Data Ready Architectures: Operationalizing AI, HPC, & Trusted Science at Scale

The Genesis Mission reframes data, computing<, and AI as national infrastructure for science, energy, and security. For DOE, NNSA, and the National Laboratories, the challenge is no longer experimentation, it is operationalization: how to evolve federated, security-constrained research environments into AI-ready, auditable, and continuously governed platforms without disrupting mission science.

The discussion between government and industry moves beyond cloud migration narratives to focus on data-centric security, provenance-by-default pipelines, HPC-AI convergence, and lifecycle governance across classified and unclassified domains.

Learning Objectives:

• Identify architectural patterns for data and AI platforms
• Review governance models that scale across federated laboratories
• Evaluate practical strategies for technical debt extraction, not just modernization
• Examine AI/ML enablement approaches grounded in data provenance and trust
• Delineate security models that enable multi-lab collaboration by design
• Define metrics that measure scientific velocity, reuse, and mission impact