Artificial intelligence (AI) technology will provide new levels of productivity and insight. But when it comes to regulated laboratory environments, such as those that must meet Food and Drug Administration (FDA), European Medicines Agency (EMA) and Good Clinical Practice (GxP) requirements, it is essential that any new capabilities developed using AI must also adhere to the same compliance requirements.
The introduction of AI requires more than simply a sophisticated algorithm; it requires developing a level of confidence in the use of AI. This includes the establishment of validated, auditable, and controlled systems to help grow AI-powered laboratories.
LabVantage Agentic AI platform has been specifically designed to provide laboratories with the governance and validation tools necessary to confidently approach AI-driven innovations, turning them into AI-powered laboratories.
Trust as Validated-State Control
In a regulated lab, “trust” is not a feeling, it is a technical requirement. It hinges on two critical factors:
1. Validated-State Control: The assurance that AI components operate consistently within approved parameters.
2. Traceability: The ability to audit every decision and action taken by the system.
By enforcing strict control over deployment and explicit dependency management, the LabVantage Agentic AI platform ensures that AI improvements can be shipped without compromising the validated state of the laboratory.
The Four Pillars of AI Governance
An effective governance model is proactive, not reactive!
LabVantage Agentic AI platform takes this proactive approach to enhance governance in AI-powered labs to another level by using four key dimensions:
- User Access and Control: By default, AI functionality will be off for all users. Any user or tenant can request to have this functionality turned on, at which time LabVantage will evaluate the associated risk before turning it on.
- Deployment of AI Functionality: LabVantage AI functionality will be grouped into separate, modular pieces. Each of these pieces will be versioned so that LabVantage can avoid using ad-hoc installations, which may introduce instability into a validated environment.
- Data Context and Provenance: LabVantage AI functionality will include metadata to ensure all information is maintained throughout the life cycle of the data. This metadata will allow LabVantage to track “who did what, and why” within the AI.
- Controlled Change Introduction LabVantage AI functionality will be introduced via formal Quality Systems Operational Procedures (SOP). All Staged Manufacturing Release (SMR) upgrades will be validated, thus allowing rapid iteration of the LabVantage AI regardless of validation status.
Technical Controls: “Trust by Design”
It is necessary to establish a secure service by ensuring that robust and effective security methods are used within that service.
The areas of strength of the Agentic platform include Identity Management, Isolation Management and Change Management.
- Multi-Tenant Isolation: The LabVantage Agentic AI platform enforces strict isolation of tenants as well as Role-Based Access Control (RBAC) to ensure that users can only perform actions authorized by their role.
- JSON Web Token Authentication: The use of a robust JWT Authentication mechanism allows accurate validation of users’ actions in compliance with stringent auditing requirements.
- Controlled Custom Code Execution: To provide a mechanism for executing custom AI agents, those agents must successfully pass a review and upload process, including a valid manifest and signature, prior to being executed.
A Granular Validation Strategy
LabVantage Agentic AI platform uses a separate validation method per provider instead of the “black box” method. Each AI component/agent is validated on its own (independently) by being subjected to unit tests, component tests, and integration tests.
Validation Basic Principles:
- Scoped Evidence: Individual agents need to have their own validation artifacts, making it easier to do audit reviews.
- Formal Distribution: Rather than using informal distribution for the validated state, formalized distribution channels will ensure the security of the validated state.
- Compatibility Matrices: Transparent dependency checking on AI components assures their integrity when being installed.
Operationalizing Compliance: The Evidence Pack
To translate technical governance into auditor-ready documentation, LabVantage Agentic AI generates a comprehensive Evidence Pack for every AI agent release. This pack includes:
| Category | Included Documentation |
|---|---|
| Intent & Scope | Impacted workflows and rollout flags. |
| Distribution Records | Traceable package identifiers and dependency metadata. |
| Validation Coverage | Unit, component, and integration test results. |
| Traceability | Versioned baselines and rich user-context metadata. |
Meeting FDA and GxP Challenges:
Pharmaceutical and clinical laboratories face unique hurdles. However, this unique platform is designed to align with FDA guidance on AI/ML Software as a Medical Device (SaMD), providing:
- Explainability & Accountability: Audit trails that satisfy regulatory demands for transparency.
- Continuous Validation: Ongoing monitoring to detect model drift, triggering re-validation to ensure the AI remains compliant to the US FDA CSV/CSA software guidelines for pharma manufacturing with a focus on data integrity (ALCOA+ principles).
The Advantage?
The LabVantage Agentic AI platform will allow you to leverage the transformational capabilities of AI while upholding the values and standards of AI-powered laboratories. It will support your ability to use AI in a compliant manner through strict governance and trust-by-design principles, as well as a robust, auditable and scalable platform that supports today’s increasingly complex, regulated laboratory environment.
Stay tuned to learn more about our Agentic AI journey in the forthcoming series of blogs. Visit https://www.labvantage.com to explore our arena of products.
