AI in Embedded Systems: Navigating Challenges and Solutions

Artificial intelligence (AI) is revolutionizing embedded systems, transforming how technology integrates into daily life. No longer limited to basic tasks, AI is now leveraged in smart cars, industrial automation, medical devices, and intelligent home appliances so that they can learn, adapt, and make decisions in real-time.  

However, cybersecurity threats have also become more prevalent thanks to AI, and these security concerns must be addressed.   

Tech companies like NVIDIA have pioneered AI capabilities in embedded systems, with the company’s hardware accelerating AI adoption in everyday devices. This increased advancement also helps cybercriminals raise the frequency and scale of attacks.  

These developments bring both heightened risk and innovative defenses. This article discusses how developers can protect AI-enabled embedded systems and address the security challenges posed by the combination of AI and internet-connected devices.  

Emerging AI-Driven Security Risks in Embedded Systems  

AI is now embedded in many devices that people rely on daily. But this increasing integration of AI into embedded systems can introduce new security vulnerabilities. The combination of limited resources, physical accessibility, and long deployment cycles mean that multiple potential points of attack could leave embedded systems vulnerable to cyberattacks.  

The embedded security market has grown significantly, with some reports estimating its worth at over $12 billion by 2032. As these systems proliferate, more cybercriminals will seek to exploit vulnerabilities.   

In one recent report, it was revealed that there were several vulnerabilities identified in NVIDIA GPUs, which could leave users at risk of denial-of-service attacks and data tampering. This flaw, identified by Wiz, was caused from a misconfiguration in how Open Containers Initiative hooks are handled. It enabled users to set and perform actions at designated points during a container’s lifecycle. 

Such embedded systems offer incredible processing power, but this power can also serve as an entry point for attackers if not properly secured. Flaws identified in these systems could lead to both financial and reputational damage.  

Everyday Devices Targeted by Cybercriminals  

From voice assistants to AI-powered health monitoring devices, embedded systems are increasingly present in homes, businesses, and government settings. As everyday devices become smarter, the likelihood increases that cybercriminals will attempt to access secure data by any means possible, including through smart devices.   

The same AI that allows these devices to function seamlessly can also be a weak link in the security chain. For example, AI-powered security cameras or home automation systems can be hacked, enabling cybercriminals to access private footage or even control home security features.

In one instance, researchers discovered that Gemini AI prompts could be used to create an agent that could control Google Home smart devices. Google announced that it had introduced multiple fixes to prevent these promptware vulnerabilities from being manipulated. However, this reveals how cybercriminals are rapidly developing their strategies to discover new vulnerabilities to target smart devices.  

AI-powered infrastructure is also becoming increasingly common with companies like NVIDIA, revealing that its technologies will be used to increase the capabilities of smart cities. This includes AI-powered traffic-management systems. However, as these systems rely on a series of interconnected devices, cyberattacks could have widespread implications, compromising public safety.   

Another sector that’s increasingly investing in AI-enabled devices is the medical industry, with hospitals using smart devices to monitor heart rates, deliver insulin, or perform diagnostics. NVIDIA announced significant investments in the healthcare technology industry, with plans to bring AI into hospitals with physical robots to undertake a range of tasks, from X-ray imaging to linen delivery.   

Health-monitoring devices, including oximeters and peak flow meters, have also become incredibly popular, with the market for these devices expected to reach $270 billion by 2029. However, research has shown that they face risks such as spoofing, data injection, and manipulation of settings.  

If hackers gain control over these systems, they could change settings or even cause a device to malfunction, endangering patient safety.  

Ensuring Robust Security for Embedded Systems  

As AI-enabled embedded systems evolve and become integrated into more devices for a wide range of purposes, the strategies for properly securing them also need to evolve. Traditional cybersecurity measures, such as firewalls, antivirus software, and data encryption, are not extensive enough to protect advanced AI systems. These strategies include: 

• Secure boot and chain of trust: A chain of trust and secure boot management are at the foundation of securing embedded systems. This process makes sure that only authenticated software is loaded at every stage of the boot process from power-on through the bootloader, operating system, applications, and over-the-air updates. If any link in the chain is compromised, the device will refuse to boot, preventing any unauthorized person from controlling the device. Without a secure boot, the system is vulnerable to rootkits and malware.   
• Data encryption: Using end-to-end encryption to safeguard data in transit ensures that communication between devices remains private and secure. Tools like dm-crypt and fscrypt protect data at rest by encrypting the underlying storage or filesystems. There’s an extensive list of existing open-source technologies that can be used to secure a device, depending on its requirements.  
• Improve redundancy mechanisms: Redundancy mechanisms such as failover systems and backup protocols mean that systems can continue operating in the event of a system failure. If the primary component is compromised due to a cyberattack or hardware failure, the system could continue running by switching to backup components. 
• Use AI for proactive monitoring: AI can also be part of the defense strategy in addition to conventional security methods. Embedded systems could use machine-learning models to monitor real-time behavior and detect unusual activity that may indicate a threat. Unsupervised learning techniques such as clustering, autoencoders, and anomaly detection algorithms also allow these systems to identify previously unknown attack patterns, providing a proactive layer of protection beyond static rule sets. These unsupervised learning techniques are able to provide more advanced security than human cybersecurity experts, highlighting the benefit of using AI for proactive monitoring and threat detection. 

Ultimately, safeguarding AI-enabled embedded systems requires a multilayered approach that uses both traditional practices and modern security technologies. By building on a robust architecture, developers can fully harness the potential of AI in embedded devices, strengthening user safety and ensuring trust.