by Sean Toohey
Edge AI has transformed how we use and manage embedded systems by shifting data processing from the cloud to the device. With less reliance on remote servers for processing insights and handling requests, operators benefit from faster responses and swifter workflows.
However, as data processing priorities shift from cloud to edge-based, the way we approach embedded system security must also evolve. To help operators harness the true benefits of modern technologies, below we explore how edge AI is changing embedded system security.
The importance of embedded system security
As the networked environment continues to expand, with the number of active IoT devices projected to surpass 40 billion by 2034, the attack surface associated with embedded systems also grows larger, presenting malicious actors with more vulnerabilities to exploit.
Strengthening security measures within embedded systems like smart sensors, AI security cameras, and industrial automation tools is becoming increasingly important to prevent data breaches and unauthorized access, particularly as threats grow increasingly sophisticated.
Common threats to embedded systems
To protect embedded systems against malicious actions like device hijacking and data theft, custom security measures must be implemented directly into the device. Measures such as implementing encryption through programmable Hardware Roots of Trust (HRoT) should be explored to defend against malicious modifications delivered through evolving attack vectors.
Modern embedded systems face significant threats related to:
- AI-powered exploits: Attackers are using malicious AI models to identify unique vulnerabilities in embedded systems and enact automated cyber-physical attacks; IBM reports that attacks designed to exploit public-facing applications rose by 44% in 2025.
- Ransomware attacks: Attackers are leveraging specialized ransomware designed to target embedded device OSs like FreeRTOS; DNI data reveals ransomware attacks rose by 15% globally in 2024; while CISA claims attacks are growing more destructive.
- Always-on exploits: Remote attackers are more frequently exploiting the always-on nature and often weak credential security of embedded systems to gain unauthorized access; CISA has issued advice on addressing the rise of such attacks in recent years.
- API hijacking: Weak API security can leave cloud-connected embedded systems vulnerable to remote hijacking attacks; in March 2026, the U.S. government identified 4 botnets targeting millions of global devices, illustrating the scale of modern threats.
Edge AI: The key to strengthening embedded systems
Edge AI has great potential to help strengthen embedded system security by minimizing the need to perform data processing outside of the device. Guided by edge AI, custom security measures can be implemented directly into critical systems to defend against evolving threats.
Adaptable Hardware Roots of Trust
Edge AI can strengthen HRoT by allowing for real-time attestation and adaptable anomaly detection at the silicon level. On-device AI models can instantly validate firmware integrity to support a more secure boot process and best address novel, evolving cyber threats, helping to defend against malicious modifications by anchoring protections inside trusted hardware.
Secure inference execution workflows
Edge AI can be deployed through Trusted Execution Environments (TEE) to ensure the privacy of input data and prevent unauthorized access to embedded systems. By isolating an edge AI model within an encrypted area of the TEE, operators can ensure data integrity while processing sensitive information by virtue of performing computation directly at the source.
Protecting edge AI via Secure Boot
Edge AI deployed within embedded systems can share a synergistic relationship with Secure Boot. By enforcing a foundational, hardware-rooted chain of trust, operators can protect edge AI models against compromise during startup, enabling those models to leverage evolving analytics processes to help prevent hijackers from accessing systems and manipulating data.
Enhanced Physical Unclonable Functions (PUFs)
Edge AI can be implemented into PUFs to strengthen device security, support more reliable authentication workflows and defend against data manipulation. Edge-enabled PUFs can be used to generate device-specific cryptographic keys for independent access attempts, which can then be verified via on-device edge AI models to drive secure, adaptable authentication.
Summary
The increasing adoption of IoT devices and sophistication of cyberattacks is introducing new complexities to the field of embedded system security. As remote attackers find new ways to exploit cloud-connected systems, threats against embedded devices grow more voluminous.
Edge AI offers an opportunity to dramatically improve embedded system security by enabling operators to minimize or eliminate cloud-based vulnerabilities. By transitioning to workflows where data is processed locally, operators can reduce the impact of remote attacks.
About the author
Sean Toohey is a freelance journalist and digital media specialist with extensive experience covering news, developments, and emerging trends in the commercial security industry. Currently focused on security technologies and data privacy, Sean’s work explores the adoption and impact of smart technologies like AI, the IoT, and cloud computing on modern industries.