AI Governance for Pharmaceuticals.
Research scientists, clinical operations, regulatory affairs, and manufacturing quality teams are sending compound data, trial documentation, batch records, and adverse event reports into ChatGPT, Copilot, Azure OpenAI, and internal AI tools. The gateway between those users and the model is where 21 CFR Part 11, EU Annex 11, GxP data integrity, and ICH obligations need to be applied, because the content control plane the LLM provider offers stops at the model boundary and is blind to the customer policy.
DeepInspect runs inline in front of the AI provider. Research IP, trial subject data, pharmacovigilance case data, and regulated manufacturing records are detected and transformed before the payload leaves the customer environment. Every decision is written to a tamper-evident forensic record with the policy version, the actor identity, and the original and transformed payloads preserved. The same configuration applies to interactive chat, retrieval-augmented applications, and autonomous agent workflows that reach lab, clinical, and manufacturing systems.
The risk surface in pharmaceutical AI
Pre-competitive research data inside prompts
Research scientists and computational chemists paste assay results, compound structures, molecule identifiers, target data, and trial protocols into AI tools to summarize and analyze. This material is the most valuable IP a pharmaceutical company holds. Once the payload leaves the customer boundary, the LLM provider retention agreement is the only remaining control, and that agreement covers retention, not the act of disclosure.
GxP records that have to survive an inspection
FDA, EMA, and notified-body inspectors ask the same question of an AI-assisted GxP activity that they ask of any computerised system: who did what, when, against which procedure, and what did the system produce. Batch records, deviation reports, validation documentation, and CAPA narratives drafted or summarized with AI need a contemporaneous, attributable trail or the activity fails the data-integrity expectation.
Pharmacovigilance and adverse event data
Drug safety teams process individual case safety reports that carry patient identifiers and free-text clinical narratives. The reporting clocks under FDA and EMA rules are strict and the underlying data is regulated personal data. Pasting a case report into a general AI tool moves identifiable safety data outside the customer boundary and outside the agreement that was meant to govern it.
Regulatory submission content
Regulatory affairs teams draft eCTD modules, labeling, and responses to health authority questions with AI assistance. Submission drafts, health authority correspondence, and unreleased label text are confidential until filed, and an inadvertent disclosure can affect a filing or a market exclusivity position.
How DeepInspect applies controls
Compound, trial, and batch identifier detection
Deterministic detectors match compound and molecule codes, clinical trial registration numbers, subject and site identifiers, batch and lot numbers, and the data classes the company information classification scheme marks as restricted. Each match is redacted, tokenized, or blocked according to the configured action for the user role in effect. Tokenization keeps a reversible mapping inside the customer environment for downstream traceability while the upstream model sees only opaque tokens.
Identity-aware policy
Role identity is supplied by the customer IdP at request time. The gateway evaluates the per-role action map and applies the matching transformation. A computational chemist, a regulatory affairs writer, and a manufacturing quality reviewer receive different transformations on the same payload. The action map is part of the policy version, so role changes are captured in the audit trail.
Evidence-grade forensic record
Every interaction writes a signed record containing the actor identity, the policy version, the rule evaluation path, the original payload, the transformed payload, and the upstream response. The signature anchors integrity. The record satisfies the attributable and contemporaneous expectations that GxP data integrity places on a computerised system, and it is queryable by inspectors against a read-only projection.
Prompt injection and adversarial input handling
Adversarial inputs attempting to override instructions, extract trial protocols, or pivot an agent into restricted systems are scored against the configured detectors and blocked or routed to escalation according to policy. The score, the input, and the action are preserved in the forensic record.
Tool and agent allowlists
Autonomous agents in research and manufacturing workflows reach electronic lab notebooks, LIMS, manufacturing execution systems, regulatory submission platforms, and safety databases. The gateway enforces allowlists and blocklists on the tools an agent invokes and the data sources it reads. An agent that attempts to call a system outside its allowlist is stopped at the gateway with a record of the attempt.
Forensic deep analysis
Patterns across the forensic store surface anomalous access, repeated near-miss policy hits, and the slow exfiltration of research data that single-event monitoring misses. The analysis runs against the customer projection and produces queryable findings that map back to the source interactions.
Regulatory mapping
21 CFR Part 11
Cryptographically signed, time-stamped, attributable records of every AI interaction meet the electronic records requirements Part 11 places on regulated clinical, laboratory, and manufacturing systems. The record set is exportable in the formats FDA inspectors expect.
EU Annex 11
Annex 11 of the EU GMP guide governs computerised systems used in regulated manufacturing. Policy versioning produces the change-control trail Annex 11 expects, and the signed record covers the audit-trail and data-integrity clauses for any AI-assisted manufacturing or quality activity.
GxP data integrity and ALCOA+
The forensic record makes every AI-assisted GxP activity attributable, legible, contemporaneous, original, and accurate, with the complete and enduring properties the ALCOA+ extension adds. FDA and MHRA data-integrity guidance both turn on those properties, and the gateway produces them without a separate logging step.
ICH E6(R3) Good Clinical Practice
Sponsor-specific handling rules for trial protocols and subject data are encoded as per-role and per-route action maps. The policy version that applied to each interaction is preserved alongside the decision, which produces the contemporaneous record GCP inspections rely on.
GDPR
Trial subject data and pharmacovigilance case data are personal data, and safety narratives often qualify as special-category health data under Article 9. Detection and transformation apply Article 32 security of processing at the AI layer, and the forensic record supports the Article 30 record of processing activities.
EU AI Act
AI used in regulated clinical and product contexts can fall inside the high-risk category. Policy versioning produces the change-control trail relevant to Article 17. The forensic record covers Article 12 record-keeping. Inline enforcement with fail-closed default behavior addresses Article 9 risk management.
The scale of the gap
of organizations across surveyed sectors reported confirmed or suspected AI agent security incidents in the past year. Life sciences organizations sit at or above that average in every published industry breakdown.
of builders cite the absence of auditability and logging as a top concern. Only 7.7% audit agent activity daily, which leaves most organizations without the contemporaneous record that 21 CFR Part 11 and GxP data integrity require.
of teams treat AI agents as identity-bearing entities. The remainder authenticate agents with shared API keys or hardcoded credentials, which makes per-agent attribution and revocation impossible.
Deployment
The gateway runs self-hosted in the customer VPC or on-premises. SaaS and hybrid deployments are available for organizations with different sovereignty requirements. Research data, pharmacovigilance case data, the forensic store, and the transaction object store stay inside the customer boundary in every configuration.
DeepInspect sits inline between users, agents, and the AI provider. It works with OpenAI, Azure OpenAI, Anthropic, Bedrock, and internal models without requiring a model migration. Existing IdP, SIEM, and validated-system integrations stay in place. Production cutover typically lands inside two weeks for a defined application scope.
Policy on every AI interaction, enforced before data leaves the boundary.