AI for Biometric and Identity Verification

Real-time face, gait, and iris recognition from live video feeds, enabling positive identification of persons of interest in dynamic, non-cooperative scenarios without requiring subjects to stop or look at a camera.

Multi-modal liveness detection that analyzes texture, reflectance, and 3D structure to defeat presentation attacks using high-resolution photos, videos, masks, or synthetic media, ensuring verification integrity.

Full-stack deployment within accredited, air-gapped networks or secure enclaves. Data processing and model inference remain entirely within sovereign boundaries, with no external API calls or data exfiltration risk.

Continuous, automated watchlist matching across distributed sensor networks (CCTV, bodycams, drones). Correlates identities over time and location to establish patterns of life and alert on reappearances.

Hardened biometric checkpoints for secure facility access. Integrates with existing PACS and credential systems, providing a definitive second factor of authentication beyond cards or pins.

Models are hardened against evasion attacks (adversarial patches, makeup, accessories) through rigorous red teaming using frameworks like MITRE ATLAS. Ensures reliable performance against sophisticated deception attempts.

Every model and pipeline is developed under a certified Secure Development Lifecycle, integrating threat modeling, static/dynamic code analysis, and peer review from initial architecture through to deployment. This proactive approach eliminates vulnerabilities before they reach production.

We train biometric models (face, gait, iris) on diverse, adversarial datasets and rigorously test against the latest spoofing techniques, including 3D masks and deepfakes. Our red teaming protocols, aligned with frameworks like MITRE ATLAS, ensure models are resilient to evasion and data poisoning attacks.

Sensitive biometric data is processed within hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV. This ensures data remains encrypted in memory during inference, providing a secure enclave that protects against host-level attacks and insider threats.

For the highest classification environments, we deploy fully air-gapped solutions or sovereign AI infrastructure confined within specific geopolitical boundaries. This ensures complete data sovereignty, compliance with mandates like the EU AI Act, and isolation from external networks.

Post-deployment, we implement continuous monitoring for model drift, performance degradation, and adversarial activity. Our systems detect anomalies in inference patterns and can trigger automated countermeasures or secure retraining pipelines to maintain operational integrity.

Our secure MLOps pipeline provides full model lineage, from training data and code commits to deployment artifacts and inference logs. Every change is cryptographically signed and auditable, meeting the strict provenance requirements of defense and intelligence agencies.