Service
Secure AI-driven control systems in ships, aircraft, and vehicles against novel cyber-physical attack vectors.
AI integration creates new attack surfaces in operational technology (OT) networks. We harden these systems end-to-end:
CAN bus, MIL-STD-1553, and proprietary control protocols.Move from reactive patching to proactive resilience. Our security frameworks are built on
MITRE ATLASandNIST SP 800-82guidelines for industrial control systems.
We deliver air-gapped testing environments to simulate adversarial attacks without risk. Partner with us to implement:
Trusted Execution Environments (TEEs) for critical AI workloads.Explore our related services for comprehensive defense AI: Secure Federated Learning for Defense and Adversarial AI Defense and Red Teaming.
Our AI-enabled cyber-physical security solutions deliver measurable improvements in platform resilience, operational continuity, and threat response times for military systems.
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.
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.
Choose the right level of partnership for securing your AI-integrated platforms. Each tier includes our core methodology of threat modeling, anomaly detection engineering, and secure update protocols, with scaling support and customization.
| Security Capability | Tactical | Strategic | Sovereign |
|---|---|---|---|
OT Network Anomaly Detection | |||
AI Control System Resilience Testing | Basic | Advanced | Full Adversarial Red Teaming |
Secure Firmware Over-the-Air (FOTA) Update Pipeline | |||
Hardware Root-of-Trust Integration | Optional | ||
Real-time Threat Intelligence Feeds | |||
On-Site Security Assessment & Integration | 1 Week | Custom Duration | |
Dedicated Security Engineering Team | Email Support | Named Engineer | Embedded Team |
Uptime & Response SLA | 99.5% | 99.9% | |
Compliance Documentation (NIST, ISO 27001) | |||
Starting Project Scope | Single System Assessment | Fleet-Wide Deployment | Program of Record Integration |
We deliver end-to-end security for AI-integrated military platforms through a structured, four-phase methodology designed to identify vulnerabilities, harden systems, and ensure continuous operational resilience against evolving cyber-physical threats.
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.
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.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Common questions from CTOs and engineering leads on securing AI-integrated military platforms against sophisticated cyber-physical attacks.
We follow a structured, four-phase engagement model tailored for defense applications. Phase 1 is a threat modeling and architecture review (1-2 weeks) where we map your OT network, AI control points, and potential attack vectors using frameworks like MITRE ATT&CK for ICS. Phase 2 involves implementing real-time anomaly detection for operational technology networks, typically deploying hardened sensors within 2-3 weeks. Phase 3 focuses on resilience engineering for AI-driven control systems, including adversarial testing and fail-safe protocol integration. Phase 4 is continuous monitoring and support, with 24/7 SLAs available. All work is conducted in accredited facilities or via secure, air-gapped development environments.
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.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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