Security must understand the full path from prompt to actuator.
AI decisions are becoming physical actions. Physics Defender maps and protects every stage between an AI's reasoning and a machine's motion — overlaying risk nodes with a continuous runtime security layer.
AI security changes when software starts moving machines.
LLMs and AI agents are no longer limited to generating text. They are beginning to plan, call tools, interpret sensors, control workflows and influence robotic systems. In physical environments, a model failure, prompt injection or unsafe plan can become a machine action.
Physics Defender exists for the new risk surface where artificial intelligence, robotics, safety engineering and cybersecurity converge.
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Cyber-Physical AI Security
AI Security + Robotics Safety + OT Security + Runtime Governance
Physics Defender helps teams understand and control the full pathway from AI reasoning to physical actuation.
The Physical AI risk surface is bigger than the model.
From AI red teaming to cyber-physical assurance.
A structured methodology for identifying, testing, constraining and evidencing Physical AI risks.
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Security architecture for the AI-to-actuator pathway.
Physics Defender maps and protects the complete stack where AI decisions become machine actions.
From AI Risk to Robotics Safety Case.
Physics Defender helps teams translate cyber-physical AI risks into hazard analyses, safety requirements, mitigations and evidence packages for future certification and regulatory review.
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The robot operates within defined safety boundaries.
Hazards have been identified, mitigations implemented and runtime controls verified.
Simulation tests, red team results, logs, requirements traceability, policy checks and monitoring records.
Runtime boundaries for Physical AI systems.
When AI systems can act, safety must be enforced continuously. Physics Defender helps teams define and test the operational boundaries that prevent AI decisions from becoming unsafe physical behavior.
Built for teams that need evidence, not slogans.
Physics Defender supports standards mapping, governance evidence and certification-readiness workflows across AI, robotics, OT and functional safety contexts.
Physics Defender does not replace certification bodies. It helps teams build structured risk evidence, safety-case documentation and technical traceability for future audits, regulatory review and certification-readiness.
Built for the systems where AI meets the physical world.
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Not just AI security. Not just robotics safety.
A platform for cyber-physical AI assurance.
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Research for the age of Physical AI.
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Read Brief →Cyber-Physical AI Assurance Platform
A structured platform for mapping, testing, constraining and evidencing the risk surface of Physical AI systems.
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The AI-to-actuator stack.
What you walk away with.
Threat Modeling for Physical AI
Understand how adversarial instructions, compromised inputs and unsafe plans can propagate into physical action.
Risk categories across the path.
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Safety Case Support for AI-Controlled Robotics
Translate AI and robotics risks into claims, arguments and evidence that engineering, legal, policy and safety teams can review.
The autonomous robot operates within defined safety boundaries in its intended environment.
Hazards have been identified, risks assessed, mitigations implemented and runtime constraints verified.
Hazard analysis, FMEA, simulation results, runtime logs, red team findings, requirements traceability and monitoring records.
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ROS2 Security Review for Robotics Platforms
Map and review the communication graph, middleware exposure and security posture of ROS2-based robotic systems.
Nodes publish and subscribe across topics and services over DDS. Each edge is a pathway that may need authentication, access control and integrity verification.
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Physical AI is becoming infrastructure.
Physics Defender supports teams building and deploying AI systems that sense, decide and act in real environments.
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Research for Cyber-Physical AI Security
Technical briefs, threat models and safety engineering notes for the age of AI-controlled machines.
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Read Brief →Securing the boundary between intelligence and motion.
Physics Defender was created for the next phase of AI: systems that do not just answer, but act.
To secure and evidence the safety boundary between AI decisions and physical action — so robotics teams can deploy Physical AI with structured risk analysis, runtime controls and reviewable evidence.
AI agents are beginning to plan, call tools and control machines. The risk surface where intelligence becomes motion is new, growing and under-secured. It needs dedicated engineering.
Security must understand the full path from prompt to actuator. Model evaluation alone is not enough — sensors, middleware, controllers, actuators and operational context all carry risk and all require evidence.
No hype, no false guarantees. We measure success in traceable hazards, verified constraints and reviewable evidence — not slogans. Human oversight remains critical at every layer.
Start with a Physical AI Risk Assessment.
Tell us about your robotics platform, AI agent, drone system, ROS2 stack or autonomous machine.
We focus on serious technical conversations with robotics, AI, security, safety and product teams.