Life Sciences AI Governance
AI Model Credibility Framework for FDA Drug Submissions
The FDA does not ask whether your AI model is good in general. It asks whether the model is credible for one defined use. Kriv AI helps sponsors turn the draft framework into a credibility assessment plan and report reviewers can follow.
The FDA's January 2025 draft guidance asks drug sponsors to show an AI model is credible for its specific context of use through a seven-step, risk-based framework. Risk is set by how much the model influences a decision and how severe a wrong decision would be. Kriv AI's life sciences governance work starts at $200 per hour.
context
What the FDA Draft Guidance Actually Asks For
In January 2025 the FDA published draft guidance titled Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (docket FDA-2024-D-4689). It gives sponsors a risk-based credibility assessment framework for AI models whose output supports decisions about the safety, effectiveness, or quality of a drug.
Scope: when the framework applies
The guidance covers AI models used in the drug product life cycle, which it defines as nonclinical, clinical, postmarketing, and manufacturing phases. It does not address AI used in drug discovery, or AI used for operational efficiencies such as internal workflows, resource allocation, or drafting a regulatory submission, where the use does not affect patient safety, drug quality, or the reliability of study results. The FDA encourages sponsors unsure whether their use falls in scope to engage with the agency early.
In its announcement, the FDA said the draft reflects its experience with more than 500 drug and biological product submissions with AI components since 2016, along with feedback that included more than 800 comments on two 2023 discussion papers. The FDA's guidance page, which we fetched on 30 September 2026, still lists the document as draft guidance dated January 2025. Draft guidance is nonbinding and describes the agency's current thinking, so sponsors should confirm the current status before relying on it.
Credibility means credibility for a context of use
The FDA defines credibility as trust in the performance of an AI model for a particular context of use, and context of use as how the model is used to address a certain question of interest. The same model can need very different evidence depending on the question it answers and what other evidence sits beside it.
ai gap
The Seven Steps, and Where Sponsors Get Stuck
The framework runs in seven steps: (1) define the question of interest, (2) define the context of use for the AI model, (3) assess the AI model risk, (4) develop a plan to establish credibility within the context of use, (5) execute the plan, (6) document the results and discuss deviations from the plan, and (7) determine the adequacy of the model for the context of use.
Step 3 is where the framework does its real work. Model risk combines two independently rated factors: model influence, meaning how much the AI output contributes relative to other evidence used to answer the question, and decision consequence, meaning how significant an adverse outcome would be if the decision were wrong. A model that is one of several evidence sources on a low-consequence question sits at the low end. A model that is the sole basis for a high-consequence decision sits at the high end, and the credibility activities in the plan should scale to match.
Step 4 is where effort concentrates. The draft asks sponsors to describe the model and its development process, including the data used to develop it and how it was trained, and to describe the model evaluation process. The plan can be discussed with the FDA through an existing engagement route before it is executed. Steps 5 and 6 produce a credibility assessment report that records results and any deviations from the plan, and the sponsor should discuss with the FDA whether, when, and where to submit it.
Step 7 is easy to overlook. If credibility is not sufficiently established for the model risk, the draft lists options: reduce the model's influence by adding other types of evidence, raise the rigor of the assessment or add development data, add controls to mitigate risk, change the modeling approach, or reject or revise the context of use.
capabilities
What a Credibility Engagement Covers
Question of interest and context of use
Written definitions for each AI model that supports a regulatory decision: the exact question, the model's role and scope, and what other evidence is used alongside it. These definitions drive every later step, so we settle them first.
Model risk assessment
A documented rating of model influence and decision consequence for each context of use, with the reasoning a reviewer can follow, so the depth of later evidence is justified rather than assumed.
Credibility assessment plan and report
A plan covering model description, development data, training, and evaluation, and a report template that records results and deviations. Both are built to stand up whether they are submitted or held for inspection.
Life cycle maintenance
Monitoring and change control so a model stays fit for its context of use after deployment. The draft notes that performance can change over time or across deployment environments, and that a model change may require re-executing parts of the plan, including retraining and retesting.
differentiation
Where This Differs From Our Other Life Sciences Pages
This page explains the FDA's AI credibility framework for drug and biological product submissions. Our 21 CFR Part 11 and GxP validation page covers electronic records and computerized system validation, and our page on why AI validation fails under Part 11 covers common failure modes. Our pharmacovigilance audit page addresses safety-surveillance inspections. The credibility framework sits alongside those requirements and does not replace them.
engagement
How an Engagement Works
A scoped engagement usually starts with an inventory of AI models that touch regulatory decisions and a scoping check against the draft guidance. We then define the question of interest and context of use for each model, rate model risk, and draft a credibility assessment plan. We work alongside your regulatory, quality, and data science teams. We do not submit to the FDA on your behalf and we do not provide legal advice.
tiers
What You Get at Each Tier
1. Enterprise / regulated (pharma, biotech, CROs)
Context-of-use definitions, model risk ratings, credibility assessment plans and report templates, and life cycle monitoring design documented for regulatory and quality review.
2. Fractional CTO / AI governance lead
Ongoing oversight of credibility evidence, model changes, and FDA engagement preparation as your AI portfolio grows.
3. Specialized advisory
A single session or second opinion on a credibility plan or model risk rating already drafted.
rate card
Kriv AI's Rates for This Work
These are Kriv AI's own published rate floors, not an industry average.
| Track | Kriv hourly rate | Typical engagement model | Minimum engagement |
|---|---|---|---|
| Enterprise / regulated (banks, broker-dealers, payment processors) | From $200/hr | Fixed-scope project or retainer | $8,000 |
| Fractional CTO / AI governance lead | $300 to $400/hr | Part-time, ongoing (monthly) | $8,000 |
| Specialized advisory (vendor evaluation, second opinion) | $400 to $700/hr | Hourly, per-session | Varies by engagement |
| Small business | $150/hr | Referred to Kriv AI's partner network | n/a |
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How to Get a Real Quote
The rates above are floors, not a quote. Actual price depends on how many AI models support regulatory decisions, how much development and validation documentation already exists, and whether FDA engagement is planned. Book a discovery call and we will scope it honestly.
Sources
Cited sources
- FDA, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, draft guidance (January 2025)
- FDA, guidance page for the draft guidance (docket FDA-2024-D-4689)
- FDA news release, FDA Proposes Framework to Advance Credibility of AI Models Used for Drug and Biological Product Submissions
Straight answers
Frequently asked questions about AI Model Credibility Framework for FDA Drug Submissions
What is the FDA AI credibility framework?
It is a seven-step, risk-based framework in the FDA's January 2025 draft guidance for establishing trust in an AI model's output for a specific context of use in drug and biological product submissions.
Is the guidance final?
The FDA's guidance page, fetched 30 September 2026, still lists it as draft guidance dated January 2025. Draft guidance is nonbinding, so confirm current status on the FDA site before relying on it.
What is a context of use?
The FDA defines it as how an AI model is used to address a certain question of interest. It sets the model's specific role and scope, and every credibility activity is judged against it.
How is AI model risk determined?
By combining model influence, the weight of the AI output relative to other evidence, with decision consequence, the significance of an adverse outcome from an incorrect decision. The two are rated independently.
Does it apply to AI used for drug discovery or drafting a submission?
No. The draft excludes drug discovery and operational efficiencies that do not affect patient safety, drug quality, or study result reliability. The FDA encourages early engagement if scope is unclear.
What does this cost with Kriv AI?
Kriv AI's rates start at a $200/hr floor for enterprise and regulated life sciences work, with an $8,000 minimum engagement. Specialized advisory runs $400 to $700/hr.
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