Cyber and data security are key concerns for physical AI such as this ANYmal inspection robot. Source: ANYbotics
If you follow the robotics industry, you have likely seen the wave of humanoids performing backflips, robot dogs navigating parkour, and robotic arms folding laundry. This pace of innovation is inspiring, and it is fascinating to see the impact of AI on physical machines. However, as we move technology from the controlled safety of the lab into the complexity of the real world, a security headline serves as a stark reminder for the broader industry.
Reports recently surfaced regarding critical security flaws in consumer robot vacuums. Interestingly, this was discovered by a software engineer who stumbled into the vulnerability by accident, gaining full control over devices and accessing cameras and microphones to peer into private homes.
While a vulnerability in a living room is a serious privacy concern, an autonomous robot in a chemical plant or a high-voltage power grid presents a significantly higher level of risk. In these environments, a cybersecurity breach is a risk to critical industrial assets and, potentially, to human life.
It is easy to get excited about robots that can jump or dance, but for the industry to truly scale, the focus must shift. It is not enough for a machine to move. We must understand how to deploy it safely and, crucially, how to secure the massive amounts of data required to train these physical systems.
I believe the next decade of robotics will be won by the company that builds the most trusted, secure data loop in the real world.
Training AI: Why simulation hits a ceiling
To reach a meaningful scale, robots need to do more than move. They need to solve high-value industrial applications that require a sophisticated level of contextual intelligence.
One example of that is Inspection Intelligence: the process of turning consistent asset condition monitoring, multi-modal sensing, and contextual analysis into actionable intelligence for industrial operations. Where robots capture the state of equipment, identify anomalies, notify the human workforce, and act as a decision-support instrument. This level of autonomy, analysis, and contextual decision-making requires the machine to understand the specific application and environment it is serving.
For basic mobility — how a robot balances and walks — simulation works remarkably well. We can train a robot to climb stairs in a virtual world millions of times before it ever touches concrete. This sim-to-real pipeline is one reason why the latest cutting-edge robots are so robust on their feet.
But for Inspection Intelligence and autonomy, simulation has a fundamental ceiling. You cannot easily simulate the vibration profile of a failing pump or the subtle acoustic signature of a high-pressure gas leak in a chemical reactor.
Beyond specific equipment, there is also the challenge of training a robot to navigate dynamic outdoor environments. Industrial sites are not static labs. Inspection robots must navigate heavy rain, thick mud, and shifting lighting, all while not getting into people’s way and avoiding temporary maintenance scaffolding.
The only way to build the high-level intelligence that’s required for these edge cases is to collect diverse, high-fidelity data from the field. However, this creates a fundamental barrier to entry. This data is locked behind the gates of critical, secure infrastructure.
Industrial operators will not grant access to their most sensitive facilities if they cannot trust the integrity of the end-to-end data flow. Scaling industrial intelligence is impossible without an uncompromising approach to data security.
The data flywheel: From scarcity to intelligence
In the software world, growth is about distribution. In physical AI, growth is about the “data flywheel.”
Robots have the ability to collect hundreds of thousands of autonomous inspection points every month. This high-fidelity, multi-modal ground truth includes thermal profiles, acoustic signatures, vibration baselines, and gas concentration readings. All must be captured with the frequency, consistency, and objectivity that manual inspection rounds just cannot achieve.
Collected in environments where humans often cannot get to safely, this data builds something that has never existed before in industrial operations: a comparable inspection baseline across every asset, over time. That baseline is what allows reliability engineers to see an asset’s degradation curve and intervene before a minor anomaly becomes a multi-million-dollar shutdown.
As robotic fleets transition from pilot programs to large-scale industrial deployment, security frameworks have evolved from theoretical models into operational necessities. For high-scale implementations, protecting the integrity of every sensor readout, 3D model, and safety-critical insight is the baseline for industrial trust.
The following principles reflect the hardened security standards required to manage the flow of data from remote assets back to centralized command systems:
1. The full-stack responsibility for security
In the consumer world, Apple is the gold standard for security because it takes responsibility for the entire stack: silicon, hardware, and OS. Robotics requires this same philosophy.
If you build software on top of generic, third-party hardware without taking ownership of the design, you inherit vulnerabilities you cannot fix. We saw this recently when research into low-cost robotics platforms revealed catastrophic failures.
This includes hardcoded cryptographic keys discovered in the Unitree G1 humanoid and undocumented backdoor services in the Unitree Go1 quadruped that established remote tunnels to external servers without user consent.
When security is an afterthought, a robot becomes a technological Trojan horse.
Industrial-grade robotics relies on full-stack responsibility. By integrating hardware and software within a unified architecture, autonomous systems achieve a level of control and security that is often unattainable with fragmented, off-the-shelf platforms.
Whether components are custom-built or sourced through audited partnerships, maintaining accountability for security outcomes is paramount. This requires a “security-first” architecture designed from the ground up—incorporating rigorous supplier vetting and hardware verification during production. This deep integration ensures data integrity across every layer, securing the encryption path from the physical sensor to the cloud server.
Delivering inspection intelligence at industrial scale requires more than good software. It requires accountability from the sensor on the robot to the insight on the dashboard. This depth of ownership must be designed into the architecture from Day 1.
Yokogawa has integrated OpreX robot management software with ANYmal inspection robots. Source: ANYbotics
2. Isolation by design
Scaling AI-driven robotics stands in contrast with the rigid constraints of traditional industrial IT. To achieve the intelligence the robotics industry needs, we must bridge the gap between site-level privacy and global learning.
Historically, the response was “air-gapping,” keeping systems entirely offline. But an air-gapped robot is cut off from the collective intelligence of the fleet. It cannot receive vital safety updates or learn from new anomalies detected at other sites.
To solve this, you need a tiered architecture that we call “isolation by design:”
Edge anonymization: Filtering and de-identifying sensitive data before it ever leaves the customer domain. This includes automatically blurring faces, cutting voices, blacking out license plates, and removing other personally identifiable information to ensure privacy.
Multi-tenant siloing: Each customer’s data is kept in logically separated data planes with unique encryption keys.
Federated intelligence: This involves using anonymized telemetry to identify fleet-wide optimizations. If data reveals a new pattern of mechanical wear or a more efficient way to navigate a complex obstacle, we can roll out an update to the entire fleet. Every site benefits from the fleet’s collective experience while maintaining customer privacy.
3. Security is a culture, not a checklist
Even the strongest encryption will fail if the culture does not prioritize responsibility. In our world, “moving fast and breaking things” could mean a refinery explosion.
This is why ANYbotics recently achieved our ISO 27001 certification, becoming the first legged robotics company in the world to reach this standard. For us, this was not a bureaucratic milestone, it was a stress test of our internal information security management system (ISMS).
We passed the multi-stage audit with zero non-conformities on our first attempt. This independently validates that security is not just embedded in our processes, but it is rooted in our culture.
Hannes Wyss, principal software engineer for cybersecurity (third from left), and the team celebrate ISO 27001 certification at the ANYbotics head office in Zurich. Source: ANYbotics
Looking ahead: Security at the speed of AI
As industrial operations enter the age of AI, cyber threats are evolving at an unprecedented pace. To maintain a defensive posture that matches the speed of modern threat actors, the robotics industry is increasingly moving toward AI-driven security.
By using automation and machine learning within the security stack, autonomous systems can identify and neutralize vulnerabilities in real time. This creates a more resilient ecosystem where threat intelligence is shared across networks, allowing the entire industrial infrastructure to learn and adapt to new vectors as they emerge.
As robotic systems gain higher levels of independence, the implementation of strict digital boundaries is essential to ensure that autonomous decision-making remains uncompromised and shielded from external manipulation. This “hardened autonomy” allows industrial operators to remain focused on the primary value of robotic inspection: identifying asset degradation months before failure, gaining visibility where fixed sensors cannot reach, and removing personnel from hazardous environments.
Maintaining the integrity of these baselines and anomaly models is the fundamental requirement for the “trusted foundation” of modern industry. When security is architected at this level, the resulting safety-critical insights are not just data points; they are the verified signals that prevent catastrophic failure and ensure long-term operational continuity.
About the author
About the authorPeter Fankhauser is co-founder and CEO of ANYbotics, a global leader in autonomous mobile robots (AMRs) using artificial intelligence for industrial inspections. He has a doctorate from ETH Zurich and 15 years of experience in robotics.
ANYbotics said it tackles critical industry challenges in safety, efficiency, and sustainability. It designed its ANYmal robots for advanced mobility and real-time data collection, making them suitable for tasks such as routine inspections, remote operations, or predictive maintenance.
With hundreds of customers in energy, power, metals, mining, and chemicals worldwide, ANYbotics claimed that its systems address labor shortages and keep workers out of harm’s way. Founded in 2009, the company has raised more than $150 million in funding and employs 200 experts. It has offices in Zurich and San Francisco.
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