What's Next For Biotech Regulation In 2026

If there is one constant in life sciences and biotechnology, it's uncertainty. Even so, 2025 brought a level of complexity that feels different—a meaningful shift in how regulators think, what they expect, and the tools they use to get there. In this environment, companies that are organized, transparent, and methodical tend to fare better when the government calls. Since 2017, more than $1 billion has been collected through Anti-Kickback Statue (AKS) and False Claims Act (FCA) settlements, and federal agencies show no sign of slowing down.

For in-house counsel and compliance leaders, the challenge is not focusing on every possible risk—there are too many—but understanding where the ground is shifting now and what may matter most in 2026.

A Changing Enforcement Climate

One of the clearest developments in 2025 has been the government's growing interest in how companies actually operate—how systems are validated, how data is checked, and how decisions are made.

Regulators across federal (FDA, CMS, DOJ, OIG) and state agencies are increasingly coordinating their enforcement and oversight efforts, driving greater scrutiny in areas such as:

• Failures in quality systems linked to digital tools
• AI-supported clinical documentation lacking traceability
• Inconsistencies in reimbursement submissions
• Gaps in oversight of third-party digital health vendors

The assumption that “digital” means “flexible” no longer holds. As regulators seek more visibility into operations and decision-making, companies may need to explain—and substantiate—their processes, especially when automation and AI are involved.

FDA: New Questions, New Pressure Points

As FDA continues to rely on familiar enforcement tools —warning letters, Form 483s, recalls, injunctions, criminal penalties— we're also seeing regulators deploy additional mechanisms like cease and desist letters. At the same time, the focus of inspections is expanding. While manufacturing quality remains central, inspectors are now probing systems implementation issues that were rarely raised even two years ago, including:

• How were AI-generated documents reviewed and validated?
• What controls govern automated analyses?
• In decentralized or hybrid trials, how was data consistency verified across sites and systems?

Companies that can clearly demonstrate decision-making logic, validation steps, and governance around digital systems will likely be better positioned for 2026 inspections.

CMS: From Accuracy to Accountability

Previously, CMS audits focused heavily on whether numbers were correct; however, 2025 marked a shift toward process accountability. CMS increasingly wants insight into the governance behind pricing submissions, inflation-rebate calculations, utilization reporting, and coding.

Instead of solely asking, “Are these numbers right?” regulators now want companies to be ready to answer at a deeper level:

• Who generated them?
• How were they reviewed?
• Which functions were involved?
• What controls ensure consistency?

This shift is particularly challenging for companies with siloed pricing functions. Going into 2026, they can expect increased pressure to demonstrate not just accurate output, but a coherent, integrated governance process across all functions.

Data Privacy: Understanding Ecosystems, Not Just the Rules

Even as privacy laws continue to change, 2025 marked a turning point: regulators are now less focused on what policies say and more focused on whether companies truly understand their data ecosystems.

Regulators will no longer accept reliance on vendor assurances alone. They will expect evidence of operational sophistication—for example:

• Data-flow mapping across the enterprise
• Monitoring of AI training data
• Insight into how third parties collect, store, and use health information

At the same time, more states are adopting digital health privacy laws that impose strict requirements for consent, transparency, and data handling—often exceeding HIPAA and sometimes conflicting with obligations under GDPR or PIPL. To remain compliant and agile, companies will need to continuously reconcile overlapping requirements and maintain defensible documentation.

AI in Research and Clinical Development

AI is now embedded across drug discovery and development, with companies using models to draft protocols, interpret imaging, triage safety cases, and clean trial data. In this environment, regulators are less concerned about whether AI will make mistakes and more concerned with whether companies understand their models well enough to explain how those mistakes occur—and how they are remediated in service of patient safety while still realizing efficiency gains.

Expect greater scrutiny in 2026 over areas such as:

• How models were trained
• What assumptions they rely on
• How outputs were validated or corrected
• How traceability is ensured across systems

AI-generated documentation will likely be held to the same standard as human-authored records. Companies could remain fully accountable for oversight—even when using third-party tools. “The model did it” is not, and may never be, a defensible explanation.

AI in Research and Clinical Development

Federal agencies remain insistent on conflict-of-interest transparency, and universities are increasingly under pressure to account for data-sharing and intellectual property ownership.

Biotech companies feel this pressure when navigating publication timelines, data-sharing practices, and software ownership in collaborations with academic institutions. In clinical research, for example, sponsors are increasingly discovering that IRBs vary widely in sophistication—sometimes leaving sponsors to fill compliance gaps themselves.

Heading into 2026, these issues are likely to intensify as regulators sharpen their focus on secondary data use and inter-institutional data flows.

Looking Ahead to 2026

Two trends stand out as likely inflection points:

1. AI-Specific Inspection Standards FDA is signaling that detailed expectations for validating AI-enabled quality systems and automated documentation tools are coming. By 2026, AI-specific Form 483s are a real possibility.
2. Increased Scrutiny of Data-Sharing Arrangements Regulators are examining how health data moves between companies, academic institutions, digital health partners, and data brokers. This is likely to result in new expectations around consent, transparency, contractual controls, and oversight of secondary uses and AI training data.

Conclusion

In a landscape shaped by evolving technology, interconnected data ecosystems, and cross-agency alignment, the fundamentals matter more than ever. Companies that document decisions clearly, escalate concerns early, and maintain strong cross-functional communication may be better positioned to navigate ambiguity effectively than those searching for perfect answers.

Regulators rarely expect perfection—but they do expect clarity, consistency, and good judgment. In an industry defined by grey areas, those fundamentals are what may keep companies on solid ground in 2026.

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