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CASE STUDY

GenAI Platform in Restricted Enclave

Deploying production GenAI capabilities in a federal enclave with no public egress while maintaining strict RMF and STIG compliance.

Situation

A federal agency needed to deploy generative AI capabilities within a highly restricted enclave environment with no public internet egress. Previous attempts using monolithic platforms had stalled in approval processes due to complexity and attack surface concerns. The agency required RMF authorization and STIG hardening for every component, with traditional approval cycles taking 12-18 months.

Solution

A "thin production slice" approach focusing on a minimal viable stack with self-hosted LLM infrastructure, RAG platform, lightweight guardrails, and automated compliance documentation. Used well-known, pre-vetted components and signed container releases with SBOM. Built compliance checking into development workflows and leveraged existing ATO packages where possible.

OUTCOMES

60% reduction
in authorization time (18 to 6 months)
Zero incidents
in first 6 months of operation
4.8/5.0
user satisfaction rating
40% lower cost
vs commercial alternatives
50+ users
actively using the platform
Rapid iteration
updates within compliance bounds

Challenges

Infrastructure

  • No public egress from enclave
  • Zero external connectivity
  • Cannot access cloud-based LLMs
  • Limited network bandwidth

Compliance

  • RMF authorization required
  • STIG hardening for every component
  • 12-18 month approval cycles
  • Complex audit requirements

Security

  • Attack surface concerns
  • Defense-in-depth requirements
  • Comprehensive audit logging
  • Content filtering and safety controls

Resources

  • Small team size
  • Competing priorities
  • Limited budget
  • Need for rapid iteration

Solutions

01

Minimal Viable Stack

We designed a focused, thin production slice that minimized complexity while delivering core GenAI capabilities.

Core components:

By keeping the stack minimal, we reduced the security review burden and accelerated approvals.

  • Self-hosted LLM infrastructure optimized for enclave deployment
  • RAG (Retrieval-Augmented Generation) platform using approved vector databases
  • Lightweight guardrails framework for content filtering and safety
  • Automated compliance documentation generation
  • Self-hosted LLM
  • RAG platform
  • Guardrails framework
  • Compliance automation
02

Security-First Architecture

Security and compliance were embedded into every layer of the architecture.

Security measures:

This security-first approach provided auditors with measurable evidence and reduced approval friction.

  • Signed container releases with SBOM (Software Bill of Materials)
  • Defense-in-depth with multiple guardrail layers
  • Comprehensive audit logging aligned with RMF requirements
  • Automated STIG compliance checking in CI/CD pipeline
  • RMF authorization
  • STIG hardening
  • Defense-in-depth
  • Audit logging
03

Recognizable Components

We prioritized well-known, pre-vetted open-source components to accelerate security reviews.

Strategy:

This approach allowed security teams to focus on integration points rather than component-level reviews.

  • Used components with existing ATO packages where possible
  • Minimized custom code to reduce security review burden
  • Leveraged proven architectural patterns
  • Provided clear lineage for all dependencies
04

Phased Implementation

The implementation proceeded in focused 4-week phases to maintain momentum and demonstrate progress.

Implementation timeline:

The phased approach allowed for continuous stakeholder feedback and early risk identification.

  • Phase 1 (Weeks 1-4): Infrastructure setup and enclave integration
  • Phase 2 (Weeks 5-8): RAG platform deployment with guardrails
  • Phase 3 (Weeks 9-12): Security hardening and compliance documentation
  • Phase 4 (Weeks 13-16): User testing and authorization package submission