Navata Library territory
Regulated AI
This territory provides direct answers about AI reliance, authority, evidence, change and human accountability in regulated work. It distinguishes model performance from the broader architecture required to explain a consequential outcome, including inputs, prompts, retrieval, orchestration, permissions, review and retained records. Pages avoid presenting emerging practice as settled regulation and identify where interpretation or client judgement remains necessary. Related guidance links to Navata’s original governed-AI analysis and to assurance work for broader reliance or a defined agent-authority boundary.
What Should a Regulated Organisation Validate Before Relying on a RAG System to Answer GxP Questions?
Validate a RAG system by controlling the grounding corpus, retrieval performance, grounding quality, per-answer source/version traceability, and continued reliance after corpus, retrieval or model change. Define the human-review requirement from the intended use and consequence of error: higher-consequence GxP reliance should require explicit review of the cited evidence before action, while any deliberately reduced-review use should be narrowly bounded and separately justified. This is assurance of a retrieval-and-generation pipeline built on a defined corpus, not a claim that the underlying language model alone has been validated.
ReadHow Should Acceptance Criteria and Test Data Be Defined for AI in a GxP Workflow?
Derive acceptance criteria from what the workflow relies on the AI output for, not from the model's capabilities. Settle the intended use first, then choose a measure that follows the direction of harm, which usually means treating the error that reaches a regulated decision undetected as the one the criterion must constrain rather than reporting overall accuracy. Source the threshold from something defensible: the performance of the process being replaced where one exists, the tolerance of the downstream control where a human reviewer or a second check remains in the path, or an explicitly recorded practitioner judgement where neither is available. Then design a test set that is representative of the real input population, large enough in every subgroup you intend to make a claim about, labelled against a stated reference standard, and genuinely independent of the data used to develop, select and tune the model. Set the criteria before testing, and state what happens if they are missed.
ReadWhat Configuration and Access-Governance Record Should Exist Before a Veeva Vault AI Agent Is Approved for GxP Use?
Before a Veeva Vault AI Agent is approved for GxP use, the configuration record should identify the actual Vault objects and boundaries that define the deployed agent: Agent name/class and status; Veeva-provided or custom LLM connection and model where knowable; Agent User and its Security Profile; objective; configured context; each agent action and its instructions; assigned agent tools; chat and API exposure; and the permissions that govern what the agent can read or change. The approval should show that these settings are consistent with the intended use and risk classification, and should define which configuration changes trigger re-approval. This record governs what the agent is configured and authorised to do; it does not replace separate performance validation of its outputs.
ReadHow to Assess and Govern a GxP AI Supplier
Assess a GxP AI supplier against the customer's specific intended use and reliance, not a generic claim that the product is validated or compliant. Establish the service and model boundary, data use, hosting and subcontractors; evaluate relevant quality, security and performance evidence; contract for change notice, incidents, records, auditability and exit; validate the configured end-to-end use; and monitor whether supplier, model, data or workflow changes move the service outside the evidence state.
ReadWhen Can AI-Generated Text Become a Controlled GxP Record?
AI-generated text should remain an identifiable draft until the regulated process supplies the controls that
ReadWhat Evidence Should a GxP AI Supplier Provide?
A GxP AI supplier should provide enough documented evidence for the regulated customer to make its own intended-use and risk decision, not a general assurance that the product is validated. At minimum this means model and training-data documentation, performance evidence under conditions relevant to the customer's use, change-notification commitments, security and logging detail, and contractual provisions for data retention and exit, with the customer retaining accountability throughout.
ReadHow to Classify the GxP Risk of an AI Use Case
Classify a GxP AI use case from the consequence of relying on its output, the authority the system exercises, the opportunity for an informed person to detect error before harm, and the ability to reverse the regulated consequence. Use the resulting tier to determine evidence, oversight, monitoring and approval—not as a legal classification or a substitute for the applicable quality-risk assessment. Reassess the tier whenever intended use, inputs, model, workflow, permissions or permitted reliance changes.
ReadHow to Define the Intended Use of AI in a GxP Workflow
An AI intended-use statement should identify the user, GxP process, authorised inputs, task, output, permitted reliance, human decision point, execution authority, operating conditions and prohibited uses. That boundary determines which failure modes matter, what evidence validation can support and which changes require reassessment.
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