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Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy hardware-rooted, air-gapped AI systems that protect classified data and model integrity on potentially compromised infrastructure.
In classified networks, traditional perimeter security fails. Adversaries with privileged access can exfiltrate model weights, training data, and sensitive inferences. Our TEE-based AI development ensures data and algorithms are cryptographically sealed within hardware enclaves like
Intel SGXandAMD SEV, even if the host OS is breached.
Trusted Execution Environments (TEEs). Code and data integrity is verified via remote attestation before any computation begins.We architect end-to-end confidential AI pipelines for defense applications, from secure data ingestion to air-gapped inference. This enables:
Move beyond vulnerable software stacks. Our expertise in confidential computing for AI workloads delivers provably secure systems that meet the strictest defense and intelligence mandates. Explore our broader approach to securing sensitive data in use or learn about secure multi-party AI computation services for collaborative analysis.
Deploy hardware-rooted AI systems that process classified data with guaranteed integrity, preventing exfiltration even on compromised infrastructure. Our TEE-based solutions deliver mission-critical reliability and compliance.
AI models and sensitive data are cryptographically sealed within Intel SGX or AMD SEV enclaves. This ensures model integrity and prevents tampering or data exfiltration, even if the host operating system or hypervisor is compromised. Critical for processing Top Secret/SCI materials.
Deploy isolated, attestable AI inference endpoints within secure government networks (e.g., JWICS, SIPRNet). Models operate in memory-encrypted enclaves with no persistent external network access, meeting the strictest air-gap requirements for classified processing.
Enable joint analysis across agencies without sharing raw data. Our secure multi-party computation services, built on TEEs, allow models to be trained and infer on combined datasets from CIA, DIA, and NSA sources while preserving each agency's data sovereignty.
Architectures designed to comply with Intelligence Community Directive (ICD) 503, CNSSI 1253, and the Zero Trust Reference Architecture. We implement policy-as-code and continuous monitoring to maintain accreditation for systems processing classified data.
Deploy lightweight TEEs on tactical edge devices for local inference on sensor data (RFML, video). Enables AI-powered decision support in disconnected, intermittent, and limited (DIL) environments without risking data spillage via satellite backhaul.
Our structured approach to deploying TEE-based AI systems ensures security validation and operational readiness at each stage, minimizing risk for sensitive defense and intelligence applications.
| Deployment Phase | Core Objectives | Key Deliverables | Timeline | Security Validation |
|---|---|---|---|---|
Phase 1: Architecture & Attestation | Define secure data flow, select TEE hardware (Intel SGX/AMD SEV), establish remote attestation chain. | Threat model, attested environment design, cryptographic key management plan. | 2-3 weeks | Initial attestation protocol validation against MITRE ATLAS. |
Phase 2: Enclave Prototyping | Develop minimal viable enclave for core AI inference, integrate with secure boot and measured launch. | Functional prototype, encrypted model loading pipeline, performance baseline. | 3-4 weeks | Memory integrity verification, side-channel resistance assessment. |
Phase 3: Pipeline Integration | Integrate enclave into existing classified data pipeline (air-gapped networks), implement secure I/O. | End-to-end encrypted data pipeline, integration test suite, operational runbook. | 4-6 weeks | Full data-in-use protection audit, penetration testing on I/O channels. |
Phase 4: Staged Rollout & Monitoring | Deploy to non-critical subsystem, monitor for stability and performance under load, gather operational telemetry. | Deployment to staging environment, performance & security dashboard, incident response playbook. | 2-3 weeks | Continuous attestation monitoring, anomaly detection for exfiltration attempts. |
Phase 5: Full Operational Capability (FOC) | Certify system for production use on classified networks, transition to ongoing support and maintenance. | Final accreditation documentation, SLA agreement, handover to operational team. | 1-2 weeks | Final security accreditation (e.g., FedRAMP High equivalency), compliance sign-off. |
Ongoing: Security Posture Management | Continuous monitoring, attestation, and updates to address novel threats and maintain air-gap integrity. | Monthly security reports, vulnerability patches, attestation log review. | Continuous | Integration with enterprise AI-SPM and Shadow AI Detection platforms. |
Deploy AI systems that process classified data with guaranteed integrity. Our TEE-based solutions prevent data exfiltration and model tampering, even on compromised infrastructure, meeting the strictest defense and intelligence requirements.
Deploy and run sensitive AI models in hardware-isolated enclaves (Intel SGX, AMD SEV) with no external network connectivity. Ensures model weights and inference data are cryptographically sealed from the host OS, hypervisor, and cloud provider personnel.
Learn more about our approach to Confidential AI Inference Enclave Development.
Enable joint analysis across different intelligence agencies using secure multi-party computation within TEEs. Agencies can contribute encrypted data for combined AI analysis without exposing raw, classified sources to each other, breaking down data silos securely.
This architecture is powered by our Secure Multi-Party AI Computation Services.
Implement continuous remote attestation to cryptographically verify that your AI model is executing unaltered within a genuine TEE. Any attempt to modify the model, runtime, or underlying platform is immediately detected, preventing supply chain attacks and insider threats.
Process satellite imagery, signals intelligence (SIGINT), and other sensitive geospatial data within secure enclaves. AI models for object detection and pattern analysis run on encrypted data, ensuring raw intelligence never persists in plaintext in memory or storage.
Explore our capabilities in Geospatial AI and Spatial Analytics (GeoAI).
Deploy lightweight TEEs on tactical edge devices for real-time sensor (video, RF, biometric) analysis. Perform AI inference locally without transmitting raw data, enabling immediate decision-making in disconnected, intermittent, or low-bandwidth (DIL) environments while preserving operational security.
Architect systems that meet specific government directives (e.g., JADC2, IC directives) and regulatory frameworks for AI in national security. Our TEE integration provides the technical controls for data-in-use protection mandated by evolving defense cybersecurity policies.
Deploy air-gapped, hardware-rooted AI systems for classified data processing, ensuring model integrity and preventing data exfiltration.
Our methodology builds AI systems where sensitive data and model weights never leave the secure memory enclave. We architect solutions using hardware-based Trusted Execution Environments (TEEs) like Intel SGX and AMD SEV to create an immutable, hardware-rooted chain of trust, even on potentially compromised infrastructure.
This transforms your secure network from a passive container into an active, intelligent asset capable of processing classified data with zero trust in the underlying host OS or cloud provider.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear, specific answers about deploying hardware-secured AI systems for classified environments. Based on our experience delivering air-gapped, hardware-rooted solutions for secure government networks.
Standard deployments for a hardened, air-gapped AI system take 4-6 weeks from architecture sign-off to operational readiness. This includes hardware provisioning, secure OS configuration, model integration into enclaves (e.g., Intel SGX, AMD SEV), and attestation pipeline setup. Complex multi-party computation or cross-domain solutions can extend to 8-12 weeks. We provide a fixed-scope project plan with weekly milestones.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
