What are the potential risks of adversarial attacks on AI-based cybersecurity systems?

Adversarial attacks on AI-based cybersecurity systems involve manipulating inputs to deceive the system and cause it to make incorrect decisions. This can be done by adding small, carefully crafted perturbations to the input data so that the system misclassifies it. To defend against such risks, measures can include robust training techniques, implementing detection mechanisms for adversarial inputs, deploying diverse models that are resistant to similar attacks, and continuously updating and testing the system’s defenses against evolving attack strategies.