Regulatory Affairs
Dr. Rachel Benett
August 18, 2026
In more than fifteen years spent reviewing computer system validation (CSV) packages for regulated companies, I have watched a single unqualified spreadsheet macro delay a marketing approval by months. Artificial intelligence raises that stake by orders of magnitude. A model that screens trial candidates, drafts a safety narrative, or flags an out of specification batch is now part of the evidence an inspector will examine, and when that evidence cannot be explained or reconstructed, the finding lands on the sponsor, not the algorithm. For any team that has already moved these systems into production, that exposure is one they can no longer afford to defer.
Regulators moved quickly. In January 2025 the FDA published two draft guidelines that together describe how artificial intelligence is expected to behave inside a regulated environment: one for drug and biological products, and one for AI-enabled medical devices. The EMA has signaled a parallel posture through its reflection paper on artificial intelligence across the medicinal product lifecycle. Neither agency treats artificial intelligence as an exception to established expectations. Both expect the same discipline that governs every other GxP system: a defined intended use, documented validation, traceable data, and a named human who remains accountable for the output.
The first document, “Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products,” was issued on January 7, 2025. It applies wherever an AI model produces information used to support a regulatory decision on the safety, effectiveness, or quality of a product, spanning the nonclinical, clinical, postmarketing, and manufacturing phases of the product lifecycle. Its centerpiece is a risk-based credibility assessment framework organized around the “context of use,” meaning the specific role and scope of the model in answering a defined question. The companion document for devices, “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations” applies the agency’s total product lifecycle approach to any device containing an AI-enabled software function. Reading the two together is the fastest way to understand where AI compliance training for life sciences has to concentrate, and the official texts are available directly from the FDA.
Across both drafts the agency returns to the same recurring concerns, and these are the areas a compliance program is expected to address first:
None of this is codified as a binding rule yet. Both documents are drafts, and both closed their comment periods in April 2025. That distinction matters for a compliance audience: an inspector cannot cite a draft guidance as a regulation, but the expectations it sets out are already shaping how submissions are reviewed and how findings are framed.
In my experience, the single idea that most teams underestimate is that risk does not live in the model. It lives in the context of use. The same algorithm that suggests a candidate site for a trial carries a very different weight when it decides which adverse events reach a human reviewer. The FDA framework asks two questions in sequence: how much does the AI output influence the decision, and how serious is the consequence if that output is wrong. The answer places the model somewhere on a risk scale and determines how much evidence of credibility the agency expects to see. A well designed program of AI compliance training teaches teams to run that classification before a model is deployed, not after an inspector asks for it.
This is also where the seven-step credibility process becomes practical. Defining the question of interest, defining the context of use, assessing model risk, and then building a proportionate plan to establish credibility gives an organization a defensible narrative for each system. The value is not the paperwork. It is that the same logic an inspector will apply has already been applied internally, in advance, and documented.
In the packages I have reviewed, the same failure points appear again and again.The most common is data integrity applied to a moving target: the ALCOA+ principles that regulated companies know well for laboratory and manufacturing records — data that is attributable, legible, contemporaneous, original, and accurate, plus complete, consistent, enduring, and available — are far harder to demonstrate for a training dataset that was assembled once, transformed several times, and never version controlled. Teams that have already invested in data integrity and in computer software assurance (CSA) have a head start, because the same evidentiary habits extend naturally to models. The second failure point is explainability. A black-box output that cannot be traced to its reasoning is difficult to defend in an audit, and generative systems add a further hazard when they produce fluent text that is confidently wrong inside a regulated document. The third is human oversight that exists on paper but not in the workflow, where a reviewer clicks approve on a volume of AI output no person could realistically scrutinize. Programs that catch these three patterns before deployment rarely encounter them again during an actual inspection.
This course turns two regulatory frameworks into one workable, audit-ready method. Clients learn to classify, validate, document, and defend every AI system they deploy, from drug development and manufacturing to pharmacovigilance and quality, so that nothing is left to guesswork when an inspector arrives. It was built by a team of Regulatory Affairs Experts from Northeastern University, Boston, and this structured AI compliance training maps the EU AI Act timeline and the FDA AI guidance ecosystem onto the day-to-day decisions a regulated team actually makes. For an organization standing up its first governance program, the AI compliance course is the most direct route from uncertainty to a defensible position.
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The obligation falls first on regulatory affairs and compliance professionals, who must answer for an AI-supported claim in a submission, and they cannot answer for what they were never trained to interrogate. Quality assurance and IT validation specialists encounter the same shift from a different angle, now extending familiar CSV and CSA thinking to models that learn and drift rather than remain static. Clinical operations and data management teams meet the same challenge from the study side, where an algorithm quietly shapes which patients and which signals move forward, while pharmacovigilance colleagues carry it into signal detection and case processing, where a missed adverse event is not a statistic but a patient. Auditors complete the circle, because an audit of an AI governance program is only as credible as the auditor’s own grasp of context of use and credibility evidence.
In the 2026 market, the arithmetic is unusually clear. Deploying AI without training does not remove the cost of getting it wrong. It only postpones that cost, with interest. A single warning letter can freeze a product line and trigger a lengthy, costly remediation, and none of that appears on a budget until it arrives. Against that exposure, a structured program of AI compliance training is a negligible line item, and it produces something a remediation never does: a workforce that can classify, validate, and document AI before an inspector does it for them. The return is measured in submissions that are not rejected, inspections that do not escalate, and models that reach production with their credibility evidence already assembled.
GxP Training develops every course with practitioners who have sat on both sides of an inspection, so the material reflects how the guidance is read in the room rather than how it looks on a slide. Each completion earns a unique, verifiable, CPD and CEU accredited certificate that clients can share directly on LinkedIn and that an auditor can confirm through an online checker, which turns a training record into an audit-ready benchmark rather than a claim. Access runs for twelve months at a self-paced pace, so a busy regulated team can fit the material around real deadlines, and managers gain progress tracking and HR-ready tools to evidence completion across a group. Content is refreshed monthly as the guidance evolves, and courses are SCORM compatible for organizations that prefer to host learning inside their own systems.
The macro I watched delay an approval was a small thing that no one had qualified because no one thought it counted. Artificial intelligence is the same lesson at a far larger scale, and the agencies have now said so in writing. The teams that will move through 2026 without a preventable finding are the ones treating their models as regulated systems today, with defined use, traceable data, and accountable people, and the fastest way to build that discipline across a workforce is a single course of AI compliance training that turns the FDA guidance into practice before the inspector turns it into a finding.
