Bounded by design
Limit sources, tools, actions, and user scope to the intended use case.
Trust & governance
Accountability is a system property.
Trust comes from clear boundaries, visible evidence, controlled change, and named human ownership—not a general promise that AI is safe.
Design principles
Make responsibility visible in the workflow.
Every AI Pharma implementation should be able to answer who can use the system, what information it can access, what action it can take, who reviews the result, and what happens when the system is uncertain or fails.
Traceable by default
Connect outputs to evidence, configuration, model, workflow, and review information.
Reviewable by people
Provide enough context for a qualified person to verify, revise, reject, or escalate.
Controlled in operation
Manage access, changes, incidents, retention, and periodic re-evaluation as part of the service.
Data & access
Use the minimum data needed for a defined purpose.
Data controls are agreed before ingestion. We document the source, purpose, permitted users, retention period, deployment location, and deletion or return process for each relevant data class.
Source governance
Use approved corpora, document owners, revision status, licences, and access classifications.
Identity & role context
Carry user permissions through retrieval and actions; do not rely on a generic shared identity.
Minimisation & retention
Collect and retain only what the workflow requires, with a documented lifecycle.
Separation
Keep tenant, environment, development, evaluation, and production boundaries explicit.
Data residency is a design input. A deployment region is only one part of the boundary; service providers, subprocessors, telemetry, support access, and integrations also need to be considered.
Security
Use layered controls appropriate to the system.
Security requirements depend on the data, environment, integration, and operating model. The design should cover identity, network, storage, application, model supply chain, secrets, monitoring, and incident response.
- 01Identity & access
Least privilege, strong authentication, service accounts, access review, and separation of duties. - 02Data protection
Encryption in transit and at rest where appropriate, key ownership, transfer controls, and secure deletion. - 03Secure delivery
Code and dependency review, secrets management, vulnerability handling, and controlled deployment. - 04Detection & response
Relevant logging, alert ownership, incident procedures, and evidence preservation.
AI governance
Manage the system across its full lifecycle.
AI governance is an operating discipline. It connects intended use, risk, evaluation, approval, deployment, monitoring, change control, incidents, and retirement.
Intended useusers, decisions, exclusions, and claims
Risk tierdata sensitivity and potential impact
Evidenceevaluation and acceptance record
Configuration control
Version prompts, models, retrieval indexes, tools, policies, and interface changes as a connected release.
Decision authority
Name the business owner, technical owner, reviewer, and escalation path for the workflow.
Change evaluation
Define which changes require focused regression testing, full re-evaluation, or formal approval.
Retirement
Plan how access, data, integrations, retained records, and open actions are handled when a system ends.
Operations
Keep the control evidence current.
Trust is maintained through recurring work: access review, source verification, monitoring, feedback review, incident exercises, and re-evaluation as the model, workflow, or environment changes.
Technical and workflow health
Failures, latency, source access, anomalous use, escalation, and reviewer outcomes.
People and evidence
Sampled outputs, corrections, missed sources, disagreement, and emerging edge cases.
Controlled change
Convert findings into test cases, documentation updates, training, or workflow changes.
Regulatory context
Controls depend on the intended use and jurisdiction.
“GxP-aware” means the delivery method recognises regulated processes, documentation, review, and change control. It does not mean a generic AI system is automatically compliant or approved for every regulated use.
Requirements may include validated systems, electronic records and signatures, electronic systems and records controls, privacy and security obligations, sector-specific requirements, and customer quality agreements. These should be assessed and documented for the actual system.
Need a due-diligence pack? Contact us with your quality, security, privacy, and intended-use requirements so the appropriate evidence can be scoped.
Make the boundary explicit
Bring your quality, security, and governance requirements.
We will help separate what the platform can support from what requires customer-specific validation, process change, or a qualified system owner.