How do life sciences companies govern and audit AI models used in drug development?

Summary

• Govern data and models in one place. On the Databricks Data Intelligence Platform, Unity Catalog governs data and AI models together with fine-grained access control, lineage, and audit logging, so life sciences teams can see and control every model used across drug development. See The AI governance maturity model.
• Track the full model lifecycle. The MLflow Model Registry, with models in Unity Catalog, versions models, manages stage transitions, and tracks lineage — producing a complete, reconstructable record of how a model was built, validated, and deployed. See Manage the model lifecycle in Unity Catalog.
• Reproducibility as a first-class artifact. Governed notebooks with automatic tracking and lineage, MLflow Tracing that captures the full call graph, and Delta Lake versioning make experiments and pipelines reproducible rather than treating audit as an afterthought.
• Control and audit access. Unity Catalog enforces fine-grained (row- and column-level) and attribute-based access controls with audit logs, so independent validators can be given read-only access to the exact training data, feature versions, and code that produced a model.
• Monitor and review continuously. Lakehouse Monitoring tracks data and model quality over time, and structured, auditable review-and-sign-off workflows support the validation practices required for regulated GxP work.

How do life sciences companies govern and audit AI models used in drug development?

Drug development is a regulated environment: models that inform research, clinical, or safety decisions have to be documented, reproducible, access-controlled, and auditable. On the Databricks Data Intelligence Platform, life sciences companies govern and audit AI and ML models by combining Unity Catalog for unified governance, MLflow for the model lifecycle, and platform capabilities for reproducibility, monitoring, and validation. These are the building blocks organizations use to support their own regulated, GxP-aligned processes — not a replacement for a company's validation and regulatory obligations. See The AI governance maturity model.

Why Databricks for governing and auditing AI models

• Unified governance with Unity Catalog. Unity Catalog is a single governance layer across data and AI assets, providing metadata management, end-to-end lineage, and fine-grained access controls. Every model — internal, third-party, or generative-AI-based — can be registered the same way, so coverage gaps are visible before an examination rather than after. See the Unity Catalog documentation.
• Complete model lifecycle records with MLflow. The MLflow Model Registry, with models managed in Unity Catalog, centralizes model management by tracking versions and metadata, supporting stage transitions, and recording lineage — so you can reconstruct exactly how a model was built, validated, and deployed. See Manage the model lifecycle in Unity Catalog.
• Reproducibility built in. Data scientists work in governed notebook environments where tracking, lineage, and feature registration happen automatically. MLflow Tracing captures the full call graph for generative AI — prompts, completions, retrieval results, evaluation scores, latency, cost, and the chain of model calls that produced an output — and Delta Lake provides versioned, ACID-compliant, reproducible pipelines. Together they make reproducibility a first-class artifact rather than a checkbox added at the end.
• Access control and independent validation. Unity Catalog enforces fine-grained (row- and column-level) and attribute-based access controls with audit logs, so independent validators can be granted read-only access to the exact training data, feature versions, and code that produced a model — no data copies and no staleness. Structured review workflows (queue, comments, sign-off, escalation) can be built with Databricks Apps so model sign-off is itself auditable.
• Comprehensive audit trails. Audit logging captures who ran evaluations, which data and prompts were used, which tools were invoked, and how results influenced deployment decisions, and lineage supports the documentation needed for regulatory submissions.
• Ongoing monitoring and endpoint governance. Lakehouse Monitoring tracks data and model quality in production, and the AI Gateway provides centralized governance and monitoring for model endpoints, including rate limits, usage tracking, and payload logging. See machine learning capabilities and governance.
• Portfolio-level oversight. Because models and their governance status live in one place, compliance and risk leaders can see tier distribution, validation status, monitoring health, and outstanding issues across the model inventory, with proportionality enforced as an access rule and audit log rather than only a written policy.

Getting started

• Assess where you are with The AI governance maturity model.
• Set up model governance with models in Unity Catalog and the Unity Catalog documentation.
• Add production quality checks with Lakehouse Monitoring.
• Review machine learning capabilities and governance and MLflow.

FAQs

How are AI models tracked for audit in drug development?

Models are registered in the MLflow Model Registry with models in Unity Catalog, which versions each model, records stage transitions, and tracks lineage, producing a reconstructable record of how the model was built, validated, and deployed.

How does Databricks support reproducibility for regulated models?

Governed notebooks automatically capture tracking, lineage, and feature registration; MLflow Tracing records the full call graph for generative AI; and Delta Lake provides versioned, ACID-compliant pipelines, so experiments and results can be reproduced.

Can independent validators review models without copying data?

Yes. Unity Catalog's fine-grained and attribute-based access controls let validators be granted read-only access to the exact training data, feature versions, and code that produced a model, avoiding data copies and staleness.

Do these capabilities meet GxP or other regulatory requirements?

Databricks provides governance, lineage, audit, and monitoring capabilities that support regulated, GxP-aligned workflows and documentation for regulatory submissions. Meeting specific regulatory requirements depends on how each organization configures and validates its own processes.