications, disclosure posture, contractual commitments). These apply uniformly regardless of which pillars are in scope.
Step 4 — Locate the validation-inheritance boundary. Map the supply chain (foundation model → wrapper vendor → hyperscaler → sponsor). For each pillar, identify which evidence the sponsor can inherit from upstream parties and which the sponsor must generate themselves. This is where most vendor qualification efforts in 2026 break down: the inheritance boundary is rarely explicit, and inspectors will ask.
Step 5 — Synthesize the qualification decision. The output is a single supplier qualification record that names the applicable pillars, summarizes the evidence collected against each, identifies the inheritance boundaries, and documents the residual risks the sponsor is accepting. This goes into the quality agreement and the supplier file.
Worked examples to make this concrete:
Vendor A: Anthropic Claude API (raw foundation model access)
Pillar mapping:
Generative AI Validation: yes
Agentic AI Governance: only if the sponsor is using it agentically; the API itself is not agentic
Probabilistic Acceptance: yes (probabilistic outputs by definition)
Continuous Monitoring: yes (drift, model updates)
So three pillars apply. The supplier qualification questions concentrate on training data provenance, model versioning, inference variance, and drift monitoring: with the agentic governance pillar deferred until or unless the sponsor builds agentic systems on top.
Vendor B: Valkit MSAT
Pillar mapping:
Generative AI Validation: yes
Agentic AI Governance: minimal (mandatory HITL on every output prevents agentic autonomy)
Probabilistic Acceptance: yes (LLM outputs are probabilistic)
Continuous Monitoring: yes (platform-level monitoring)
Three pillars apply, but the agentic pillar is structurally suppressed by the product design. That's a strength; the product architecture eliminates a class of risk that other vendors leave open.
Vendor C: An agentic AI vendor doing autonomous trial design
Pillar mapping:
Generative AI Validation: yes
Agentic AI Governance: yes (this is the central concern)
Probabilistic Acceptance: yes
Continuous Monitoring: yes
All four pillars apply, and the agentic pillar carries the highest weight. The qualification questions concentrate on tool-use scaffolding, action scope, autonomous decision boundaries, and HITL/HOTL design: questions that wouldn't be load-bearing for Vendor A or B.
BioPhorum's A Practical Guide to Technical Assurance for AI (April 7, 2026) and the companion SCOPE paper propose a four-dimensional assurance framework (Structural, Process, Technical, and Cultural) addressing the types of evidence to collect. VALID Trust's pillars are orthogonal; they decompose by AI capability rather than by evidence type. The two frameworks complement each other: BioPhorum tells you what evidence to collect; VALID Trust tells you which pillar that evidence applies to for any given vendor modality.
What “good” looks like
What made the Valkit MSAT launch worth pausing on wasn't the product itself; it was that the disclosure pattern is finally becoming legible. Two vendors in the category are now publicly disclosing AI-specific supplier-qualification artifacts that go beyond SOC 2 and ISO 27001:
MasterControl achieved ISO 42001 certification in July 2025: the AI Management System standard. Notably, ISO 42001 is the prerequisite for CSA STAR for AI Level 2 — meaning a vendor holding the Level 2 designation is, by definition, already ISO 42001 certified.
Valkit MSAT discloses a CSA STAR Valid-AI-ted Level 1 self-assessment, ISO 27001:2022 certification, a written commitment that customer data is not used to train underlying models, and mandatory human review with electronic signature on every AI-generated record.
Different artifacts, different emphases. MasterControl leads on the certification stack; Valkit leads on the bundled disclosure of architectural commitments (no-training, mandatory HITL) that complement the cert. Most AI vendors selling into life sciences haven't disclosed either. When you ask, they hand you a SOC 2 report and a marketing deck.
Veeva, by contrast, illustrates a third pattern. Their public 'Third-Party Access and AI Models' policy requires that customer data not be used to train any third-party LLM — but the obligation is contractual, and it's pushed onto the customer rather than absorbed by Veeva. That's a meaningfully different posture than MasterControl's certification stack or Valkit's bundled architectural commitments. All three are 'disclosed,' but they place the locus of accountability in three different places: the auditor (MasterControl), the product architecture (Valkit), and the customer's procurement contract (Veeva). For a sponsor running supplier qualification, where the accountability lives is exactly the question.
What pharma quality teams should be asking
If you're qualifying an AI supplier in 2026, the standard pack is the floor, not the ceiling. The questions worth adding:
What data was the model trained on, and how was its provenance documented?
Is customer data ever used for training, fine-tuning, or evaluation: and if so, under what consent?
How are model versions controlled, and what is the change control process when the underlying model updates?
Where reproducibility matters, what is locked down - temperature, system prompts, retrieval context, tool scaffolding?
What validation does the supplier perform, what evidence will they provide, and where does sponsor responsibility begin?
What AI-specific security controls are in place beyond ISO 27001 — ISO 42001, CSA STAR for AI, equivalent frameworks?
None of these questions appear in the typical GAMP supplier audit template. All of them are now, as of the GAMP AI Guide's July 2025 publication and the EU's Annex 22 consultation, defensibly within the scope of regulatory expectation.
The vendors who can answer these cleanly are differentiating themselves; the sponsors who are asking them are getting ahead of the next inspection cycle. The ones who aren't are about to have an uncomfortable year.
Note: I have no commercial relationship with Valkit.ai, Veeva, or MasterControl, and was not compensated by any of them in connection with this piece.
The Safety Paradox: Why Frozen Models Aren’t Always Safer Than Agentic Ones
Picture this: A pharma company deploys an AI-assisted deviation triage model. They freeze the weights, version-lock the prompts, pin the RAG corpus, and validate it under GAMP. The validation team signs off. The monitoring dashboard shows no drift. Eighteen months pass. Everyone relaxes.
Now picture this: A second company deploys an agentic AI workflow for pharmacovigilance signal detection. It chains decisions across multiple models, pulls from live data sources, and acts semi-autonomously. The governance architecture assumes continuous change: so the team builds real-time integrity checks, prompt-layer monitoring, privilege boundaries, and automated anomaly detection into the design from day one.
Which system is more secure? The intuitive answer is A. The correct answer is: it depends on which dimension of risk you're measuring — and right now, most organizations are only measuring one.
Frozen or Agentic?
The pharma industry's current mental model: frozen = conservative = safe; agentic = autonomous = dangerous. This is a logical assumption; it’s built on decades of deterministic validation where locking a system's state was genuinely protective (locked code, locked config, locked database = validated state preserved). Why it transferred: when organizations began deploying AI, they applied the same logic: freeze everything, validate the frozen state, maintain it. CSV taught us that controlling change is how you control risk. For deterministic systems, that was correct. For probabilistic systems, it's partially correct: and the partial is where the danger lives.
Freezing a deterministic system preserves its validated state. Freezing a probabilistic system may only preserve the appearance of one.
The Two-Dimensional Problem
At minimum, two dimensions matter from a validation/quality standpoint:
Dimension 1: Validation integrity (consistency, reproducibility, drift control)
Frozen architectures are stronger than dynamic ones when measured in this dimension. Locked weights don't drift. Pinned prompts produce more consistent outputs. Version-controlled RAG sources don't introduce new information. This is real and shouldn't be dismissed.
Agentic systems are genuinely harder to validate on this dimension, by design. They're dynamic, and the validation challenge is continuous rather than point-in-time. The psychological shift here is thinking about validation as a project with a definitive endpoint to a lifecycle project, requiring intentional controls and monitoring. As a field, we are already undergoing a version of this shift, moving from a strict V-model mindset to a risk-based approach.
The final revised ICH Q9 (R1) guideline was endorsed by the ICH Assembly and regulatory agencies on January 18, 2023, and became effective on 26 July 2023. The FDA published its availability in the Federal Register on May 4, 2023. In parallel, the FDA formalized its Computer Software Assurance (CSA) for Production and Quality Management System Software guidance, finalized September 24, 2025.
The next frontier as an industry is for us to extend these frameworks to more complex machine learning and probabilistic applications, such as Large Language Models (LLMS) and Agentic AI.
Dimension 2: Adversarial resilience (security posture, attack surface awareness, compromise detection)
Frozen architectures are actually weaker here. A frozen system that passes validation creates organizational complacency: "it's validated, it's locked, we don't need to keep watching." The static state becomes a stable target for attackers. The prompt layer, the RAG corpus, the model weights sit in databases and storage that are still live infrastructure, still exposed, still being probed.
Agentic systems, paradoxically, may be stronger here; because the governance model assumes dynamism, it builds continuous monitoring, integrity checks, and anomaly detection into the design by necessity. You can't govern an agentic system with a point-in-time assessment, so you don't try. The watching is built in by necessity. The safest-looking system on paper may be the most dangerous in production; not because it failed, but because no one was looking when it was compromised.
Case in point:
McKinsey launched the AI platform Lilli in 2023, naming it after Lillian Dombrowski, the firm’s first professional female hire in 1945. Three years later, a hacker named CodeWall deployed an autonomous AI agent to identify and exploit vulnerabilities found in Lilli. Within two hours, CodeWall's agent had achieved full read and write access to Lilli's production database: 46.5 million chat messages, 3.68 million RAG document chunks, and write access to 95 system prompts.
If McKinsey, considered by many to be the most prestigious consulting firm in the world, could be systematically infiltrated by an AI agent within two hours, what surfaces in our industry are vulnerable? In the regulated life sciences industry, our patients and shareholders’ trust both depend on robust security infrastructure.
On March 11, 2026, McKinsey confirmed the vulnerability in the following statement posted to their website: “McKinsey was recently alerted to a vulnerability related to our internal AI tool, Lilli, by a security researcher. We promptly confirmed the vulnerability and fixed the issue within hours. Our investigation, supported by a leading third-party forensics firm, identified no evidence that client data or client confidential information were accessed by this researcher or any other unauthorized third party. McKinsey’s cybersecurity systems are robust, and we have no higher priority than the protection of client data and information that we have been entrusted with.”
On March 31st, 2026, CodeWall claimed they also hacked BCG’s data warehouse. The accessed account is claimed to have held full write privileges, meaning the attacker could not just read the data, but silently alter it.
On April 13th, 2026, the same group claimed their autonomous agent had also been able to view nearly ten thousand conversations held with the third of the Big Three, Bain’s, internal AI chatbot for employees, named Pyxis. CodeWall claimed that it was able to access some data within Pyxis. As more agentic AI use cases are deployed within organizations, surface area vulnerability will only increase.
Imagine the data warehouse was a clinical trial repository instead of competitive intelligence. The consequences may not just be exposure, but an inexplicably failed clinical trial, or even patient stratification inaccuracies.
Stability vs. Adaptability
A static model with locked prompts stored in a database is itself a form of vulnerability: the predictability gives an attacker a stable, predictable target. They know what they're compromising and what effect their modification will have. The attack surface doesn't change, so the attacker has unlimited time to probe it.
Five of the top 8 OWASP LLM Top 10 risks (prompt injection, supply chain and data/model poisoning, system prompt leakage, and vector/embedding weakness) cannot be detected via one-time validation. A filed validation package isn't evidence your system is safe. It's evidence your system was safe at one moment, eighteen months ago.
What Agentic Governance Gets Right (By Necessity)
Agentic systems can't be governed with a point-in-time validation. This is widely understood as a disadvantage, but it has a reframe: because of continuous monitoring, there are controls in place to manage the overall risk of the model’s performance for its context of use.
Because agentic governance assumes continuous change, the monitoring architecture is continuous by design: real-time integrity checks, privilege boundaries, action scope controls, prompt-layer monitoring, automated anomaly detection. This continuous monitoring infrastructure catches adversarial manipulation as a side effect of catching legitimate drift; the detection surface is always active. The HITL/HOTL controls required for agentic systems (similar to those presented in my piece Untangling the Web: HOTL for Agentic PV) also serve as security controls: human review of autonomous actions creates checkpoints that a silently compromised frozen system doesn't have. Continuous change control means the security posture is reassessed with every change, not once at initial validation.
Agentic AI isn't harder to govern because it changes. It's harder to compromise silently: because someone is always watching it change. This is not an argument for agentic AI over frozen architectures broadly speaking; rather, this is an argument that the risk profiles for both are different, and that the validation approach should consider this context.
The Risk Topology (Both-And)
The question isn't "frozen or agentic": it's "which risks are you willing to accept and which controls address each dimension?"
Read across each row: neither column is all green.
What This Means for Your Governance Architecture
Professor Renato Cuocolo of the University of Salerno, citing the Clusmann work, explicitly framed the problem: "Once the model has been poisoned, we cannot just go and excise the poisoned data after the fact. We need to retrain the model from scratch, reimplement it from scratch, and validate again. Obviously, this has an order-of-magnitude higher cost compared to traditional software, which can just be straightforwardly patched.”
For a validated pharmaceutical AI, this means a successful poisoning attack is not just a data breach: it is a revalidation event, which is itself a regulatory inflection.
Palo Alto Networks' "Securing Agentic AI: Where MLSecOps Meets DevSecOps" explicitly articulates the required convergence: "MLSecOps teams concentrate on AI-supply chain security including machine learning models, training data validation and AI-specific risks…For agentic AI, these parallel tracks must converge into an integrated security approach" with "unified threat modeling that considers both AI and software attack vectors" and "comprehensive security testing that evaluates the entire system, not just its components.”
This is recognition that the security system of the house must apply to all systems, whether frozen or agentic. Both architectures need continuous security monitoring; the difference is that agentic governance models build it in by default and frozen ones often don't.
A frozen generative AI system still needs prompt injection testing. A frozen RAG system still needs corpus integrity monitoring. This maps directly to the FDA-EMA Good AI Practice Principle 3: the call for adherence to regulatory standards (in this context, cybersecurity) isn't architecture-dependent. It applies to everything.
Three operational implications:
Frozen systems need a layer of continuous security monitoring just as agentic ones do. The validation sign-off doesn't protect the infrastructure layer.
The "point-in-time" validation model needs a security reassessment cadence, not just a performance monitoring cadence.
Organizations choosing between frozen and agentic architectures should assess the risk topology across all dimensions, not default to "freeze everything" as the conservative choice.
The frozen system that everyone assumed was safe had no one watching when a prompt-layer compromise went undetected for months. The agentic system that everyone assumed was risky had continuous monitoring that caught an integrity anomaly within hours.
Safety isn't a property of the architecture. It's a property of the governance around it.
The $100M Question Your AI Can't Answer (Yet): Why Validation Is the Missing Variable in Pharma's AI ROI
Editor's Note (4/15/26): Updates for sourcing accuracy and clarity.
1) 173+ figure refers to "AI-involved", not AI-discovered programs in clinical development. A previous version of this article stated incorrectly that 173+ AI-discovered programs are now in clinical development. Based on Dharmasivam et al., Pharmacological Reviews, Jan 2026, the number of AI-discovered programs in clinical development was approximately 75 by the end of 2024.
2) The 100-month/7.5% increase was sourced from DrugPatentWatch.
3) 78% figure is based on the Deloitte finding that only 22% of people have successfully scaled AI, suggesting the vast majority cannot yet deploy AI at regulatory grade.
4) The Deloitte survey is confirmed from Deloitte's 2026 Life Sciences Outlook, a survey of 280 executives conducted August–September 2025. A previous version of this article incorrectly cited a "Deloitte March 2025 report".
5) Eli Lilly committed $5B+ across 13 AI investments: the $5 billion figure refers to total potential deal value, not committed/upfront capital.
6) The 52–64% range matches BIO/Biomedtracker data (52.0% from 2011–2020; 63.2% from 2006–2015). 7) 400M figure comes from Mahlich, Bartol & Dheban (2021), "Can adaptive clinical trials help to solve the productivity crisis of the pharmaceutical industry?", Health Economics Review, 11, article 4. The $400M represents a modeled scenario (reducing development costs from $2.6B to $2.2B per drug through a hypothetical 4-percentage-point increase in success rates).
Pharma spent $29.7 billion on AI drug development deals in 2025 (January to October 17, 2025). Approximately 75 AI-discovered programs are now in clinical development. And yet, development timelines actually increased 7.5% over five years, approval rates hit an all-time low of 6.7%, and 42% of companies abandoned most AI projects in 2025.
The problem isn't the AI. It's that most companies can't prove their AI is credible to a regulator. Only 22% have successfully scaled AI, suggesting the vast majority cannot yet deploy at regulatory grade.
That's not a technology problem. It's a validation problem. And it's costing the industry billions.
What validated AI actually delivers
The business case for AI in drug development is clearly evident in the case of compressing time from target to preclinical development. Insilico Medicine’s rentosertib, which is the most advanced end-to-end AI-discovered drug to date, moved from target identification to preclinical candidate in 18 months, and to first-in-human dosing in 30 months. This stands in stark contrast to the industry average of approximately 4.5-6 years.
Similarly, Exscientia achieved comparable compression from target identification to clinical dosing: DSP-1181 transitioned from initial screening to clinical candidate in under a year. Another leading “tech-bio” company, Recursion Pharmaceuticals, claims target identification to IND-enabling studies in 17-18 months, with an astounding 90% reduction in experimental cost and workout of chemical development.
The acceleration of progress is clear: 3 AI-discovered programs entered clinical trials in 2016. A decade later, 75+ and counting AI-discovered programs are in clinical development.
Quantification of cost savings from AI in drug development fall broadly into three “buckets”:
Per-program: Pfizer’s Model-Informed Drug Development (MIDD) program, the most rigorously documented internal case study, saves an average of 10 months of cycle time and approximately 5 million dollars per program. At scale, this translates to a $100 million anticipated annual reduction in clinical trial budgets.
Per-trial: At the trial level, a study in the Health Economics Review suggests that Bayesian adaptive designs (a form of probabilistic AI in regulated drug development that predates frontier model-based AI) accumulate approximately $400 million in savings per approved drug when they work. Another example is the CALGB 49907 trial, which used predictive probability monitoring to stop enrollment at 633 patients, well short of the anticipated 1800: a 65% sample size reduction. On average, sponsors may save up to 40% in total trial costs through predictive modeling-mediated patient selection.
Industry-wide: The Information Technology and Innovation Foundation estimated probabilistic models could save up to $26 billion per year in drug discovery and $28 billion per year in clinical research. This amounts to an astounding $54 billion per year in R&D and clinical cost savings.
Failure rate reduction: promising but honest
The early signal is compelling: AI produces significant time and cost savings for sponsors in R&D. Where uncertainty remains is when drug targets begin their transition to the clinic. A systematic BCG analysis published in Drug Discovery Today (2024) analyzed 67 “tech-bio” or AI-native biotech companies and found a Phase 1 success rate of approximately 80-90% for AI-discovered molecules. This stands in stark contrast to the historical industry average of 52-64%. Phase II success rates were approximately 40%, a more tempered figure when compared to historical averages of 28-40%. Whether this represents a bottleneck remains to be seen. The first phase III readouts for AI-discovered drugs, expected between the second half of 2026 and 2027, will be eagerly anticipated industry-wide. To date, no AI-discovered drugs have been approved for commercial use.
The overall likelihood of successful transition from Phase 1 has declined from approximately 10.4% to 6.7%. This drives more demand for validated probabilistic AI that can reliably widen the Phase 2 bottleneck. More attention to validation is needed: development timelines have not decreased, but have in fact increased 7.5% over five years to over 100 months from phase 1 to commercial filing.
The market is structurally rewarding validated AI platforms.
Validation may cost money upfront, but ultimately, the validation gap costs more than the validation itself. So far, only 22% of life science companies have successfully scaled AI. Even fewer - just 9% - reported achieving significant returns, according to Deloitte’s 2026 survey. In fact, according to S&P Global, 42% of companies (not life-science specific) abandoned most AI projects in 2025, up from 17% the prior year.
Approximately 97% of AI deal value is milestone-dependent, and upfront payments average only ~3% of total deal value. Translation: realized returns require regulatory success. Your AI platform is worth $2.75B on paper but $0 if it can't survive FDA scrutiny. Eli Lilly committed $5B+ across 13 AI investments: they're selecting for validation readiness, not just algorithmic novelty.
The FDA credibility framework rewards the prepared
January 2025: 7-step credibility assessment framework
Risk = model influence × decision consequence
EMA emphasizes transparency and interpretability as important considerations, particularly for high-risk applications, while taking a risk-proportionate approach
As Tina Kiang, Director of the Division of Regulation, Guidance and Standards in OPQ/OPPQ at the FDA, put it directly on the April 1, 2026 episode of the ISPE Podcast: Shaping the Future of Pharma: "if (the first adopter is) you, that's fine, as long as you can control the risks" and "once the early adopters move, sequential adoption by wider industry tends to be pretty fast." The FDA is telling industry: we're not going to give you prescriptive guidance, but we're also not going to punish you for being ahead of us. That's permission, with the caveat that you need to demonstrate control.
The companies that build validation infrastructure now capture asymmetric advantage. The ones waiting for perfect guidance will eventually adopt under time pressure without institutional learning.
Biological variability isn't noise. It's signal that deterministic models suppress.
A probabilistic model that quantifies its uncertainty is safer than a deterministic one that's confidently wrong. This isn't philosophy; it's the operational reality of modeling living biological systems:
Hybrid PBPK+ML models outperform pure deterministic models by predicting endpoints they were never trained on
Bayesian adaptive trial designs: every single one of the first five FDA Complex Innovative Trial Design submissions used Bayesian frameworks
Pharmacovigilance: hybrid probabilistic approaches (AUC 0.83) significantly outperform standalone deterministic methods (AUC 0.73)
You can't write a first-principles equation for how a drug interacts with a living human immune system. The best you can do is build models that are honest about what they don't know. That's a probabilistic framing by definition. DILI animal-to-human concordance data from the research is approximately 50% - basically a coin flip.
This is also why LLMs, agentic AI, and swarm architectures will face the same validation challenge as they enter clinical workflows: the validation question is technology-agnostic. It's about uncertainty quantification and regulatory defensibility.
The real gap isn't data scientists. It's people who can translate between AI capability and regulatory expectation. The FDA credibility framework, ICH M15, EMA Reflection Paper: these all require someone who speaks both languages. Most quality organizations aren't staffed to design probabilistic validation lifecycles from scratch.
The first-mover window is closing quickly:
FDA-EMA harmonization (2026 joint principles) + ICH M15 creating global credibility framework
EU AI Act high-risk deadline: August 2026
The regulatory floor is rising. Companies that invest now build capability at their own pace. Companies that wait build under deadline pressure.
The difference between validated and unvalidated AI in drug development isn't marginal.
The question is not "should we use AI?" That's settled. $29.7B in deals says it's settled.
The question is: can you prove your AI is credible to a regulator?
That's the $100M question. And right now, most pharma companies can't answer it.
Your Frozen Architecture May Have a Backdoor
Editor's Note (4/15/26): OWASP elevated "Supply Chain" to the number three position on its Top 10 for LLM Applications 2025. A previous version of this article incorrectly stated that it was listed in the number one position.
Picture this: A validated electronic data capture system in a clinical trial gets breached. An attacker modifies efficacy endpoint data in the database. The change is discoverable: the audit trail shows altered records, timestamps don't match, and the investigation follows a clear path. IT remediates the access, the clinical data management team assesses impact, and the deviation is documented. Two domains, two workstreams, clear boundaries.
Now picture this: Your clinical drug development team has hundreds of millions of pathology samples flowing through an AI-assisted diagnostic classification model, with federated data inter-sharing between academic medical centers and community health systems. An attacker compromises one contributing institution's data pipeline: not the model itself, but the upstream image repository that feeds it. They introduce a small number of subtly mislabeled samples into the training or fine-tuning data: a fraction of early-stage malignancies labeled as benign, scattered across thousands of cases. The model doesn't break or flag errors. It recalibrates slightly, and its classification threshold for that tumor subtype drifts just enough to reduce sensitivity. The confidence scores still look normal. The validated golden-set rescoring might not catch it if the poisoned distribution is close enough to the natural edge cases the model already struggles with.
Now your clinical team is making go/no-go decisions on a compound's efficacy based on AI-assisted pathology reads that are ever so slightly undercounting responders. The signal-to-noise ratio in your trial shifts. Not dramatically; just enough that a borderline effective therapy looks ineffective, or an ineffective one looks borderline.
No one in IT sees a breach because the data pipeline technically functioned per specifications. No one in validation sees a failure because the model is performing within its accepted statistical thresholds. The compromise lives in the space between those two domains, and the patient safety consequence doesn't surface until someone asks why the Phase II results don't match the preclinical signal.
Stanford’s 2025 AI Index Report documented a 56.4% increase in AI-related security incidents in the previous year (2024-2025).
Right now, every drug development company adopting AI tools is asking their IT team about network security and their validation team about model performance. Nobody is asking the question that sits between those two domains: what does a validated AI system mean when the infrastructure it runs on can be compromised by a teenager with stolen credentials?
Historically, GxP validation and traditional IT security operated in two separate domains.
Validation asks: “Is this system fit for its intended purpose? And what evidence proves it?”
IT asks: “Is the network protected? Are credentials managed? Are firewalls configured?”
For deterministic software, this separation worked. A network breach was an IT event. A validation failure was a quality event. Clear separation of duties.
Enter probabilistic technology. While versions of this existed before the GPT “era”, the advent of modern neural networks catalyzed a worldwide frenzy of excitement over artificial intelligence and its capabilities. With these advancements, natural drift and adversarial manipulation can, and often do, become indistinguishable.
The playbook has shifted, and the rulebook must evolve to match it. An AI system’s validated state depends on the architecture it rests upon: model weights, training data, prompt templates, retrieval databases (RAG sources), API connections, cloud infrastructure.
Several types of surface exposure unique to probabilistic technology exist:
Data poisoning: Many researchers consider data poisoning as potentially the most vulnerable entry point. In a January 2026 study in the Journal of Medical Internet Research (Abtahi et al.), researchers analyzed multiple independent empirical studies and concluded that attack success depends on absolute sample count rather than poisoning rate. Only a fraction of compromised samples (hundreds out of millions) are needed to shift model behavior. The potential downstream consequences are severe: over or underestimating the efficacy of ineffective compounds, or modified pharmacovigilance data suppressing adverse event signals.
Adversarial attacks: Adversarial attacks, or intentional perturbations, can potentially reconstruct proprietary structures, presenting a risk to substantial intellectual property (IP). Research has identified a tactic called “drift adversarial”, which are specific drift patterns designed to exploit weaknesses in drift detection methods (Hinder et al. 2024).
Supply chain vulnerabilities: Each supply chain dependency presents an additional exposure channel. Open-source AI/ML tools, pre-trained frontier models and cloud-hosted APIs all present potential attack vectors. OWASP elevated "Supply Chain" to the number three position on its LLM Security Top 10 list for 2025. Data poisoning and Retrieval Augmented Generation (RAG) during retrieval are two additional areas of concern.
A breach of AI infrastructure could potentially take weeks or months to be discovered; if a prompt injection is inserted externally, or model weights are updated, output drift isn’t always immediately visible. A subtly poisoned AI model looks just like a working one on the surface: both produce outputs. Validation monitoring detects drift, but may not distinguish between natural data drift and adversarial manipulation.
“Frozen” architecture, like version-locked prompts, locked model weights, is partially protective, but only if the locking mechanism itself is secure.
The FDA-EMA Good AI Practice Principles call for multi-disciplinary collaboration for good reason; no single discipline on its own can ensure validated probabilistic outputs. Principle 3 explicitly calls out cybersecurity, and the ISPE GAMP AI Guide (2025) covers adversarial attacks and cybersecurity considerations.
Cybersecurity experts don’t fully understand validation. Validation practitioners don’t fully understand attack vectors. AI engineers are focused on model performance. The gap between these three domains is where the patient safety risk actually lives.
Probabilistic technology has reshaped the landscape of the road, and our guardrails must advance in parallel. The security of the environment is inseparable from the validity of the system.
Why AI Governance Fails Without a Control Layer: A House-of-Trust Model for Regulated Drug Development
Regulators have made something clear.
Organizations deploying AI in drug development must demonstrate that these systems are:
fit for purpose
risk-appropriate
governed across their lifecycle
What regulators deliberately did not specify is how that evidence should be generated.
That gap is where validation architecture lives.
Validation Architecture
The diagram below maps the structure behind the work published so far. Each layer represents a different component required to move from AI experimentation to inspection-ready systems.
Layer 1: AI Regulation
AI validation does not exist in a vacuum. It emerges from signals across the regulatory and scientific landscape.
Several developments over the past year illustrate this shift:
“Artificial Intelligence and Medicinal Products” (March 2024, updated February 2025)
The EU AI Act (June 2024)
EMA Reflection Paper AI in the Medicinal Product Lifecycle (September 9, 2024)
FDA Draft Guidance - AI in Drug Regulatory Decision-Making (January 7, 2025)
EMA First AI Qualification Opinion (March 2025)
FDA “Elsa” Launch (June 2025)
GAMP Artificial Intelligence Guide (July 2025)
FDA internal deployment of agentic AI (December 1, 2025)
CIOMS WG XIV Final report on AI in Pharmacovigilance (December 4, 2025)
FDA–EMA Good AI Practice Principles (January 14, 2026)
(Insert Infographic)
Layer 2: Framework
Once the signal is clear, the next question becomes architectural:
How should AI systems actually be validated?
The core framework I propose follows a lifecycle structure (the Britt Biocomputing Probabilistic Validation Lifecycle)
CoU → Risk → Evaluation Design → Acceptance Criteria → HITL → Monitoring → Change Control
Each step ensures that validation evidence reflects context of use and scientific risk, not generic benchmarks.
This is a validation workflow, but per the FDA/EMA Good AI Practice Principles, multidisciplinary expertise is required, and an AI Validation Architect should work with relevant stakeholders, including domain specialists, Digital, Data Science, Quality, and Regulatory groups.
Layer 3: Operational Controls
Architecture alone is not enough. Regulators do not audit concepts; they audit controls.
Operational controls transform validation architecture into real-world, practical governance mechanisms.
These include:
human oversight structures
drift monitoring
lifecycle validation plans
inspection-ready documentation
https://www.kaylabritt.com/blog-1-1/data-drift-a-risk-based-and-gamp-aligned-approach
https://www.kaylabritt.com/blog-1-1/human-in-the-loop-liability-still-in-play
https://www.kaylabritt.com/blog-1-1/ai-trust-is-not-a-feeling-its-a-validation-strategy
https://www.kaylabritt.com/blog-1-1/the-guidance-says-what-the-next-12-articles-show-how
Layer 4: Failure Modes & Evaluation
Validation begins with a simple premise:
You cannot validate a system until you understand how it fails. Identifying failure modes comprehensively necessitates a multi-disciplinary approach. Scientific workflows fail in two fundamental ways: either the system is fed the wrong evidence, or it draws the wrong conclusion from the evidence. When both occur together, the result is compounded system risk.
In an AI-enabled workflow, evidence-generation failures sit upstream of the AI layer with data governance, while evidence-interpretation failures sit within the realm of AI validation itself.
In AI-enabled life-science workflows,
Evidence failure includes:
assay variability
poor provenance
mislabeled samples
cohort bias
missing metadata
batch effects
nonrepresentative training data
Inference failure includes:
wrong model choice
overfitting
unsupported extrapolation
hallucinated LLM output
prompt fragility
weak acceptance criteria
automating a task that still requires expert judgment
This is where evaluation design and golden datasets become critical.
https://www.kaylabritt.com/blog-1-1/validation-for-llms-an-interdisciplinary-perspective
https://www.kaylabritt.com/blog-1-1/fit-for-purpose-llms-why-it-matters
Layer 5: Worked Examples
This is the final layer that translates architecture and controls into real use cases.
Early examples include:
pharmacovigilance case processing
deviation categorization workflows
The next phase of this work expands into more complex systems:
agentic workflows
multimodal models
vendor qualification
human performance validation
The Transition to the Next Phase
The foundation is now in place.
The next phase of this work will stress-test the architecture against increasingly complex systems:
agentic AI workflows
multimodal models
vendor qualification
full validation case studies
Because in regulated science, the real question is never simply whether AI works.
It is whether we can stand behind the evidence it produces.
The Guidance Says What. The Next 12 Articles Show How
Over the past four months, I've published 12 articles on AI validation for life sciences, starting with the case for interdisciplinary expertise in AI validation, and have touched on everything from the technical implementation side (HITL/HOTL in practice, transparency architecture) to the business case for a robust validation strategy.
Since my first post, I launched Britt Biocomputing Insights and filed Britt Biocomputing as an LLC on November 25th. On the regulatory side, the FDA announced their internal usage of agentic AI on December 1st, and the FDA and EMA jointly released the Good AI Practice Principles on January 14th, just weeks later (https://www.kaylabritt.com/blog-1-1/fda-amp-ema-just-released-ai-guiding-principles-for-drug-development-heres-what-they-actually-mean).
Prior to launching my consultancy publicly, I shared my core framework on my website:
CoU Definition → Risk → Eval Design and Development → Acceptance Criteria → Deployment and HITL Control → Continuous Monitoring
And also noted that rigor always scales with the risk tier. The FDA/EMA Good AI Practice Principles formalized these same pillars.
Now that I’ve laid the foundations, the next phase of my blog addresses the harder operational questions, like agentic and multimodal architectures, human performance validation, full worked examples, and vendor qualification: specifics that are useful for sponsors navigating the changing landscape of AI architectures in drug development. The guidance tells sponsors what to demonstrate but deliberately stops short of how. That's where the next phase of this work lives.
The foundations are built. Now we stress-test them.
AI Trust is not a feeling - It’s a Validation Strategy
Everyone says they want "trustworthy AI." But when I ask pharma teams what that means, I get feelings, not metrics.
Trust isn't built by reassurance. It's built by evidence.
"Trust" in AI adoption is currently treated as a communications problem - better change management, better messaging to stakeholders - when it's actually an engineering problem. You can't talk your way into trust with a QA team that's seen AI hallucinate. A Pistoia Alliance survey found that 27% of respondents didn't even know the source of data used to train their AI models.
Trust is the output of a validation strategy, not the input. You engineer it through three things:
1. Transparency: Can you show the human reviewer why the model made that decision? (Chain-of-thought logging, confidence scores)
2. Reproducibility: Can you get the same result twice? (Frozen architectures, version-locked prompts)
3. Accountability: When it fails, does someone own it? (HITL/HOTL)
Stop asking "how do we get people to trust AI?"
Start asking "what evidence would it take?"
Regulatory and validation teams have been trained their entire careers to demand reproducible evidence: and many AI implementations haven't produced any.
Transparency doesn't mean explaining every decision. It means providing the right level of evidence for the risk tier. Abandoning "black box" models for all contexts of use just because you can't trace every internal weight is its own form of risk. Post-hoc explainability techniques can validate even opaque models: the question is whether the evidence matches the stakes.
Reproducibility needs to be reframed. In the context of probabilistic models, exact output-level replication isn't the standard. Functional and statistical reproducibility are the primary domains of significance. Across N runs, does performance stay within your pre-defined acceptance thresholds? That's the question that matters.
Accountability means more than assigning blame. We don't just validate the model; we validate the human-AI interaction itself. Who reviews the output? How do we know the reviewer is actually reviewing, and not rubber-stamping? The delta between model suggestion and human action is where trust lives or dies.
Trust isn't the prerequisite for AI adoption. It's the deliverable.
The Engineering of Uncertainty: Transparency in the Probabilistic Era
For the last thirty years, validation engineering has rested on a single, comforting bedrock: Determinism.
In the world of traditional GxP software, the contract was simple: Input A must always equal Output B. If you ran the test a thousand times, you expected the same result a thousand times. Any deviation was a defect; any variance was a failure.
But we have left that world behind.
We are now integrating systems where variance is not a bug; it is the engine. Large Language Models and generative agents do not offer us the safety of repetition; they offer us the power of inference. This shift creates a fundamental paradox for the life sciences: How do we apply rigid, binary validation standards to fluid, probabilistic systems?
The answer isn't to force these models to act like legacy software. We cannot simply "test out" the uncertainty. Instead, we must learn to measure it, bound it, and ultimately, engineer it. We are moving from validating for correctness to validating for stability: and that requires a completely new set of metrics.
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The Regulatory Landscape
While research on methodology is robust, best practices for validation remain fluid. The FDA has yet to offer specific guidance on the methodology of explainability, whereas the EMA explicitly specifies:
"To allow review and monitoring of black box models, methods within the field of explainable AI should be used whenever possible. This includes providing explainability metrics, such as SHAP and/or LIME analyses..."
Similarly, the CIOMS Working Group XIV includes “Explainability” as the eighth core “guiding principle” for AI in pharmacovigilance. For QA, PV, and clinical leaders who must sign validation reports, the core question is not “Is the model clever?” but “When it fails, do we see it coming?”
Here, we propose a layered approach to the Explainable AI (xAI) problem, utilizing risk stratification to determine the appropriate methodology.
1. Interpretable Architectures ("Glass Box")
In some very high-risk use cases, interpretable architectures are, and should remain, the default approach. A 2025 analysis suggests that the “black box vs. glass box” tradeoff is not as clear-cut as we may assume. Atrey et al. quantified this metric as “Composite Interpretability” (CI), identifying instances where interpretable models (like Decision Trees or Generalized Additive Models) outperform neural networks when human error integration is accounted for.
2. Post-hoc XAI
For “black box” models inherent to modern NLP and deep learning, we cannot see the architecture, so we must interrogate the behavior. Three primary methods dominate the literature:
LIME (Local Interpretable Model-agnostic Explanations) LIME assumes that even if a model’s global decision boundary is complex, it is likely simple (linear) locally around a specific data point.
How it works: It takes a single inference, generates thousands of slightly "off" versions (adding noise), and observes how predictions change. It then fits a weighted linear model to explain that specific prediction.
The Intuition: "I don't know how the whole brain works, but for this specific decision, it acted like a simple linear equation where "Feature A" carried the most weight."
SHAP (SHapley Additive exPlanations) SHAP derives from cooperative game theory. It treats each feature as a "player" in a game where the "payout" is the model's prediction.
How it works: It calculates the marginal contribution of a feature by analyzing prediction changes when that feature is present vs. absent across all possible combinations.
The Intuition: "Feature A contributed +10% to the probability, and Feature B subtracted 5%, based on a mathematically fair distribution of credit."
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Counterfactuals Often the most intuitive for end-users, this method ignores "feature weights" and focuses on outcomes.
How it works: It searches for the smallest change to the input vector that would flip the prediction class.
The Intuition: "Your loan was denied. If you earned $5k more per year, it would have been approved.”
The Validation Angle: For life sciences, a counterfactual is only useful if it is Feasible. We must apply Validity metrics (does it flip the class?) and Actionability metrics. If a model suggests changing a patient’s age or genetic history to optimize a trial outcome, the counterfactual is mathematically valid but clinically useless.
3. Uncertainty Quantification as Transparency
In a deterministic system, "transparency" means seeing the logic. In a probabilistic system, transparency means seeing the confidence.
If a model predicts a tumor classification with 51% probability, presenting that result as a binary "Malignant" is a failure of transparency. It implies a certainty that does not exist.
The Method: We must move beyond simple point estimates (soft-max probabilities) which are notoriously uncalibrated. Techniques like Conformal Prediction allow us to generate prediction sets (e.g., "The diagnosis is {Class A, Class B} with 95% confidence") rather than a single label.
The Validation Angle: Validation here shifts from checking for "correctness" to checking for Calibration. We validate that when the model says it is 90% confident, it is actually correct 90% of the time. This "error bar" is often more valuable to a clinician than the prediction itself.
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4. Concept-Based Explanations
Feature attribution methods (like SHAP) tell us where the model is looking (e.g., "Pixel 402"), but they fail to tell us what the model sees. In life sciences, we need explanations that speak the language of the domain expert, not the language of the matrix.
The Method: Approaches like TCAV (Testing with Concept Activation Vectors) bridge this gap. Instead of highlighting pixels, TCAV measures the model's sensitivity to high-level concepts defined by the user (e.g., "Is the model predicting 'Zebra' because of 'Stripes'?").
The Validation Angle: This allows us to validate the scientific plausibility of the model’s reasoning. If a dermatology model is detecting skin cancer, TCAV can confirm it is triggering on "irregular borders" (a valid clinical concept) rather than "ruler markings" (a confounding artifact).
5. Documentation-Based Transparency
Transparency is not solely about the algorithm; it is about the artifact. Before a single inference is run, the system’s pedigree must be transparent.
The Method: We advocate for the adoption of standardized "nutrition labels" for models, such as Model Cards (Mitchell et al.) and Datasheets for Datasets (Gebru et al.). These documents must explicitly detail the training data composition, known limitations, intended use cases, and performance metrics across different demographic subgroups.
The Validation Angle: This is Static Transparency. In a GxP audit, this documentation serves as the primary evidence that the system's "Intended Use" matches its operational reality. It prevents "scope creep" where a model validated for adults is inappropriately deployed for pediatrics.
6. Contextual Disclosure
The final layer of transparency is the user interface itself. "Explainability" is not a dump of raw data; it includes the delivery of relevant information to the human operator at the moment of decision.
The Method: This involves Progressive Disclosure. A physician using a Clinical Decision Support (CDS) tool does not need to see a SHAP value for every inference. They need a "traffic light" indicator of uncertainty and a "Click for Details" option to drill down into the counterfactuals when the case is ambiguous.
The Validation Angle: This is Usability Engineering (IEC 62366). We must validate that the transparency mechanism reduces, rather than increases, cognitive load. If the XAI tool confuses the user, it is a safety hazard, not a feature.
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The arc of HITL-HOTL-xAI was intentional; human performance remains a key variable in AI performance. We cannot fully address transparency without the human element.
A few operational suggestions for industry leads:
Inventory your AI systems and classify them into risk tiers aligned with CIOMS XIV and the AI Act.
For each tier, define a minimum transparency stack (which of the six layers are mandatory) and embed this into your QMS templates and validation plans.
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Example of risk-tiering transparency methodology based on AI context of use.
Pilot conformal prediction or similar calibration techniques on at least one CDS or PV model in 2026 and document coverage and calibration as primary validation endpoints.
The onus is on us to develop robust frameworks for transparent AI. Abandoning high-performance models solely due to their “black box” nature is a hindrance to patient benefit. Technology will move forward; our validation frameworks must move with it.
Note: Pharmacovigilance is used here as a representative model; R&D and CMC leaders can and should adapt these transparency frameworks for upstream applications.
Human-in-the-Loop, Liability Still in Play
Note: This approach aligns with established GxP principles around procedural controls, segregation of duties, and auditability.
Human-in-the-Loop is such a critical component of any probabilistic AI deployment within regulated life sciences spaces that it received its own explicit carve-out in the FDA/EMA's Good AI Practice Principles release (Principle 1: Human-Centric by Design). As AI technologies become embedded within infrastructure and workflows in R&D/CMC and healthcare organizations, HITL is a guardrail against downstream propagation of model errors.
However, this means we must evaluate and document the human-interface interaction as critically as we do the model performance and architecture itself. In practice, there are several ways to accomplish this:
1. “Draft Only — Requires Human Review”
For AI-assisted protocols, reports, or structured records, model outputs should be explicitly labeled Draft Only.
System controls should prevent finalization or downstream use until a human reviewer:
performs review,
documents rationale, and
applies a signature or electronic attestation.
This enforces procedural accountability and prevents silent adoption of AI-generated content.
2. Workflow Design (Preventing “Blind Approval”)
In RAG or multi-step AI workflows, each stage should require human confirmation before progression.
The goal is not speed reduction; it is preventing opaque, end-to-end automation where no single human can attest to what they actually reviewed.
3. Cognitive Forcing Functions (“Friction-by-Design”)
One of the most common HITL failure modes is automation bias: over time, humans may stop reading and simply click “Approve.”
To counter this, interfaces should require intentional cognitive engagement before submission.
Examples include:
requiring the reviewer to highlight supporting evidence in source text,
selecting a justification or confidence code,
or highlighting discrepancies.
This aligns with established human-factors and safety-critical system design and ensures the review is real, not ceremonial.
4. Confidence-Based Triage Routing (Risk-Based HITL)
Not all AI outputs require the same level of scrutiny.
HITL workflows should adapt based on:
calibrated uncertainty scores,
confidence thresholds,
or predefined risk classifications.
Higher-uncertainty outputs can be automatically routed for deeper or secondary review, while low-risk outputs follow streamlined paths. This mirrors traditional GxP risk-based validation approaches and supports scale without sacrificing control.
5. Full Traceability of the Hybrid Decision
Traditional audit trails track data changes. AI workflows must also track decision lineage.
The audit record should capture:
model output,
human edits,
timestamps,
reviewer identity,
and rationale.
This directly supports ALCOA+ principles and regulator expectations around accountability and traceability.
Real-World Example: AI in Pharmacovigilance (PV) Case Processing
Here is a scenario you can use to tie all the points together. It demonstrates how HITL protects the process during high-volume data intake.
The Scenario: A pharmaceutical company uses a Large Language Model (LLM) to scan incoming unstructured emails from patients to identify potential Adverse Events (AEs).
The Risk: If the AI misses an AE (False Negative), a safety signal could be ignored. If it hallucinates an AE (False Positive), resources are wasted investigating non-events.
The HITL Implementation:
Draft Only: The AI scans the email and pre-fills the intake form (Patient ID, Drug Name, Symptom). The status is automatically set to "Pending Medical Review": the system prevents the record from moving to the safety database until a human signs off.
Cognitive Forcing: The UI displays the original email on the left and the extracted data on the right. The "Submit" button is disabled until the human reviewer clicks the specific sentence in the email that describes the symptom (e.g., "I felt dizzy after taking the pill"). This proves the reviewer actually read the source text.
Audit Trail: The reviewer notices the AI listed "nausea" but the patient actually wrote "queasy." The reviewer corrects the field. The system logs: Field 'Reaction' changed from 'Nausea' (Model) to 'Queasy' (User: Dr. Smith) at 10:42 AM.
The Result: The efficiency of AI is gained (pre-filling data), but the regulatory requirement for validated safety reporting is maintained through forced, documented human oversight.
Implementing HITL is not a "set it and forget it" deployment; it is an ongoing process of quality assurance. Just as we monitor models for data drift, we must rigorously monitor our workforce for "reviewer drift": the tendency for human oversight to degrade over time due to fatigue or over-reliance on the AI.
To ensure the human element remains a robust guardrail, organizations should implement a Reviewer Quality Assurance (QA) Protocol:
Randomized "Golden Set" Evaluation: A configurable percentage (e.g., 5–10%) of all AI-processed records that have been "Verified" by a human are automatically routed to a Senior Quality Lead for a blind secondary review. This acts as a continuous audit of the HITL process.
The "Three Strikes" Threshold: We must quantify human performance just as we do model performance. If a human reviewer fails to catch a model error (or erroneously edits a correct output) more than X times in a rolling period:
HITL as a Validated Control — Not a Checkbox
By validating the interaction, not just the output, HITL becomes an active, inspectable control that satisfies both the letter and the spirit of the FDA/EMA’s Human-Centric by Design principle.
As AI systems evolve toward multimodal and agentic architectures, HITL must scale accordingly: shifting from manual intervention inside every step to structured oversight of the loop itself.
Next week: a deep dive into Human-on-the-Loop (HOTL) and how oversight changes as autonomy increases.
FDA & EMA Just Released AI Guiding Principles for Drug Development: Here’s What They Actually Mean
Today, the FDA and EMA jointly released Guiding Principles for Good AI Practice in Drug Development.
If you work in life-sciences R&D, stop scrolling. This is not just another policy document. It’s a signal that the era of experimental, undocumented AI is ending.
What matters isn’t the principles themselves. What matters is who now owns the risk, and what regulators will expect to see when AI influences scientific decisions.
Below is what sponsors should understand now.
This is not regulation. And that’s exactly why it matters.
The guidance is deliberately non-prescriptive. There are no checklists, no templates, no mandated validation methods.
That’s not a gap. That’s the point.
Regulators are saying:
“You are responsible for demonstrating that your AI system is fit-for-purpose, risk-appropriate, and well-governed — across its entire lifecycle.”
In other words:
Waiting for rules is no longer defensible
Pointing to vendor benchmarks is insufficient
Treating AI as 'just software' is no longer a viable regulatory strategy
The quiet but critical shift: from tools to evidence
The most important change in the FDA–EMA principles is subtle:
AI is no longer framed as a productivity tool. It is framed as a system that can generate, analyze, or influence scientific evidence.
That has consequences.
When AI contributes to:
target identification
candidate prioritization
trial design
safety interpretation
manufacturing decisions
…it becomes subject to the same scrutiny as any other system that influences patient outcomes.
This is why the guidance emphasizes:
context of use
risk-based validation
human oversight
lifecycle monitoring
clear documentation and traceability
Not accuracy. Not model size. Not novelty.
Why “we’ll validate later” no longer works
A common pattern I see in R&D organizations is:
“We’re piloting AI now; we’ll formalize validation once it’s closer to GxP.”
The problem is that model behavior is shaped early:
by training data
by prompt strategies
by human-AI interaction patterns
by how outputs are trusted (or over-trusted)
By the time a system is “critical,” the evidence gap already exists.
The FDA–EMA principles make this explicit: validation is proportional to risk, not delayed until formality.
Early-stage AI still requires:
defined decision boundaries
known failure modes
fit-for-use performance criteria
documented assumptions and limitations
What regulators are really asking sponsors to show
Stripped of policy language, the principles boil down to five questions regulators will increasingly expect sponsors to answer:
Who owns the risk if the model fails?
Can you trace the decision back to the data?
Is the human actually overseeing it, or just clicking 'OK'?
How is performance monitored as data, context, and models change?
Can you explain its use and limitations to the people affected by it?
Answering these questions with intent is no longer enough. The new standard requires evidence.
Where most organizations may struggle
In practice, the hardest parts of alignment are not technical:
Translating AI behavior into scientifically meaningful failure modes
Defining acceptance criteria that reflect biological risk
Evaluating human-AI interaction, not just model output
Maintaining evidence over time as models drift and evolve
Maintaining multi-disciplinary expertise over the entire lifecycle of the model
These are validation problems, not data science problems.
And they sit squarely between R&D, Quality, Regulatory, and Digital teams. Teams that historically spoke four different languages must now answer to one shared standard.
What “fit-for-purpose AI validation” actually means now:
A fit-for-purpose approach does not mean validating everything to the same standard.
It means:
defining context of use first
tiering risk explicitly
tailoring evaluation methods to scientific impact
generating evidence that is proportionate, traceable, and defensible
planning for lifecycle monitoring from day one
This is exactly the operating model regulators are signaling, without telling sponsors how to implement it.
The bottom line
The FDA–EMA principles do not slow AI adoption.
They raise the bar for trust.
Organizations that treat this moment as a documentation exercise will struggle. Organizations that treat it as a scientific quality problem will move faster, and safer.
AI in drug development is no longer about whether it works.
It’s about whether you can stand behind it.
Patients must be able to trust that we aren't just accelerating discovery, but governing it. Because in the end, speed without safety isn't a breakthrough; it's a liability.
History Rhymes: Why AI is the "Paper-to-Digital" Shift of Our Generation
1997: FDA drops 21 CFR Part 11. Pharma validation breaks overnight.
2026: FDA deploys agentic AI internally. History rhymes—and your validation frameworks aren't ready.
The question isn't if you'll need GxP-aligned AI validation. It's whether you'll build it before the audit pack lands on your desk.
The First Wave: Paper (deterministic)
The first major shift was moving from physical atoms (paper) to binary bits (electronic records). We had to prove that the computer would do exactly what the paper did, every single time. 1 + 1 had to equal 2. This birthed "Computer System Validation" (CSV). It was rigid, script-based, and binary. Pass/Fail.
The Second Wave: Digital (probablistic)
We are now entering the second massive shift. We aren't just changing the medium (paper to screen); we are changing the logic.
We aren’t changing the medium; we are changing the logic. We are moving from Deterministic (If X, then Y) to Probabilistic (If X, then likely Y).
The original “CSV” playbook doesn’t work when applied to LLMs or agentic AI. You can't write a test script for an infinite number of potential outputs.
Like biological processes, AI requires a unique approach to validation. AI is more like biology than software. We are moving from Validation as Architecture (checking blueprints) to Validation as Medicine (monitoring health). You don't 'debug' a biological system; you diagnose it. AI is the same.
The "Compliance Tollbooth": Bridging the Gap
Validation isn't dying; it’s just getting harder. We need a new "Tollbooth": a set of checks that acknowledges uncertainty rather than trying to eliminate it.
The Britt Biocomputing Playbook:
Fit-for-Purpose Validation: We assess the context-of-use to identify the appropriate risk-tier, rather than a one-size-fits-all approach.
From Checklists to Guardrails: We don't test every output; we test the safety boundaries.
Critical Thinking vs. Scripting: This aligns tightly with the GAMP 5 ISPE guidance; instead of checking every function utilizing the same checks, we implement risk-based approaches that acknowledge that not every use case needs the same level of validation.
Golden Datasets: We validate against a proprietary suite of “golden datasets” developed via testing against dozens of frontier models.
Continuous Monitoring: We provide the framework to monitor your model long-term, so you don’t stumble into barriers like data drift.
This is why we need interdisciplinary professionals as the new generation of AI Validation Engineers - we need people who can translate between the code, the science, and the regulations.
Part 11 rewrote validation overnight. AI validation guardrails aren't optional anymore.
The Capability Paradox: Why Soaring LLM Benchmarks Demand Stricter Validation
We often assume that as LLMs get smarter, the validation burden decreases.
The opposite is true, especially for R&D and CMC workflows.
Recent benchmarks, such as OpenAI’s FrontierScience, confirm that while models are becoming exponentially better at scientific reasoning, they are also becoming adept at “ardently defending” their mistakes.
In a Manufacturing environment, physical constraints often catch these errors; a bioreactor can only spin so fast before a safety breaker trips. But in R&D and CMC, where the output is decision-making, data interpretation, and regulatory drafting, a 'confident' hallucination can contaminate an entire development lifecycle before it is caught.
Intelligence does not equal Compliance. In fact, without fit-for-purpose validation, high-IQ models are high-risk liabilities.
The Shift from Retrieval to Reasoning
OpenAI acknowledges that FrontierScience measures only part of the model’s capability. However, it represents a critical leap: it is one of the first benchmarks to measure a model's ability to reason through novel scientific input rather than just regurgitating training data.
Previous benchmarks (like MMLU) tested Knowledge Retrieval (e.g., "What is the boiling point of ethanol?"). FrontierScience tests Scientific Process (e.g., "Given these novel conditions, predict the reaction yield.").
The New Validation Mandate
For Life Sciences, this shift signals that the era of "Generic Benchmarks" is over. We can no longer rely on general reasoning scores to predict GxP safety.
If an agentic workflow is capable of 79x efficiency gains in protocol design (as recent reports suggest), it is also capable of generating errors at an accelerated speed.
To harness these tools safely, we must move beyond standard evaluation metrics and implement Context-Specific Validation layers: frameworks that don't just test if the model is "smart," but verify that it is "compliant."
The models are ready for the lab. The question is: Are your safeguards ready for the models?
FDA’s Agentic AI Announcement Signals a New Era for Scientific Computing
In early December, the U.S. Food and Drug Administration quietly released one of the most consequential technology updates in its recent history: an agency-wide deployment of agentic AI tools for internal use across regulatory review, scientific computing, compliance, inspections, and administrative workflows.
For an organization historically defined by caution and structured decision-making, the introduction of planning-capable, multi-step-reasoning AI systems may mark a turning point. And not only because of what FDA will do with these tools internally, but because of what this move signals to the life-sciences sector watching closely from the outside.
What the FDA adopts today becomes the industry’s expectation tomorrow.
What FDA Actually Announced
The agency’s announcement included several key components:
FDA has deployed agentic AI systems: advanced models designed for planning, reasoning, and executing multi-step tasks — within a secure government cloud environment.
Use of these systems is optional for staff but available across a wide range of regulatory and operational functions.
The AI is configured not to train on reviewer inputs or on confidential industry submissions, a critical safeguard for regulated data.
FDA also launched an “Agentic AI Challenge,” inviting staff to build and test AI-augmented workflows, with outputs slated for presentation at the agency’s Scientific Computing event in January 2026.
This builds on the earlier rollout of Elsa, FDA’s generative-AI assistant, which rapidly reached over 70% voluntary staff adoption.
In short: FDA is no longer exploring AI. It is operationalizing it.
A Strategic Inflection Point for Scientific Computing
Within regulatory agencies, change tends to be incremental. But when it comes to computational approaches, the last five years have been an acceleration curve: real-world evidence tooling, large-scale data integration, model-informed drug development, and now agentic systems capable of generating structured workflows.
For life-sciences organizations already experimenting with LLMs, the FDA’s move does two things:
1. It normalizes AI-augmented scientific computing.
If internal regulatory workflows are being reshaped by agentic systems, it is now reasonable for industry scientific and quality teams to pursue AI-enabled efficiencies as well. Organizations that adopt AI may have a significant competitive advantage in the not-so-distant future as efficiency gains compound.
2. It raises the bar for validation, auditability, and evidence.
When regulators embrace AI, the natural next question is:
How will regulated companies demonstrate that their own AI systems are fit-for-purpose?
The FDA’s announcement implicitly signals that risk-based, evidence-driven evaluation frameworks will become even more essential for LLMs and other agentic tools used in R&D, quality, and manufacturing.
A Personal Note on Timing
A few days before the press release, I filed the paperwork for Britt Biocomputing LLC, a consultancy built around fit-for-purpose LLM validation for life sciences.
The timing wasn’t intentional.
It was simply a response to the same trends that FDA is now making explicit: AI is no longer a novelty within scientific and regulated environments: it is becoming infrastructure. And once a technology becomes infrastructure, it requires rigor, governance, and evidence to support its use.
If anything, the FDA’s announcement confirms what many early practitioners have already been preparing for: the shift from theoretical AI governance to operational AI validation.
Implications for Industry
While the FDA emphasized internal usage, the downstream effects will extend across the entire life-sciences ecosystem.
1. Regulatory interactions may accelerate, but expectations may rise.
More efficient internal workflows could shorten review cycles or increase throughput. At the same time, companies may face more structured questions about how their own AI-enabled processes operate.
2. AI will most likely become part of the “normal” regulatory conversation.
Whether in submissions, inspections, or quality system discussions, AI-driven workflows will cease to be exotic. They will be treated like any other computerized system: something to be understood, assessed, and validated.
3. Evidence packs and traceability frameworks will matter more than ever.
If agentic tools are helping generate analyses, summaries, or draft documents, both regulators and industry will need clear provenance, human-in-the-loop controls, and risk-mitigation strategies that map cleanly to existing quality expectations.
4. The adoption gap will widen.
Organizations that prepare now will move faster later; not because they “trust AI” more, but because they understand how to govern it.
What to Watch in Early 2026
The upcoming Scientific Computing event, where FDA staff will showcase their internally built AI workflows, will likely set the tone for:
how agentic systems are evaluated in a regulatory context,
what kinds of tasks FDA sees as low-, medium-, or high-risk,
how reviewers incorporate AI outputs into their decision-making pipelines, and
what transparency expectations may start to form for industry.
Even if details remain internal, the themes that emerge will shape the industry’s next steps.
Conclusion: AI Has Entered the Regulated Core
The most important part of FDA’s announcement is not the technology itself: it is the signal.
AI is no longer peripheral. It is becoming part of the regulated decision-making fabric.
For the life-sciences sector, this creates a dual responsibility:
to innovate with these tools, and
to validate them with the same rigor we apply to any system that touches product quality or patient safety.
Agentic AI inside FDA is more than a technological shift: it is a governance shift. And governance shifts always reshape the landscape for those who operate within it.
Data drift: a risk-based and gamp-aligned approach
Why it matters: LLMs can fall out of spec without any code change, because the inputs, policies, or real-world tasks evolve. That’s data drift. In GxP, we handle it with a continuous, risk-based approach: define intended use → set acceptance criteria → monitor → re-validate on triggers.
1) Define the context of use (CoU)
State exactly what the model may influence and the allowable autonomy (draft-only, HITL required, blocked actions). Tie it to process/scientific risk.
Example (Deviation/CAPA assistant): Suggests categories using the approved ontology; HITL required; never commits system-of-record changes.
2) Set acceptance criteria up front
Pre-register the bar so you know when drift matters.
Coverage/accuracy (gold set): ≥ 90–95% top-k on SME-labeled cases
Safety: 0% prohibited actions
Traceability: ≥ 95% of suggestions include source/rule citation
Contradictions/hallucinations: ≤ 1% on spot checks
Ops KPI: −30–50% time-to-first-draft; rework ≤ 10% needing >1 revision
3) Know the drift you’re watching for
Input/format drift: new document types, vendors, equipment, templates
Concept drift: updated taxonomy, new CAPA rules, new SOPs
Prior/frequency shift: distribution of cases changes (e.g., more of type X)
4) Monitor and act on triggers
Treat re-validation as triggered and proportional to risk.
Periodic review: keep a light cadence (e.g., quarterly) even without triggers.
5) Minimal evidence pack (inspection-ready)
CoU & allowable autonomy
Risk register (what can go wrong; key slices)
Acceptance criteria & test plan (pre-registered)
Gold set + results (with ALCOA+ lineage)
Monitoring plan + trigger log
Change-control entries (what changed, why, evidence)
6) Worked micro-example (new equipment type)
A new controlled rate freezer goes live → input drift.
Add a representative “equipment-X” slice to the gold set.
Re-run evals; require ≥ 92% top-k, 0% prohibited actions, ≥ 95% citation coverage.
Don’t enable suggestions on equipment-X until the slice meets the bar.
Update CoU, risk register, and change-control record.
Compatibility note: I run a continuous, risk-based lifecycle and map evidence to the CSA/GAMP guidance.
From Pilot to Production: A Practical Roadmap for LLM Implementation in GxP Environments
Editor’s note (Nov 17, 2025): This article has been updated to reflect a continuous, risk-based lifecycle consistent with GAMP 5 (Second Edition) and the ISPE GAMP AI guidance. Per GAMP 5 (2nd ed.), specification and verification are not inherently linear and fully support iterative, incremental methods. Where legacy terms (e.g., IQ/OQ/PQ) appear, they are provided as a crosswalk for teams whose SOPs still file that way.
What's the difference between an LLM that works and one that's validated for life sciences use? Everything.
When implemented safely, AI can bring intelligence, automation, and real-time decision-making to quality processes. But in life sciences, where errors can impact patient safety and regulatory compliance, bridging the gap between AI's potential and reality necessitates careful strategy and implementation.
From identifying a clear scope of use to monitoring and evaluation, the full lifecycle of a deployed LLM requires end-to-end validation.
While organizations own their validation destiny, the specialized nature of LLM validation often requires external expertise. Whether providing strategic frameworks, hands-on validation execution, or capability building, experienced partners can accelerate compliant AI adoption while avoiding common pitfalls.
Let’s walk through the process below . . .
Note: Before embarking on validation, organizations need a governance framework defining when and how LLMs can be considered. This isn't part of validation itself but rather the prerequisite “organizational readiness” that enables compliant AI adoption. Phase 1 then builds on this foundation with specific use-case documentation.
📍Phase 1: Definition & Risk Assessment
Definition- we must define the user requirements and do a thorough risk assessment for the LLM.
Organizations don't need to reinvent their validation approach for AI. A risk-based approach aligned with GAMP emphasizes comprehensive testing around AI-specific risks like hallucination, drift, and traceability. This evolution, not revolution, approach helps maintain regulatory compliance while addressing novel AI challenges. We’ve pre-built standard LLM additions, enabling seamless integration into your existing processes.
The URS and SOP work in tandem but serve distinct purposes. The URS defines what the system must do: its capabilities, limitations, and performance standards. The SOP defines how humans interact with that system: who can use it, when it's appropriate, and what procedures to follow. Together, they create a complete framework for compliant LLM use. Think of it this way: The URS ensures the LLM is fit for purpose. The SOP ensures it's used for that purpose.
📍Phase 2: Design & Development
To create a true fit-for-purpose LLM, we must ensure the model architecture aligns with risk level and use case. The outputs from Phase 1 directly inform our approach.
*Note: Unlike traditional software, LLM performance can degrade over time as production data evolves: a phenomenon called "data drift." This occurs when new products, updated SOPs, or changed terminology cause the production environment to diverge from training conditions. This reality shapes our design decisions, requiring built-in monitoring capabilities and clear revalidation triggers from day one.
Risk-Based Model Selection
High-Risk (patient safety, batch release):
Smaller, specialized models
Deterministic components (where possible)
Extensive guardrails and confidence thresholds
Medium-Risk (document review, categorization):
Balanced models
Commercial or open-source options possible
Emphasis on explainability features
Low-Risk (literature search, drafting):
Larger models acceptable
API-based solutions may be appropriate
Emphasis on performance over interpretability
📍Phase 3: Verification & Model Validation
Confirm correct deployment- model version verification
Fit-for-Purpose Qualification addresses LLM-specific testing:
Model verification against accuracy benchmarks (≥95% vs SME)
Use case validation with real-world scenarios
Integration testing with existing QMS systems
Performance Check demonstrates sustained performance with production data and confirms users can follow updated SOPs effectively.
📍Phase 4: Deployment & Control
Beyond technical deployment, successful implementation requires:
SOP revision: “AI-Assisted [Process Name] with clear oversight requirements
Training requirement: 2-hour session on reviewing/verifying LLM outputs
Output controls: All LLM-output marked as “Draft- Requires Review”
Change control: Model versions, prompts, and data pipelines under formal control
Audit trail: Complete traceability of inputs, model version, and human decisions
📍Phase 5: Continuous Monitoring & Improvement
Key Metrics to Track:
Model accuracy trending
Confidence score distribution
User override rates
Processing time per request
Revalidation Triggers (Defined in Advance)
New equipment types added
Changes to review criteria in SOPs
Model performance degradation <5% week-over-week
Regulatory guidance updates
Example: A deviation categorization LLM following this framework achieved 94% accuracy against SME review and reduced processing time from 4 hours to 30 minutes per batch.
Validating LLMs for life sciences isn't about reinventing validation—it's about thoughtfully embracing software tools and automation to improve higher quality and lower risks. Ready to accelerate your AI validation journey? Stay tuned for next week's deep dive on data drift.
Fit-For-Purpose LLMs: Why it Matters
Validated ≠ leaderboards. Recent studies show that large language models can be more agreeable than humans—optimizing for pleasing answers rather than correct ones. That’s entertaining in chat apps; it’s risky in life‑sciences workflows. The antidote is simple: design for fit‑for‑purpose, not applause.
The problem: helpful isn’t the same as correct
Most LLMs are tuned to be helpful and polite. In practice, that can morph into sycophancy—agreeing with the user’s assumption even when it’s wrong. In R&D and GxP‑adjacent settings, this shows up as:
False reassurance: an LLM gently validates a shaky hypothesis or casual assumption.
Label echo: the model over‑indexes on prior labels and quietly repeats them.
“Looks right” bias: well‑phrased but ungrounded answers that slip through review.
Bottom line: if you don’t explicitly design against sycophancy, you’ll ship it.
What “fit‑for‑purpose” actually means
“Fit‑for‑purpose” is not a vibe; it’s a measurement and operations problem:
Context of Use (CoU) + risk: who uses the model, for what decision, with which failure modes. Evidence depth matches impact.
Consequence‑weighted metrics: errors are not equal—weight them by business/clinical consequences.
Traceable, domain data: evaluation sets with lineage (ALCOA+), leakage controls, and real edge cases.
Pre‑registered acceptance criteria: metrics, thresholds, and sample sizes agreed upfront.
HITL & SOPs: clear review thresholds, escalation paths, and training—so "agreeable" outputs don’t slide through.
Monitoring & drift: golden‑set rescoring, quality KPIs, and ownership in production.
Change control for retraining: triggers, impact assessments, rollback, and signed release notes.
Anti‑sycophancy tests you should run
If your model can pass these, you’re on the right path:
Agreement‑vs‑truth: does the model side with a confident but wrong user, or with the evidence?
Dissent calibration: can it respectfully challenge a claim and cite sources?
Authority flip: does behavior change when the “speaker” is a junior analyst vs. a PI/manufacturer lead?
Self‑confidence checks: does it hedge appropriately when uncertain?
Grounding audits (for RAG): are citations real, relevant, and actually used in the answer?
R&D vs. regulated work: same measurements, scaled
In R&D, a lightweight credibility plan prevents “polite hallucinations” from steering experiments.
For GxP‑impacting steps, expand those measurements into formal V&V, audit trails, and independence in testing. The framework is the same; the rigor scales with risk.
Why this matters to regulators and QA
Health authorities and QA teams don’t ask for leaderboard screenshots. They expect risk‑based credibility tied to the model’s context of use, with documented operation, monitoring, and change control. If you can walk into an audit with that story, and evidence, you’re ready.
A simple flow that works
CoU → Risk → Eval Design → Acceptance Criteria → HITL → Monitoring → Change Control
Ship with this lifecycle in place and you’ll avoid the trap of “agreeable but wrong.”
What I deliver
R&D Fit‑for‑Purpose Sprint (2–4 wks): CoU & risk rubric • eval set + error taxonomy • acceptance criteria • small pilot • decision memo.
GxP Validate → Launch (6–10 wks): validation protocol & report • supplier qualification • change control • monitoring/drift • audit pack.
Monitor → Improve (retainer): golden‑set rescoring • drift watch • periodic re‑validation • release notes • inspection readiness.
CTA
Curious if your LLM is truly fit‑for‑purpose? Book a complimentary 30‑minute consultation. I’ll share a quick scorecard, highlight gaps, and recommend the smallest experiment that proves value.
Validation for LLMs: An interdisciplinary perspective
The advent of modern neural networks carries the promise of transforming industries worldwide. Yet, the “black box” nature of large language models (LLMs) introduces substantial risk — particularly in high-stakes domains such as life sciences and pharmaceuticals.
Effective validation requires more than code reviews or benchmark scores. It demands a risk-based, interdisciplinary approach that integrates expertise in both data science and the domain being modeled. A biologist, for instance, can spot when a generative model produces biologically implausible hypotheses that might escape a purely technical evaluator.
True validation extends beyond technical metrics. It involves translating complex architectures and training data assumptions into a transparent, testable framework — one that aligns with scientific rigor and regulatory expectations.
As AI systems increasingly shape discovery pipelines, interdisciplinary validation will become the foundation of trust. Building teams that bridge computational and domain knowledge isn’t optional; it’s the key to ensuring LLMs advance science responsibly, rather than simply accelerating it.