Life Sciences and Biotech GenAI Modernization and Regulated Data Assess

Life sciences organizations are under increasing pressure to adopt generative AI while ensuring strict compliance with regulatory frameworks governing clinical, research, and patient data. Uncontrolled GenAI usage across regulated datasets introduces risks related to data privacy, traceability, auditability, and regulatory non-compliance.

Compass UOL helps life sciences companies design and validate secure GenAI architectures on AWS that are compliant by design. This engagement assesses current data environments, governance models, and AI usage patterns, identifying risks and defining a controlled approach to deploying GenAI over regulated datasets. Leveraging AWS services such as Amazon Bedrock, data governance frameworks, and secure data pipelines, this assessment provides a clear roadmap for compliant GenAI adoption—ensuring traceability, access control, and regulatory alignment across clinical and research workflows.

Customers leave with a validated AWS architecture, governance model, and prioritized plan to safely deploy GenAI workloads while reducing compliance exposure and enabling scalable AI innovation.

Buyer Problem Need to enable GenAI over clinical or research data without violating compliance requirements (e.g., GxP, HIPAA-like constraints) Lack of data governance and traceability for AI-generated content and model usage Rising risk from uncontrolled GenAI experimentation across regulated datasets Pressure to modernize data platforms for AI readiness on AWS

Delivery Model Discovery and regulatory context alignment Data and GenAI usage assessment across regulated environments AWS architecture design and governance framework definition Roadmap and prioritized implementation plan

Assessment / Engagement Scope Review of regulated data environments (clinical, R&D, real-world data) GenAI use case mapping and risk classification Data governance, access control, and auditability assessment AWS-native GenAI architecture definition (Bedrock, data services, security layers) Compliance alignment review (data residency, encryption, lineage, monitoring) Gap analysis between current state and target compliant AI architecture

Expected Output / Deliverables GenAI readiness and compliance assessment report Target AWS architecture for regulated GenAI workloads Data governance and control framework (access, audit, lineage) Risk map with prioritized remediation actions Implementation roadmap aligned to AWS services

Customer Decision Questions This offer helps the customer answer: How can we safely use GenAI with regulated clinical or research data? What controls are required to ensure auditability and compliance of AI workloads? Which AWS architecture supports compliant GenAI deployment at scale?

Highlights

Enables compliant GenAI deployment on regulated data Ensures traceability, auditability, and governance Reduces risk of regulatory exposure Defines production-ready AWS architectures for life sciences workloads