Life Sciences and Biotech GenAI Modernization and Regulated Data Assess
Compass UOL · Intelligence & Research
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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.
Show the rest of the publisher’s description (35 more lines)
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
Highlights
Highlighted by the publisher on AWS Marketplace.
Enables compliant, GenAI deployment on regulated data, Ensures traceability and control, Reduces compliance risk, Defines AWS architecture for regulated workloads,
Agent build and provenance
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