Secure AI Model Obfuscation and Protection

Deploy AI models within hardware-enforced secure enclaves (e.g., Intel SGX, AMD SEV). Even with physical device access, adversaries cannot extract model weights or architecture, preventing replication of critical intelligence or targeting algorithms.

Key Differentiator: Unlike software-only encryption, hardware root-of-trust provides tamper-evident protection.

Our implementations are designed to meet and can be validated against stringent standards like Common Criteria and FIPS 140-3 for cryptographic modules. We architect solutions for air-gapped and classified networks, ensuring processing occurs only within accredited boundaries.

Credibility Signal: Solutions are engineered for FedRAMP Moderate/High and IL5/6 equivalency.

Integrate runtime attestation and anomaly detection within the TEE to identify and mitigate data poisoning, evasion attacks, and adversarial examples designed to manipulate model outputs in the field. This maintains operational accuracy in contested environments.

Outcome: Models resist manipulation attempts that could lead to incorrect intelligence or failed missions.

Orchestrate cryptographically signed, over-the-air updates for models deployed on thousands of edge devices. Each update is verified by the hardware root-of-trust before installation, preventing supply chain attacks and ensuring only authorized code runs.

Client Value: Maintain fleet-wide model currency and patch vulnerabilities without recalling hardware.

Generate immutable, hardware-attested logs of all model inference activity. This creates a verifiable chain of custody for intelligence products, proving data was processed within sovereign boundaries and meeting EU AI Act and national data localization mandates.

Related Service: Learn more about our Sovereign AI Infrastructure Development for air-gapped solutions.

We begin with a comprehensive threat assessment based on frameworks like MITRE ATLAS, identifying specific attack vectors for your model and deployment environment. This adversarial perspective ensures our obfuscation strategy targets the most critical vulnerabilities first.

We deploy your model within hardware-secured enclaves (e.g., Intel SGX, AMD SEV) or on certified secure elements. This isolates the model and its data in memory, preventing extraction even with root access to the host system—a critical control for deployed edge devices.

Our engineers apply a suite of proprietary techniques including model encryption, parameter entanglement, and control flow flattening. This renders the model binary indecipherable to static and dynamic analysis tools, protecting your core intellectual property.

We embed cryptographically verifiable watermarks and integrity checks within the model. This allows for definitive attribution if a model is stolen and detects any tampering or adversarial fine-tuning attempts, providing a forensic trail.

We establish a complete, accredited MLOps pipeline within your secure facility or air-gapped cloud. This covers secure model training, the obfuscation process itself, and final deployment, ensuring end-to-end control and verifiable model lineage. Learn more about our Secure AI Model Training and Fine-Tuning services.

Our security does not end at deployment. We conduct continuous red teaming and adversarial testing using the same techniques as nation-state actors. We simulate capture scenarios and attempt model extraction to validate and iteratively strengthen defenses. Explore our AI Red Teaming and Adversarial Defense capabilities.