In the past, cybersecurity concerns centered on data privacy—protecting sensitive information from breaches and ensuring encrypted communication. But as automation expands its reach, security is no longer just about privacy. It’s about safety. 

Machine vision and AI-driven automation are putting once-futuristic technologies, such as autonomous vehicles to industrial robots, into the near horizon. This exciting scenery is not without its precautions, since the same technology that makes it automation so powerful, also makes these systems vulnerable to deception, exploitation, and attacks. 

AI, Machine Vision, and Safety Risks 

“Traditionally, security concerns were mostly about privacy,” said John Stih, sensor specialist at Future Connectivity Solutions. “But with automation, safety has become a major issue.” 

Consider an autonomous vehicle navigating a highway. Its lane-detection system relies on machine vision to distinguish between pavement and road markings. If an attacker manipulates those visual inputs, the car’s decision-making could be thrown into chaos, leading to potentially serious consequences. 

AI systems are constantly trained to interpret visual data and learn from infinite scenarios. However smart, these systems can be fooled. Don Gunn, processor specialist at Future Intelligent Solutions, pointed out a relatively simple example: Drivers in automated vehicles today must be in active monitoring mode to take control of the vehicle at any required time. To ensure this, automated vehicle manufacturers first relied on steering wheel movement but are moving on to internal cameras and machine vision to track eye movements in the driver. But what if the driver wears sunglasses or is deliberately covering his eyes with fake open eye prints to take a nap while the vehicle drives itself. It’s a simple and hypothetical premise, but can AI detect the trick? 

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This deception extends beyond vehicles. In manufacturing plants, facial recognition systems help monitor worker movement, ensure compliance, and improve safety. Yet some individuals have managed to fool these AI-powered cameras by wearing t-shirts with printed faces, tricking the system into misidentifying their presence. “That’s why liveness detection is crucial,” Gunn explained. Some AI systems now incorporate infrared (IR) cameras, detecting heat signatures to verify whether an image represents a living, breathing human rather than a static decoy. 

The Trade-Off Between Automation and Security 

Here’s where the equation gets more interesting. As automation continues to advance, we find more areas where we could benefit from it. But as Ed Baca, regional solutions manager at Future Intelligent Solutions, highlighted: “The more sensors we add to automation, the more points of attack emerge.” 

Cameras and sensors enhance system intelligence, improving reliability and decision-making. But each additional component also introduces new vulnerabilities.  

Every sensor—whether tracking movement in a factory or scanning for obstacles in an autonomous robot—is a potential entry point for an attacker. Do we use more cameras and risk security gaps, or fewer cameras with stronger protection and reduce visibility? 

Exploiting AI-Enabled Systems 

It’s not just visual deception. Attackers will look for any open door to gain access, all being critical, but some being even more sensitive than others. For example, a key target could be the processors powering AI models.  

Many AI-enhanced cameras and automation systems run on high-performance processors, often using Linux-based software. 

“Since Linux is so well understood, bad actors can exploit vulnerabilities and load their own programs in the background,” Gunn warned. A compromised AI-powered camera, for instance, could be manipulated to stream unauthorized footage, or worse, provide false inputs to an industrial control system. 

To counteract these threats, modern processors include built-in security mechanisms like secure boot protocols. These ensure that only authenticated, verified software can run on the system, preventing rogue programs from taking control. 

Cybersecurity and Network Protection 

Then there’s another level. Autonomous systems are rarely isolated machines. They usually form a part of a web of networked devices which enhance their functionalities, but you guessed it, each becomes a potential vector for cyberattacks. 

“Security isn’t just about the software running on a device—it’s about who can access it” Gunn mentioned. A vulnerability in one node of a network can be exploited by an external hacker, a rogue insider, or an attacker leveraging a weakness in cloud-based infrastructure. 

Consider a high-risk scenario like a power plant’s cooling system. If an unauthorized entity were to breach the control system and shut it down, the consequences could be catastrophic. The challenge, therefore, is not just to secure individual components but to establish robust network defenses, limiting access, isolating systems with firewalls, and ensuring critical infrastructure remains protected from intrusion. 

It’s an arms race. As automation becomes more intelligent, adversaries will try to outsmart it, and so for this reason, remaining vigilant and updated is no longer optional—it’s imperative.