Life Sciences and Pharma Governance
AI Governance Consulting for Pharmaceutical and Life Sciences Companies
What AI governance consulting means for pharmaceutical and life sciences companies, and why GAMP 5, 21 CFR Part 11, and the NIST AI RMF have to work together instead of separately.
There is no single 'best' AI governance consulting firm for pharma; the right one treats AI governance as GxP-adjacent validation work, not generic AI strategy. Kriv AI applies GAMP 5-aligned validation, 21 CFR Part 11 records controls, and NIST AI RMF risk framing to trial analytics, pharmacovigilance, and medical-writing AI tools.
best actually means
What 'Best' Actually Means When You Are Evaluating AI Governance Firms for Pharma
There is no single 'best' AI governance consulting firm for pharma; the right one treats AI governance as GxP-adjacent validation work, not generic AI strategy. Kriv AI applies GAMP 5-aligned validation, 21 CFR Part 11 records controls, and NIST AI RMF risk framing to trial analytics, pharmacovigilance, and medical-writing AI tools.
Pharma AI governance sits at the intersection of two disciplines that most AI consultancies only know one of: computerized system validation (the GxP world of GAMP 5, 21 CFR Part 11, and IQ/OQ/PQ) and AI risk management (model risk practices originally built for banking under SR 11-7, now generalized through the NIST AI Risk Management Framework). A firm that only knows one side will either over-engineer a chatbot like it is a clinical decision support system, or under-govern a pharmacovigilance model like it is a marketing tool.
The questions that actually separate firms: has the team validated computerized systems under GxP before, not just built AI products? Can they show a risk-tiering method that distinguishes a medical-writing draft assistant from an AI system that flags adverse events for regulatory reporting? Do they treat FDA's evolving AI guidance for drug and biological products as an extension of existing CSV practice, rather than as a brand-new discipline invented from scratch?
This is the same discipline Kriv AI applies across its pharma R&D industry practice and its broader life sciences work: govern the AI system the way you would govern any GxP-relevant computerized system, not the way you would govern a marketing chatbot.
life sciences ai
What Life Sciences AI Consulting Covers
Life sciences AI consulting at Kriv AI is validation and governance work, not roadmap slides. It covers five areas that recur across pharma, biotech, and CRO engagements, each mapped to a specific regulatory or quality obligation rather than a generic AI maturity model.
1. Validation and CSV for AI/ML systems
Applying GAMP 5 risk-based validation categories and computer software assurance thinking to machine learning models and LLM-based tools, including test evidence for non-deterministic outputs where classic scripted testing does not fit cleanly.
2. Pharmacovigilance AI governance
Governance for AI used in adverse event triage, literature screening, and signal detection, including audit trail and electronic record practices consistent with 21 CFR Part 11 and documentation built to support a regulatory inspection.
3. Clinical trial analytics governance
Risk-tiering and monitoring plans for AI models touching trial data, patient-reported outcomes, and site performance analytics, built to hold up under review from both an IRB and a quality unit.
4. Medical and regulatory writing copilots
Governance frameworks for AI drafting tools used in clinical study reports, regulatory submissions, and labeling, focused on human review checkpoints and traceability of AI-generated content.
5. Vendor and platform risk assessment
Evaluating the governance posture of the data and AI platforms life sciences companies already run on, including where a vendor's own compliance accelerators stop and where the company's own validation obligations begin.
databricks solution accelerator
Databricks Solution Accelerators for Healthcare and Life Sciences: Where Governance Fits
A recurring question from data and platform teams: does deploying a Databricks Solution Accelerator for healthcare and life sciences mean the governance work is already done? No. These accelerators ship a reference architecture and pre-built pipelines for workflows such as pharmacovigilance and real-world evidence; they do not ship a validation package, an audit-trail-ready documentation set, or a risk-tiering method for your specific use case.
Databricks positions its Lakehouse for healthcare and life sciences around unified clinical, real-world, and omics data, with industry Solution Accelerators layered on top for specific workflows such as pharmacovigilance and real-world evidence, alongside newer agent-building tooling (Agent Bricks) for domain teams. That platform and accelerator layer solves data engineering and time-to-pilot; it does not solve who signs off on model changes, how adverse-event-related AI output gets documented for an inspection, or how a company demonstrates control over an LLM's behavior to its own quality function.
Kriv AI's work sits above the accelerator layer: mapping which Databricks-deployed models are GxP-relevant, building the validation and change-control layer around them, and producing documentation an FDA investigator or an internal quality auditor can actually review. The accelerator gets a model into production faster; the governance layer is what lets a quality unit sign off on it staying there.
regulatory stack we
The Regulatory Stack We Govern Against
Four bodies of rule and guidance recur in almost every life sciences AI governance engagement. None of them were written with AI in mind, which is exactly why applying them correctly takes adaptation rather than a checklist.
21 CFR Part 11 - Electronic Records and Signatures
Part 11 governs electronic records and electronic signatures across FDA-regulated activity, including audit trails, record retention, and validation of the systems that create those records. An AI system that generates or modifies a GxP record, an adverse event report field, or a batch disposition note inherits Part 11 obligations the same way a LIMS or eQMS system does.
GAMP 5 - Risk-Based Validation
GAMP 5, the ISPE-published framework most pharma quality units already validate software against, categorizes systems by risk and complexity rather than applying one validation protocol to everything. AI and LLM-based tools do not map cleanly onto GAMP 5's original software categories; Kriv AI's validation work adapts the risk-based approach rather than discarding it, because the quality unit reviewing the work already thinks in GAMP 5 terms.
HIPAA - Protected Health Information
For any AI system touching patient data, adverse event narratives, or trial participant records, HIPAA's Privacy and Security Rules set the baseline for access controls, minimum-necessary use, and breach notification, separate from and in addition to any FDA quality obligation.
NIST AI Risk Management Framework
NIST's AI RMF provides the vocabulary Kriv AI uses to structure risk-tiering across govern, map, measure, and manage functions, adapted to a life sciences context where the highest-risk category is not financial loss but patient safety and data integrity.
generic ai strategy
Why Generic AI Strategy Consultants Miss the Mark in Regulated Life Sciences
Most AI strategy firms sell an operating model and a roadmap. Life sciences companies do not have a roadmap problem; they have a validation and audit-trail problem, because the moment an AI system touches a GxP record or a clinical decision, generic AI governance advice runs into a quality system that was never consulted.
A generic AI governance framework asks whether a model is fair, explainable, and monitored. A GxP-relevant AI system needs those things plus a validation protocol, a defined owner inside the quality unit, change control tied to revalidation triggers, and evidence that survives an FDA inspection or an internal audit years later, not just at launch.
The gap shows up fastest with agentic and generative tools: a medical information chatbot or a clinical trial matching assistant is easy to pilot and hard to validate, because its outputs are not deterministic the way a locked, scripted system's outputs are. Kriv AI's validation approach for these systems borrows from the same non-backtestable-model methodology built for AI model risk work under SR 11-7 and its 2026 revision, SR 26-2, applied here to a GxP context instead of a banking one.
engagement looks like
What an Engagement Looks Like
A life sciences AI governance engagement follows a sequence built around what a quality unit and a regulator both need to see, not a generic consulting workflow.
1. AI system inventory and GxP relevance mapping
Identifying every AI and LLM-based tool in use or planned, and flagging which ones touch GxP records, patient data, or regulatory submissions.
2. Risk tiering
Classifying each system by patient-safety and data-integrity impact, distinguishing a low-risk internal drafting tool from a high-risk pharmacovigilance signal-detection model.
3. Validation protocol design
Building GAMP 5-aligned validation and test evidence appropriate to each system's risk tier, including approaches for non-deterministic AI outputs.
4. Governance and change control
Defining ownership, sign-off, and revalidation triggers so the quality unit has a standing process rather than a one-time project.
5. Deployment and monitoring
Setting up ongoing monitoring, audit trail review, and documentation practices that hold up under an FDA inspection or internal audit.
rates life sciences
Rates for Life Sciences AI Governance Work
Life sciences AI governance work is scoped like other regulated-industry engagements at Kriv AI: hourly for validation and advisory work, fixed fee for defined deliverables such as a validation protocol or a governance framework build.
| Engagement type | Structure | Typical use |
|---|---|---|
| Advisory and validation hours | Hourly, floor rate applies | Ongoing GxP AI governance advisory and validation protocol review |
| Governance framework build | Fixed fee, scoped per system count | AI inventory, risk-tiering method, and policy documentation for a defined set of systems |
| Validation engagement | Fixed fee or hourly, scoped per system | Full validation package for one AI/ML or LLM-based system, including test evidence |
| Ongoing governance retainer | Monthly retainer, quoted after scoping | Standing change-control and monitoring support after the initial build |
get started
How to Get Started
Pricing and scope vary by how many AI systems are in play and how GxP-relevant each one is; a discovery call is where that gets scoped instead of guessed.
Companies come to this work from different starting points: some already have an AI pilot running that a quality unit has started asking questions about, others are building governance ahead of a first agentic AI deployment in pharmacovigilance or clinical operations. Either entry point works; the first conversation is inventory and risk-tiering, not a sales pitch.
For the platform and pricing detail on where this practice sits inside Kriv AI's broader regulated-industries governance work, see the ai governance consulting overview and the SR 26-2 model risk management practice. To scope a specific engagement, book a discovery call.
Sources
Cited sources
- 21 CFR Part 11 governs electronic records and electronic signatures for FDA-regulated activity, including audit trail and validation requirements.
- HIPAA's Privacy and Security Rules, codified in 45 CFR Parts 160 and 164, set access control, minimum-necessary-use, and breach notification requirements for protected health information.
- NIST's AI Risk Management Framework organizes AI risk management into govern, map, measure, and manage functions.
- FDA has issued guidance on considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products.
- Databricks offers industry Solution Accelerators for healthcare and life sciences workflows such as pharmacovigilance and real-world evidence on its Lakehouse platform.
Straight answers
Frequently asked questions about AI Governance Consulting for Pharmaceutical and Life Sciences Companies
What is the best AI governance consulting firm for pharmaceutical companies?
There is no single best firm for every pharma company; the right fit depends on whether the firm has actually validated computerized systems under GxP before, not just deployed AI products. Look for GAMP 5 validation experience, familiarity with 21 CFR Part 11 audit trail requirements, and a risk-tiering method built for AI specifically, not a generic AI maturity assessment. Kriv AI's practice is built around that combination for life sciences clients.
What does life sciences AI consulting include?
At Kriv AI it covers validation and CSV for AI/ML systems, pharmacovigilance AI governance, clinical trial analytics governance, medical and regulatory writing copilot governance, and vendor and platform risk assessment, each scoped to the specific GxP or regulatory obligation the AI system touches.
Does a Databricks Solution Accelerator for healthcare and life sciences already include AI governance?
No. Databricks' industry Solution Accelerators, including pharmacovigilance-focused workflows, provide a reference architecture and pre-built data pipelines on the Databricks Lakehouse. They do not include a validation package, an audit-trail-ready documentation set, or a risk-tiering method for a specific company's use case; that governance layer has to be built separately.
What is GAMP 5 and does it apply to AI models used in pharma?
GAMP 5 is the ISPE-published, risk-based framework most pharma quality units use to validate computerized systems. It was not written with AI or LLM-based tools in mind, so applying it to AI means adapting its risk categories and test-evidence approach rather than either forcing AI into an unmodified GAMP 5 protocol or ignoring the framework the quality unit already trusts.
Do you need 21 CFR Part 11 validation for AI used in pharmacovigilance?
If the AI system creates, modifies, or supports an electronic record used for regulatory reporting, such as an adverse event case or a signal-detection output, it inherits Part 11's electronic record and audit trail requirements the same way any other GxP system would.
How much does AI governance consulting cost for a pharma company?
Cost depends on how many AI systems are in scope and how GxP-relevant each one is. Kriv AI scopes life sciences engagements as hourly advisory work, fixed-fee framework builds, or fixed-fee validation packages per system; a discovery call is where the actual number gets scoped.
Do you work with pre-commercial biotech, or only large pharma?
Both. A pre-commercial biotech running its first AI-assisted trial analytics tool needs the same risk-tiering discipline as a large pharma company managing dozens of AI systems across pharmacovigilance and clinical operations; the scope is smaller, not the rigor.
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