Cyber-Physical AI Security
Your plant just hired a thousand decision-makers. None of them went through security review.
Robots, AGVs, vision systems, and AI copilots are now making operational decisions on the factory floor — with physical consequences when they're wrong or manipulated. Traditional OT security wasn't built for them. Neither was traditional AI security. We work at the seam.
Where Autonomy Meets Physical Consequence
WHO WE ARE
"Where autonomous decision-making meets physical consequence."
Manufacturers, utilities, and logistics operators are pushing AI into places it has never been: robotic cells, autonomous material handling, vision-based quality inspection, predictive control, and operator copilots wired into historians and control networks.
That shift introduces failure modes neither discipline was built for. IT security tools don't see a poisoned inspection model. OT security controls don't govern an agent that arrived in a firmware update and now holds credentials to five downstream systems. AI security practice largely assumes the worst outcome is a bad answer, not a moving arm.
We spent years building the systems that create this exposure — multi-agent orchestration, on-device inference, sensor fusion, robotics. We now secure them. That is an uncommon combination, and it is the entire reason we're useful.
From Model Integrity to Plant Floor
OUR SERVICES
AI & Autonomy Asset Discovery
Most operators cannot answer a simple question: what models and agents are running in our environment right now? Some arrived through procurement. Many arrived through a vendor firmware update.
Key Services:
- • Inventory of models, agents, and embedded AI across OT and edge
- • Identification of unmanaged AI shipped inside vendor equipment
- • Data-flow mapping: what each system reads, decides, and actuates
- • Baseline documentation an auditor or OEM customer will accept
AI-in-OT Readiness Assessment
A structured gap analysis against the December 2025 CISA/NSA/FBI joint principles for AI in operational technology, plus IEC 62443 alignment where autonomous systems cross zone boundaries.
Key Services:
- • Gap analysis against federal AI-in-OT guidance
- • Governance structure across OT, IT, legal, and procurement
- • Vendor transparency and disable-ability review for AI-enabled equipment
- • Prioritized remediation roadmap with cost and sequencing
Adversarial Testing: Perception & Control
If a camera decides pass/fail, or a model decides where a vehicle goes, that model is an attack surface. We test it the way an adversary would.
Key Services:
- • Sensor spoofing and adversarial input testing against vision systems
- • Attacks on AGV/AMR navigation and localization
- • Prompt injection against operator-facing copilots and assistants
- • Model drift and degradation monitoring design
Agent Containment Architecture
Federal guidance is converging on one assumption: agentic systems will behave unexpectedly. Design for containment and reversibility before efficiency.
Key Services:
- • Distinct machine identity and credential scoping per agent
- • Least-privilege reachability enforced at the network layer
- • Human-in-the-loop approval gates on consequential actions
- • Reversibility, kill-switch, and safe-state design
Secure Edge Deployment & Model Supply Chain
On-device inference removes the cloud from the loop — and removes most of your existing security controls with it.
Key Services:
- • Model provenance, signing, and integrity verification
- • Secure OTA update and rollback for edge fleets
- • Hardened Jetson/ARM/Coral deployment patterns
- • Isolation between inference and control paths
Fractional AI Security Leadership
Ongoing ownership for organizations that need the expertise but cannot hire it. Retained monthly.
Key Services:
- • Security review of AI-enabled OT procurement
- • Incident response planning for AI-caused physical events
- • Regulatory readiness, including incident-reporting obligations
- • Operator training and tabletop exercises
We Built This Layer Before We Secured It
ENGINEERING FOUNDATIONS
Our security practice comes out of production work, not a framework binder. We have shipped multi-agent orchestration across cloud and edge, optimized models for Jetson and ARM under real latency budgets, deployed vision and sensor-fusion systems on plant floors and in yards, and managed OTA updates across device fleets.
That work continues for select clients. It also means that when we tell you an agent boundary is wrong or an inference path is exposed, we've been on the other side of that decision.
Multi-agent workflow design, model routing, and human-in-the-loop guardrails
Edge model optimization (TensorRT/ONNX) and fleet management
Vision, OCR, tracking, and anomaly detection in industrial settings
Edge-to-cloud pipelines, observability, and drift analytics