Pharmaceutical and life sciences companies are entering a new phase of digital transformation. AI is embedded in GxP workflows, influencing quality investigations, deviation management, safety signal detection, supply chain decisions, and regulatory documentation.
In this environment, compliance must shape how decisions are made as systems run. This shift has profound implications for how ethics and compliance leaders think about data protection, governance, and audit readiness.
The limits of traditional compliance models in regulated environments
Most compliance frameworks across pharma and life sciences were designed for systems that behaved predictably. Data was structured, workflows were linear, controls were predefined, and audits were retrospective. Even when AI was introduced, governance often remained static.
Modern, AI-driven compliance workflows behave differently. They pull data dynamically from multiple systems, adapt to context, and generate new intermediate information as they reason. In these workflows, risk can emerge mid-execution. This means:
- A workflow may begin as non GxP and become GxP.
- A dataset may be benign until it’s combined with patient or product context.
- A decision may cross a regulatory threshold without any explicit handoff.
Static controls are blind to these transitions. And in regulated environments, the same information may be acceptable for exploratory analysis, restricted during regulated decision making, or prohibited once patient impact is introduced.
Agentic workflows compound compliance risk. At scale, even small gaps repeat thousands of times before they’re detected. What would’ve been a minor issue in a pilot becomes a systemic exposure in production. This is why point in time reviews and static controls are insufficient for agentic systems. Without continuous governance during execution, risk increases with every turn.
A real example in life sciences
A global life sciences organization deploying AI across quality and safety operations encountered this challenge directly. The company had strong SOPs, established GxP controls, and mature GRC tooling. What it lacked was real-time visibility into how AI systems behaved once deployed.
Key risks included overexposure of sensitive manufacturing and patient related data, inconsistent masking across roles and use cases, manual and time intensive audit preparation, and hesitation from compliance leaders to approve broader AI rollout. The result was slower adoption and increasing operational risk.
Rather than modifying each AI system individually, the organization implemented a centralized governance layer that sat above existing workflows. This approach enabled continuous monitoring of AI driven decisions, context-aware data masking based on task, role, and regulatory state, enforcement of compliance thresholds during execution, and automatic generation of audit ready evidence.
Importantly, this governance operated without disrupting validated systems, preserving GxP integrity while improving control and transparency.
Within the first two quarters, the organization saw clear impact.
- Audit preparation effort reduced by approximately 45%.
- Manual compliance review cycles were cut nearly in half.
- Multiple potential data exposure scenarios were prevented before escalation.
From a business perspective, the reduction in compliance overhead and delayed deployments resulted in estimated annual savings of over $1 million. More importantly, ethics and compliance leaders gained the confidence to approve additional AI use cases that had previously been paused.
What regulators are starting to expect
Ethics and compliance leaders across pharma and life sciences are being asked to answer new kinds of questions. Standards are evolving from “Did this comply?” to “What data was available, why was it available in that context, and how can we prove appropriate controls were applied?” Answering these questions requires governance that operates inside live workflows. Compliance must become operational.
Inspectors want to understand how decisions were made, what data informed them, how controls adapted in real time, and whether governance was proactive or reactive. Organizations that rely solely on static controls struggle to answer these questions. Those with in-flight governance can.
The most effective compliance strategies today embed governance directly into operational systems. They generate evidence by default, allowing ethics and compliance leaders to move from gatekeepers to enablers and supporting faster, safer adoption of AI across pharma and life sciences.
The strategic advantage for ethics and compliance leaders
In highly regulated environments, trust is built on control, traceability, and evidence. As AI becomes part of the operational backbone, compliance must evolve from a static function to a living capability, one that operates continuously, adapts to context, and proves its value every day.
For organizations navigating this transition, the most productive next step is often a practical conversation, grounded in real production workflows, about how in-motion governance can be implemented without disrupting validated systems. Teams at Turing work closely with ethics and compliance leaders in pharma and life sciences to help turn this shift from a risk into a durable advantage.
Talk to a Turing Strategist about implementing continuous, in-flight compliance without disrupting validated systems.
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