Physics-Based Control: The key to securing Cyber-Physical Systems against AI threats

As we face the rise of Agent-centric AI—capable of spoofing data protocols, mimicking operator behaviors, and generating "statistically plausible" logs—we need a defense mechanism that lies beyond the reach of statistical inference.

At TranzAI, we begin with the premise that the dynamics of a system—how it evolves over time—are much more difficult to simulate than its state at a given moment.

The synthetic AI threat

We are entering an era where "seeing is no longer believing." Agent-centric AI and Generative Adversarial Networks (GANs) can now manufacture synthetic sensor data that passes every statistical quality check. They can mimic the texture of noise, the range of values, and the syntax of protocols.

Agent-centric AI is a master of syntax and semantics. It can speak the language of Modbus, DNP3, and IEC 61850 fluently. The core risk is that linguistic mastery of protocols is not equivalent to operational legitimacy or physical safety. An agent that fluently “speaks” Modbus, DNP3, or IEC 61850 can produce command sequences that are:

• Structurally valid
• Semantically plausible
• Indistinguishable from system-engineered control logic

This enables semantic impersonation, where malicious, erroneous, or injected instructions look operationally correct. Protocol fluency allows prompt injection to cross the IT/OT boundary. Historically, OT systems were protected by protocol complexity and engineering expertise requirements. Agentic AI removes these barriers. A crafted prompt can cause an agent to generate a valid IEC 61850 control sequence that violates operational policy or safety margins.

Why Physics-Based Control is a powerful firewall in the AI era

In the traditional cybersecurity view, "Time" is a timestamp—metadata attached to a packet. But in the world of Cyber-Physical Systems (CPS), "Time" is a dimension of physics. It is the manifestation of inertia, resistance, and causality.

By embedding the laws of physics directly into the control loop via Digital Twins, we treat Temporal Consistency not just as a check, but as a hard constraint on system actuation.

Temporal Consistency

Temporal Consistency is the property by which system states, observations, predictions, and actions evolve in a manner that is coherent with time, causality, and known dynamic constraints. In a digital twin–driven control loop, it means that no state transition, control action, or inferred condition can occur faster, earlier, or differently than what is physically and dynamically plausible for the real system.

In short: what happens must respect when and how it can happen.

Physics-Based Control: the Digital Twin as governor

Consider the following scenario: an attacker intercepts a sensor signal and replaces it with a generated value.

• The Attack: The AI agent sees a pressure reading of 100 psi. It generates a fake stream showing 100 psi, while the real pressure spikes to 500 psi.
• The Weakness: If the controller is purely data-driven (i.e., "If sensor says X, do Y"), it is easily fooled. It has no internal concept of reality; it only knows inputs.

In a Physics-Based Control architecture, the Digital Twin is not a passive dashboard; it is an active Reference Model running in parallel with the physical plant.

This model solves the differential equations of the system in real-time (dx/dt = f(x, u)). It knows that state x cannot simply jump to a new value; it must evolve according to, for example, the system's Hamiltonian or Lagrangian dynamics.

Here, Temporal Consistency becomes the rejection criteria for control inputs.

The TranzAI solution: encoding physics into probabilistic graphs

The TranzAI platform is based on a fundamental principle: physics is a causal graph that unfolds over time.

We don't just monitor streams of numbers; we build Probabilistic Graphical Models (PGMs) where the nodes represent physical components (valves, rotors, circuits) and the edges represent the laws of physics (flow, torque, current) connecting them.

Here is how we turn "Physics" into a "Security Primitive" using Dynamic Bayesian Networks (DBNs):

The Graph as the topology of truth

In a TranzAI Digital Twin, the system is represented as a graph.

• Node A (Voltage) causes Node B (Heat).
• This is not a statistical correlation; it is a structural constraint derived from Joule’s First Law.

If an AI agent injects a spoofed signal into Node B (reporting a temperature spike) without the corresponding causal precursor in Node A (current spike), the graph structure breaks. The conditional independence properties of the graph immediately flag the anomaly. The attack fails because the "fake" data violated the causal topology of the Digital Twin.

Dynamic Bayesian Networks (DBNs) for Temporal Consistency

Static graphs show how things are connected. Dynamic Bayesian Networks show how things evolve.

A DBN models the state of the system at time t and calculates the probability of the state at time t+1, conditioned on the laws of physics:

P(State_t+1 | State_t, Physics)

This allows us to embed differential equations directly into the probabilistic transition logic:

• The Scenario: A sensor reports a sudden halt in a massive conveyor belt.
• The Physics: Momentum and friction dictate that velocity cannot drop to zero instantly without infinite force.
• The DBN Check: The network calculates the probability of the observed transition (v_t -> v_t+1). Because the transition violates Newton’s laws of motion (inertia), the probability is effectively zero.

Even if the attacker’s data looks "normal" for a stopped belt, the temporal transition from moving to stopped was physically impossible. The DBN rejects the future that the AI agent tried to write.

Why is this difficult to fake

Why can’t an agent-centric AI just learn the DBN?

Because DBNs on the TranzAI platform are Hybrid Models. They combine data-driven learning with explicit Expert Knowledge (Physics Laws).

• Data-Driven: Handles the noise, sensor jitter, and environmental uncertainty.
• Physics-Driven: Enforces the hard boundaries of reality.

For an attacker to fool a TranzAI DBN, they would need to know the hidden states of every variable in the graph and simulate the exact causal ripple effect of their spoofed signal across the entire network in real-time. They are no longer fighting a firewall; they are fighting the laws of thermodynamics and causality, encoded in a graph structure.

Conclusion

As AI agents become more capable of generating synthetic reality, our defense systems must be grounded in something they cannot hallucinate.

By deploying Graph-Based Digital Twins and Dynamic Bayesian Networks, TranzAI provides the infrastructure to treat Physics and Time as unforgeable signatures. We don't just detect anomalies; we mathematically verify the physical integrity of the moment.