Generative AI Integration for Enterprise Workflows

Challenge

• Interest in generative AI capabilities but uncertainty about secure deployment models
• Risk of sensitive data exposure through public AI services
• No framework for evaluating, governing, and monitoring AI model usage

Approach

• Assess use cases and classify by data sensitivity and deployment model requirements
• Deploy private LLM infrastructure or authorized FedRAMP AI services for sensitive workloads
• Implement retrieval-augmented generation (RAG) patterns for domain-specific knowledge
• Establish AI governance policies for acceptable use, monitoring, and responsible AI principles

Typical Outcomes

• Secure, private AI capabilities deployed within authorized boundaries
• Measurable productivity improvements in targeted workflows
• Governance framework enabling responsible AI adoption across the organization

Procurement Paths

• NASA SEWP V and SEWP VI for AI/ML hardware and cloud services
• GSA MAS for AI engineering and advisory services
• CIO-CS (NITAAC) for enterprise AI platforms

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Partner Technology Examples

• NVIDIA
• Microsoft Azure (OpenAI Service)
• AWS GovCloud (Bedrock)
• Elastic