2. Are We Ready for AI Orchestration at Scale?
Inventory your container estate (Kubernetes/OpenShift), continuous integration and continuous delivery, and observability. Validate multitenant isolation, GPU scheduling, secrets management and software bill of materials/patch workflows. Align with agency DevSecOps baselines, Trusted Internet Connections 3.0 and zero-trust plans. If maturity is low, start with managed orchestration while you harden pipelines and standardize images.
3. Can Our Facilities Support AI Workloads?
Confirm power density, cooling and floor space for GPUs; review UPS and generator capacity. Assess network throughput to mission systems and cloud exchanges. Check physical/SCIF requirements, supply chain lead times and maintenance windows. Where constraints exist, prioritize colocation or provider GPU capacity while modernizing core data center infrastructure.
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4. What Governance Should We Apply to AI Workloads?
Adopt the National Institute of Standards and Technology’s AI Risk Management Framework with agency policy for model risk scoring, human oversight and privacy. Integrate approvals into ATO packages; require lineage, data set provenance and model cards. Monitor drift and bias, log prompts/outputs appropriately and set rollback paths. Enforce acquisition clauses for intellectual property, security and incident response.
5. How Do We Scale AI Without Overbuilding?
Pilot with a small, high-value mission use case. Rightsize GPUs/CPUs from real use, not peak estimates. Establish chargeback and total cost of ownership tracking for training vs. inference. Expand iteratively across bureaus, reusing patterns and pipelines. Sunset underused resources and continually re-evaluate building versus buying as offerings mature.
