As AI systems become more pervasive and powerful, they also become more vulnerable.
Software-based security measures such as application firewalls, intrusion detection, and patch management are important for protecting AI systems — but they are not enough. These solutions cannot fully address the risks introduced by modern AI architectures and the distributed workloads they support.
That’s why a hardware-based approach to security is also required. The strategy and related solutions, which complement software features, begin at the silicon level and embed security from the ground up.
AI’s expanding attack surface
AI workloads are growing at an unprecedented rate, from cloud to edge, with model complexity doubling every few months. This surge is driving demand for specialized, high-performance silicon architectures, including multi-die systems and advanced interconnects. While these innovations enable breakthroughs in performance and scale, they also increase the number of potential vulnerabilities.
AI systems process vast amounts of sensitive data, such as biometric identifiers, medical diagnostics, and financial records. This makes them attractive targets for hackers, insider threats, cybercriminal organizations, and even nation-state actors. What’s more, new methods of attack are emerging, including:
- Data poisoning — Manipulating training datasets to skew model behavior.
- Model theft — Extracting proprietary models through side-channel or inference attacks.
- Decision tampering — Altering inference results or sensor inputs to compromise system integrity.