The Drug Doesn't Get Approved. The Data Does.

An FDA inspector sits down for a pre-approval inspection and starts with a single number in the submission. She wants to know where it came from: which site captured it, which system logged it, whether the value in the final report still matches the value recorded the day a patient walked into a clinic.

The data trail behind that number has to hold at every link, all the way back. Break it anywhere and regulators stop trusting one number, then start questioning all of them.

One trial, a dozen systems, a hundred places for the places for the data trail

A modern clinical trial runs across contract research organizations (CROs), trial sites, electronic data capture systems, lab platforms, genomic sequencing pipelines, and cloud storage, often a dozen vendors deep before a single result reaches the sponsor's own systems.

Every handoff is a chance for something to break. A record gets duplicated or a timestamp goes missing. A value gets copied into a cleaner-looking file that quietly stops matching the original.

Regulators use the paperwork to decide whether to believe the science at all.

Five rulebooks, one dataset

Ask a life sciences company which regulation governs its data and the honest answer is several, all at once, with no coordination between them.

21 CFR Part 11 sets the bar for electronic records and electronic signatures in the U.S. Good Clinical Practice (GCP) covers data integrity across a trial through its own set of expectations. The International Council for Harmonisation's E8 and E9 guidelines want the statistical logic behind a trial's design documented along with its conclusions. The EU's Clinical Trials Regulation adds governance requirements and a reporting system of its own. Export control sits on top of all four. For drug discovery and genomic research, the International Traffic in Arms Regulations (ITAR) and the Export Administration Regulations (EAR) decide who can touch certain data, whatever the other frameworks say.

Most life sciences companies manage this as six separate compliance checklists, run by different teams and checked against different systems at different points in the trial. None of them can look at the same map: where the underlying data lives, who has touched it, whether it still matches where it started.

The cost of not having that map

That gap costs real money, not just compliance standing.

Data exposure and remediation costs run into the tens of millions for a typical life sciences organization once sensitive research and trial data has sprawled across so many systems that nobody has a current picture. Brand and reputational damage adds millions more. The operational drag lands in R&D itself, where a data integrity finding can stall a submission for months over a discrepancy that surfaced at the final regulatory review instead of the first data handoff.

The same gap shows up during acquisitions. Life sciences M&A means inheriting someone else's clinical and research data estate, and the standard approach to figuring out what's actually in it takes about 12 weeks per deal, done manually.  

Start with a real map of that data instead, and the integration timeline cuts roughly in half.

Map the data once, and every framework gets easier to satisfy

Satisfying all of them takes one accurate, current picture of where sensitive research and trial data lives, who can reach it, and whether it still matches its original record. Build that picture once and Part 11 audit trails, GCP integrity checks, ICH documentation, EU reporting, and export control restrictions can all draw on it, instead of on five disconnected sources.

Cyera builds that map from the sponsor’s side of the trial. It discovers and classifies research and trial data across the cloud, SaaS, and on-prem stores the sponsor controls, including copies that arrive from CROs and lab platforms. Access Trail records which people and AI agents opened each dataset. Data lineage shows where a file moved and what changed on the way, which is exactly what the inspector asked in the first paragraph.

The same classification tells security teams which datasets fall under ITAR or EAR and who outside the approved group can still reach them. In an acquisition, Cyera scans the target’s data estate before integration starts. That’s where the 12-week manual review gets cut down.

The FDA inspector will follow the data trail through every hand and system it touched, and the number at the end has to match the one recorded at the start.