By Jason Smith, CTO Within3
Every life sciences AI vendor will tell you their platform is secure. The implicit argument from major suite vendors is that application-level safeguards, restricted access controls, and built-in guardrails are sufficient to satisfy enterprise compliance requirements. The position is essentially: trust the product; the governance is handled.¹
That argument fails at the moment of expansion. You can get one AI workflow approved by a compliance committee. Getting the next five approved using the same undocumented approach costs just as much time and political capital as the first one. If you cannot show a compliance committee what happened inside a model run, which data it touched, how it was evaluated, and who reviewed the output before it was acted on, you are rebuilding your justification from scratch every single time.
The pharmaceutical companies that are scaling AI deployments in 2026 are not doing so because they trusted a vendor’s security posture statement. They are doing so because they can demonstrate controls.
What Regulators Are Actually Requiring
There is a tendency in industry to treat AI regulation as a future concern. That window has closed.
The European Medicines Agency (EMA) and Heads of Medicines Agencies (HMA) have published explicit guidance on the use of large language models in medicines regulation. That guidance names specific failure modes: hallucinations, data security risk, and the tendency toward overreliance on AI outputs.² These are not theoretical warnings. They reflect documented patterns in healthcare AI deployments. Published peer-reviewed evidence confirms that inconsistent evaluation approaches create material reliability gaps in healthcare language model performance and that these gaps are not reliably visible from general benchmark scores.¹⁵
The FDA’s framework for AI-assisted regulatory decision-making requires definition of the context of use, traceability of how data flows into a material decision, and documentation of model limitations aligned to that specific context.³ The EU AI Act introduces risk categorization and transparency obligations that apply to AI systems deployed in regulated industries. The EMA’s guiding principles for good AI practice make the lifecycle argument explicit: versioning, monitoring, and change control are not implementation details to be handled post-launch. They are required features of trustworthy AI from day one.¹¹
The practical implication for any life sciences organization deploying AI in 2026 is that it is no longer sufficient to say: “governance is built in”. Governance needs to be demonstrable, not implied. A compliance committee cannot approve what it cannot examine.
The Procurement Acceleration Argument
Here is the counterintuitive case: visible AI governance infrastructure reduces procurement cycle time.
A security questionnaire covering AI data handling, model versioning, audit log availability, and human review requirements is essentially the same questionnaire every enterprise pharmaceutical company sends when evaluating a new AI deployment. If the answers require custom investigation, the review takes months. If they are pre-answered with artifacts, the cycle shortens dramatically.
This means the AI evidence pack is not documentation overhead. It is a commercial accelerator. For every workflow, the evidence pack should include: which model version ran, which datasets were used, how the output was evaluated, what the documented limitations are, and which human reviewer signed off before the output was acted on. When that pack exists, a compliance conversation becomes a handoff. When it does not, it becomes an investigation.
The required capability stack to produce this is specific: immutable audit logs with export functionality, configurable human-in-the-loop routing, redaction and de-identification support for Protected Health Information (PHI), and model monitoring with documented change management. These are not aspirational features. They are the components that make an AI deployment repeatable across compliance reviews rather than exceptional.
HIPAA security safeguards apply to ePHI processed in these workflows.²⁶ GDPR applies to European HCP data with clear controller/processor role documentation.¹⁴ An ICH Q9-aligned risk assessment framework provides the pharmaceutical-grade risk governance logic that connects regulatory requirements to specific technical mitigations.²⁴ Each of these can be built-in-advance rather than discovered during a security review.
Reframing What “Trusted AI” Means in Practice
The market uses “trusted AI” as a positioning phrase. It should be a functional specification.
In a regulated organization, “Trusted AI” means there is a responsible person overseeing the AI who can answer four questions: What did this model do? What data did it use? How was the output evaluated before someone acted on it? If the output turns out to be wrong, how do we trace where it went wrong and prevent recurrence?
An AI system that cannot support those answers is not trusted. It is used under tolerance, until something goes wrong and those questions get asked in a less comfortable context.
Decisions in life sciences are consequential, and the stakes are high. Advisory board summaries inform launch strategy. HCP engagement patterns drive field deployment. Competitive intelligence shapes resource allocation. These are not informational suggestions. They influence material decisions with financial and regulatory consequences. The governance architecture around AI in these workflows needs to be proportionate to those stakes, not to the stakes in a general productivity tool context.
The organization-level shift required is a reframing of governance as infrastructure rather than overhead. Governance built upfront, embedded in the product rather than configured as an afterthought by the customer, is the type of governance required for scalability. Governance as infrastructure is what enables the second, third, and tenth AI workflows to move faster than the first. Governance treated as an afterthought limits the organization to one approved use case indefinitely, because the compliance review cycle never gets shorter.
The Metric That Demonstrates the Argument
If this reframing is correct, the metric that validates it is not the number of AI features deployed. It is the expansion rate of AI-enabled workflows after the first one is approved.
Organizations stuck at one approved workflow after eighteen months are experiencing the undocumented governance problem. The compliance review for the second workflow starts from zero because the first review produced no reusable artifacts. Organizations expanding from one to ten workflows over the same period built their first approval as a template, with reusable evidence packs, audit infrastructure, and human review design that generalizes across use cases.
Governance is not a speed bump. It is the compounding infrastructure that makes speed in a regulated environment sustainable. The organizations treating it as a product feature, rather than a compliance tax, are the ones that will have AI operating at scale in 2026 and beyond. Platforms that ship governance as first-class product functionality, as Within3 does with its audit-ready Trusted AI capabilities, reflect this philosophy rather than retrofitting it.³⁷
References
Veeva Systems. “Veeva AI Agents to Be Released Across All Veeva Applications.” https://www.veeva.com/resources/veeva-ai-agents-to-be-released-across-all-veeva-applications/
European Medicines Agency / Heads of Medicines Agencies. “Harnessing AI in Medicines Regulation: Use of Large Language Models (LLMs).” https://www.ema.europa.eu/en/news/harnessing-ai-medicines-regulation-use-large-language-models-llms
U.S. Food and Drug Administration. “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products.” https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
European Medicines Agency. “Guiding Principles for Good AI Practice in Drug Development.” https://www.ema.europa.eu/en/documents/other/guiding-principles-good-ai-practice-drug-development_en.pdf
European Parliament and Council. General Data Protection Regulation (GDPR). https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng
Published evidence on evaluation inconsistency in healthcare LLM deployments. JAMA. https://jamanetwork.com/journals/jama/fullarticle/2825147
International Council for Harmonisation. ICH Q9(R1) Quality Risk Management. https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2022_1219.pdf
U.S. Department of Health and Human Services. “HIPAA Security Rule Guidance.” https://www.hhs.gov/hipaa/for-professionals/security/guidance/index.html
Within3. Terms of Service. https://within3.com/term-of-service
