Best Practice

Physical AI Cybersecurity

Protecting Physical AI systems, from industrial and collaborative robots to humanoids, and from autonomous drones to quadrupeds and rovers, requires a specialist cybersecurity approach capable of correlating physical operational behaviour with the digital communications of autonomous fleets in real time

Protecting automated manufacturing and logistics

As Physical AI systems become ever more widely integrated into enterprise networks, cloud services, robotic middleware such as ROS and industrial collaboration platforms, new attack surfaces are emerging. To address these specific vulnerabilities, Spike Reply has developed a comprehensive approach to Physical AI cybersecurity, focused on monitoring and protecting industrial and collaborative robots, humanoids, autonomous drones, autonomous vehicles, quadrupeds, rovers and other autonomous systems operating in connected manufacturing and logistics environments. By combining vertical threat modelling, tailored cyber threat intelligence, AI-based behavioural analysis and custom-designed network intrusion detection, it is possible to significantly strengthen the security and operational resilience of Physical AI systems.

The threat landscape in the Physical AI domain

The deployment of autonomous systems, including humanoids, quadrupeds, rovers and drones, is accelerating rapidly. The manufacturing sector is now moving well beyond the pilot phase, with various types of robots and autonomous machines being used in factory environments and logistics centres. However, this market expansion is accompanied by a widespread lack of security-by-design principles. Physical AI cybersecurity remains a relatively unexplored field within the wider cybersecurity domain.

Unlike traditional IT systems, cyber attacks targeting Physical AI systems can have direct physical consequences, affecting production processes, plant integrity and operator safety. The market is currently driven by thousands of start-ups that often prioritise functionality and time-to-market over solid security foundations. Furthermore, a compromised autonomous system represents not only an operational risk but also a potential vector for serious data exfiltration. These machines are equipped with advanced sensors and cameras and therefore pose a significant risk to the confidentiality of intellectual property.

Compliance and threat intelligence: a strategic necessity

The need for continuous cybersecurity monitoring in Physical AI is reinforced by evolving regulatory obligations and industrial regulations. In addition, because Physical AI systems are powered by the most advanced AI models, organisations adopting them must also comply with AI and privacy regulatory frameworks. Implementing robust telemetry monitoring and intrusion detection systems directly supports compliance with directives and standards such as the EU Machinery Regulation 2023/1230, the Cyber Resilience Act (CRA) and ISO 10218:2025. To maintain a proactive defensive posture against these complex risks, security mechanisms must also be continuously enriched with Cyber Threat Intelligence (CTI) specialised for industrial and Physical AI environments. This makes it possible to correlate alerts with known threats, indicators of compromise (IoCs) and specific attack patterns, supporting the early identification of cyber attacks capable of altering the operations of autonomous systems.

A defence architecture based on behavioural and network intrusion detection

Protecting these cyber-physical environments requires specialist intrusion detection mechanisms designed specifically for Physical AI systems. Effective Physical AI cybersecurity requires a granular understanding of the physical actions expected of a machine within its specific operational context. To meet this need, Intrusion Detection Systems specialised for Physical AI environments enable real-time telemetry monitoring, protocol parsing of standards such as DDS, and anomaly detection. On this basis, Spike Reply's detection architecture has been designed to operate across multiple layers.

Behavioural IDS:
analyses movement patterns, trajectories, operational states and task sequences to identify deviations from the robot's expected behaviour.
Network IDS:
monitors communications between robots, controllers and connected systems, detecting suspicious network activity and anomalous traffic patterns.
Contextual Verification:
integrates with a Collaboration Platform from which it retrieves the tasks assigned to the robots and the operational context of the production environment. On this basis, the platform verifies in real time that the actions performed remain consistent with the assigned tasks.
Local AI Evaluation:
sensor data and video streams are analysed locally on the robotic infrastructure. This edge computing approach uses lightweight machine learning models to enable fast anomaly detection and object flagging, while ensuring privacy and real-time processing.

Use cases tested in cyber-physical environments

Drawing on the established experience of Spike Reply's experts in cybersecurity, manufacturing and logistics, this Physical AI cybersecurity architecture is currently being tested and progressively adopted in production across several advanced operational scenarios.

Automated Drone Security Patrolling
In the defence sector and in the protection of large private properties, autonomous drone systems are used to monitor extensive perimeters. Solutions integrating technologies such as DJI Dock 2 and DJI Matrice 3TD enable drones to take off automatically when an alarm is triggered, inspect the affected areas using onboard sensors and thermal cameras, and transmit video in real time. Protecting these mobile assets requires the adoption of a zero-trust paradigm capable of preventing backdoors and security deviations. Thanks to Low Earth Orbit (LEO) satellite connectivity, these drones can also maintain secure and reliable surveillance capabilities even in remote or hard-to-reach areas.

Humanoid Integration in Manufacturing
As automotive manufacturers conduct extensive factory trials to evaluate the use of humanoids in complex assembly tasks, rigorous validation of functional safety becomes essential. An onboard Behavioural IDS ensures that, if a humanoid's kinematic instructions are maliciously altered, the local anomaly detection engine can identify the deviation from expected behaviour. This enables anomalies to be detected promptly and countermeasures to be activated to prevent physical damage or operational disruption.
Multi-Robot Fleet Coordination via Open RMF
Industrial plants increasingly rely on heterogeneous robot fleets that use open robotics frameworks, such as Open RMF, to standardise communications. This framework is critical middleware that connects higher-level cloud collaboration platforms to physical robots in the field. Securing this environment means developing specialised integration plugins for these platforms. In this way, the collaboration layer is strengthened against cyber threats, enabling the secure integration of multi-robot fleets.

Spike Reply's approach to Physical AI Cybersecurity

To address these complex vulnerabilities systematically, Spike Reply provides a structured engineering and operations offering designed to support industrial deployments of Physical AI systems. This framework is built on five core pillars.

Strategy Services

threat landscape analysis, security posture assessment, threat modelling, risk assessment and security benchmarking of robotics vendors.

Architecture Design

definition of robot-centric secure-by-design architectures, convergence of cybersecurity and physical security, secure integration of multi-robot fleets and hardening of ROS and ROS 2.

Engineering Solutions

development of custom IDS platforms for robotic integration, building of threat intelligence capabilities dedicated to Physical AI cybersecurity and definition of robotics incident playbooks with SOAR automation.

Security Testing Activities

functional safety validation of robots, security verification of integrations and specialist penetration testing of robotic systems.

Robotics-Security Operations Centre (R-SOC)

enables continuous threat detection across robotic fleets, with 8x5 monitoring, proactive robotics threat hunting and cyber-physical digital forensics.

Spike Reply's experience in protecting Physical AI ecosystems

Spike Reply supports manufacturers and logistics companies in assessing the security of Physical AI system deployments, implementing dedicated intrusion detection architectures and building a secure-by-design approach for autonomous fleets. Spike Reply's approach to Physical AI Cybersecurity makes it possible to protect industrial and collaborative robots, humanoids, autonomous drones, quadrupeds and rovers through specialist expertise that combines industrial cybersecurity, behavioural analysis and network intrusion detection.

Frequently asked questions about Physical AI Cybersecurity

What is the current level of market maturity in adopting fully secure fleets of Physical AI systems?

The market is currently still strongly oriented towards pilot projects. Many organisations are testing these technologies to understand how they integrate with industrial and IT networks. Prototype IDS solutions dedicated to Physical AI systems are already being proposed in these environments. While recognising the strong disruptive potential of these new technologies, organisations still regard this phase as a collaborative journey, in which building a solid security posture proceeds in parallel with the adoption of robots, humanoids, drones and other autonomous systems.

How does Reply's security framework handle an active threat when an anomaly is detected in a fleet of autonomous systems?

What methodologies are used to test and validate the security of new Physical AI systems before they go into production?

The evaluation process takes place in a dedicated prototyping laboratory, Spike Reply's Area42. In this environment, a wide variety of Physical AI systems are studied and tested over extended periods. For each new autonomous system, a rigorous iterative process is followed to build expertise and integration assets. The process begins with a security assessment and the integration of the individual system, in order to collect logs and monitor any anomalous behaviour. It then continues with the activation of security controls, integration with a custom security dashboard, multi-robot IDS integration and, finally, integration with Open RMF.

How can AI accelerate the initial risk assessment and threat modelling phases for Physical AI system deployments?

What organisational structures and specialist teams are needed to build a complete Physical AI cybersecurity solution?

Given that the IDS relies extensively on AI and machine learning for anomaly detection, how are the security and compliance aspects of the AI models addressed?

A team dedicated to platform compliance and MLSecOps was established from the start of the project. It is essential to be able to demonstrate precisely how machine learning models are trained and protected. This proactive approach ensures that the security platform itself remains secure and prepares the solutions for possible future compliance requirements under frameworks such as the AI Act, particularly if specific Physical AI use cases are classified as medium risk.

How does the security architecture manage the complexity of coordinating heterogeneous, multi-brand fleets?

The architecture leverages open robotics frameworks such as Open RMF. This framework acts as comprehensive middleware, providing an SDK that abstracts the cloud collaboration layer from low-level communications with devices. By developing specific security plugins for these platforms, it is possible to ensure secure communications and effective coordination across heterogeneous fleets of autonomous mobile robots, rovers, drones and humanoids.

What technologies are used to analyse video streams in real time without compromising operational latency or data privacy?

The system processes video streams efficiently and locally, directly on the robotic infrastructure. Advanced, lightweight machine learning models, such as Qwen, run locally to perform video anomaly detection and object flagging in real time. This edge computing approach ensures data privacy and makes critical operational insights immediately available, without relying on continuous transmission to the cloud.

How are autonomous drones used for perimeter monitoring in remote or hard-to-reach areas protected?

Autonomous drone deployments for physical security adopt a rigorous zero-trust paradigm to prevent backdoors and security deviations. The architecture integrates cryptographic foundations, secure communication protocols and data pipeline hardening. In addition, Low Earth Orbit satellite connectivity is used to ensure continuous, reliable and secure surveillance even in changing environmental conditions or isolated areas.

Spike Reply

Spike Reply specialises on Cyber Security, Personal Data protection and tailored Managed Security Services. Spike Reply Cyber Security services range from helping customer to develop an effective cyber risk management program, in line with the strategic objectives and risk appetite of the organization, to the planning, design and implementation of all the corresponding technological, legal, organizational, underwriting and risk-limiting countermeasures. With a broad network of partnerships, Spike Reply select the most appropriate security solutions and helps organizations to improve their cyber response capabilities through its advanced threat simulation.