AI-Enabled Cyber Physical System Security

Deploy unsupervised ML models that analyze operational technology (OT) network telemetry to identify subtle deviations indicative of intrusion or component failure weeks before traditional thresholds are breached, preventing catastrophic system compromise.

Implement cryptographically verified, zero-trust firmware update pipelines for deployed platforms. Updates are signed, validated via hardware TEEs, and rolled back automatically upon integrity check failure, ensuring resilience against supply chain attacks.

Harden AI-driven navigation and control logic against adversarial data poisoning and model evasion attacks using techniques aligned with the MITRE ATLAS framework. Systems maintain core functionality under electronic warfare conditions.

Integrate cyber and physical security alerts into a single AI-powered command dashboard. Correlate IT network intrusions with physical sensor anomalies (e.g., unauthorized access hatches) for accelerated, coordinated incident response.

Automate adherence to defense-specific standards like NIST SP 800-53, NIST AI RMF, and ISO/IEC 27001 for cyber-physical systems. AI continuously audits configurations and generates evidence for accreditation bodies.

Shift from reactive, schedule-based maintenance to AI-predictive upkeep. Extend mean time between failures (MTBF) for critical components by 40% and reduce unplanned downtime, directly lowering lifecycle sustainment costs.

We conduct comprehensive threat modeling using frameworks like MITRE ATT&CK for ICS to map attack surfaces across your AI-driven control systems, OT networks, and physical interfaces. This establishes a security baseline and prioritizes risks.

Our engineers design and implement secure-by-default architectures. We integrate hardware-based Trusted Execution Environments (TEEs), enforce zero-trust principles for OT/IT convergence, and develop secure, cryptographically signed firmware update pipelines.

We deploy unsupervised ML models trained on your operational telemetry to establish behavioral baselines and detect subtle anomalies indicative of intrusion or system manipulation. We also conduct adversarial testing using the MITRE ATLAS framework to harden your AI models against evasion and poisoning.

We establish a continuous security posture with real-time monitoring, automated incident playbooks, and periodic red teaming exercises. Our MLOps pipelines ensure your detection models are retrained on new threat data, maintaining resilience as your platform and the threat landscape evolve.