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With the advent of AI, the cybersecurity industry must adapt to new threats. This means integrating security into AI projects and workflows from inception, known as secure by design. It also means building a culture and communication that ensures security is a priority at all levels of an organisation.

The ability to rapidly detect and analyze patterns of behavior is an important security use case for AI. By detecting abnormal activity, AI can help to prevent cyberattacks, identify vulnerabilities and limit their impact.

It can also reduce the amount of time spent on manual tasks by automating routine processes. This frees up cybersecurity teams to focus on more strategic efforts and can improve response times when threats are detected.

However, it is crucial to keep in mind that even the most advanced AI systems can be compromised by hackers and cybercriminals who develop novel methods of attack. This makes it critical for cybersecurity leaders to continually evaluate AI systems and update their training data to keep up with new threat tactics.

One such tactic involves manipulating input data to manipulate the output of an AI system, known as a bait and switch attack. This type of attack can skew the results of an AI model and lead to inaccurate detection of security threats.

Other common attacks against AI include data poisoning and supply chain attacks, which exploit vulnerabilities in third-party components or software libraries used by an AI system. These types of attacks can expose sensitive data or allow for unauthorized access to a system.