Validation and Security Testing of AI Tools in GxP Settings

• Head of Computer System Validation
• Head of Quality Assurance
• Digital Quality Lead
• Chief Information Security Officer

Quality, manufacturing and safety teams are adopting AI tools: SOP assistants, deviation drafting, adverse event screening, vision inspection and AI features inside QMS or LIMS products. Traditional CSV scripts were written for deterministic software, and most teams have no method yet for testing an LLM's outputs or its exposure to prompt injection and data leakage.

We run a risk-based assessment of an AI tool against its intended use. First we agree the use, risk level and acceptance criteria with QA and CSV. Then we build a test set from your own de-identified records and run it repeatedly to measure accuracy, consistency and refusal behaviour. In parallel we attack the tool: prompt injection through documents and inputs, attempts to retrieve other users' or sites' data, bypass of business rules, and checks that audit trails, access controls and electronic signatures behave as required. Findings come with reproducible evidence and fix guidance, assembled into a pack your validation team can reference.

• Intended-use and risk assessment aligned with your CSV or CSA procedure
• Task-specific test sets from your de-identified records, run repeatedly to measure consistency
• Prompt injection, jailbreak and data-leakage testing across documents, chat and API inputs
• Access-control and site-segregation tests for retrieval-based assistants
• Audit trail, electronic signature and change-control checks for the AI workflow
• Re-runnable regression suite for model, prompt or vendor version changes

1. 01Define intended use and riskWith QA and CSV we document what the tool is for, who relies on it and the GxP impact of a wrong output.
2. 02Build test setsWe create functional cases from your records and adversarial cases from known LLM attack patterns.
3. 03Run functional testsEach case runs several times and is scored against the acceptance criteria agreed in step one.
4. 04Run security testsWe attempt prompt injection, data leakage, privilege escalation and rule bypass, recording every attempt.
5. 05Review findingsResults are reviewed with QA, CSV and IT, and each failure is classed as acceptable, mitigated or blocking.
6. 06Deliver the evidence packA report, test logs and a regression suite are handed over for your validation file.

1. Scope and risk
   • Intended-use statement
   • Risk assessment with GxP impact
   • Acceptance criteria
2. Test assets
   • Functional test set from de-identified records
   • Adversarial prompt and document library
   • Test accounts per role and site
3. Test execution
   • Automated test runner against UI or API
   • Repeat-run consistency scoring
   • Attack attempt logger
4. Evidence
   • Findings with reproduction steps
   • Requirement-to-test traceability matrix
   • Regression suite for future changes

QA and CSV approve the intended use, risk level and acceptance criteria before testing starts, and decide how each finding is dispositioned. We provide evidence and recommendations; the validation decision stays with your quality unit.

• The AI tool under test (web interface, API or vendor platform)
• QMS for change control and CAPA records
• Validation document management system
• Identity provider for role-based test accounts
• CI pipeline for regression runs, where available

• Testing only in agreed environments under written authorisation
• De-identified or synthetic data in all test sets
• No testing against production patient or batch data
• Findings shared only with named stakeholders
• Regression suite re-run after any model, prompt or version change

• Evidence is structured to support validation under 21 CFR Part 11 and EU GMP Annex 11, using risk-based approaches such as ISPE GAMP 5 and FDA's Computer Software Assurance guidance.
• EU regulators consulted in 2025 on a draft GMP Annex 22 on artificial intelligence; the pack is organised so it can be mapped to the final text.
• Security testing references the OWASP Top 10 for LLM Applications and the NIST AI RMF.

• Pharma & Life Sciences

  Regulatory Dossier and SOP Q&A Assistant

  A question-answering assistant over your effective SOPs, specifications and CTD dossier modules that cites the exact section, respects versions and access rights, and says when it does not know.

• Pharma & Life Sciences

  Batch Manufacturing Record Review

  Pre-review of executed batch manufacturing and packaging records that flags blank fields, out-of-limit values, signature gaps and unlinked deviations, with page references, before QA starts.

• SaaS & AI Product Companies

  RAG Data-Leakage and Access-Control Testing

  Test whether your RAG pipeline leaks documents across users, roles or tenants through retrieval, caching, citations or logs, using seeded canary documents and role-based test personas.

• SaaS & AI Product Companies

  EU AI Act and ISO/IEC 42001 Evidence Pack

  Assemble testing records, risk assessments, oversight design and technical documentation for your AI features, mapped to EU AI Act and ISO/IEC 42001 requirements, ready for counsel and auditors.