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Artificial Intelligence in Cybersecurity

What is AI in Cybersecurity?

Artificial intelligence (AI) has both advantages and challenges in cybersecurity. AI is a powerful cybersecurity tool, but it does have its limitations, and cyberattackers work tirelessly to find new ways to leverage its power for their cybercrimes.

AI improves cybersecurity in the following ways:

Vulnerability management. Traditional vulnerability databases manage known vulnerabilities. AI and machine learning techniques can analyze baseline behaviors on a network and can identify unusual behavior that might indicate a zero-day attack, which is a previously unknown exploit that exposes a vulnerability in software or hardware. The sooner that a vulnerability is identified, the sooner that it can be patched.

Threat hunting. Traditional security techniques use signatures or threat indicators to identify known threats. This works for the majority of threats, but AI threat hunting can increase the detection rate by integrating behavioral analysis into the hunting process. The combination of traditional security techniques and AI threat hunting is more effective than traditional methods alone and can reduce the time from intrusion to discovery as well.

Network security. AI improves its knowledge by learning network traffic patterns and recommending security policies and function groupings of workloads that will be most appropriate and secure.

Data centers. Many essential data processes can be optimized and monitored by AI, such as backup power, cooling filters, bandwidth usage, power consumption, and internal temperatures. AI can help to improve the operation of data centers monitoring data center conditions and creating alerts when equipment needs to be repaired or maintained.

Risk analysis. AI can reduce the time that cybersecurity analysts take to make critical decisions and remediate threats.

The following are challenges faced when adopting AI for cybersecurity:

Expense. AI technology is costly, as it requires a substantial investment of time and money in order to build and maintain these technologies.

Cyberattackers. In order to strengthen and improve their malware, cyberattackers learn from existing AI technologies. As they learn more about an AI technology, they can discover new ways to infiltrate a security system. For instance, cyberattackers hacking tasks performed by AI could program the AI-enabled application to ignore suspicious behavior, allowing the hacker to carry out their scheme. Cyberattackers can also leverage AI to create more sophisticated schemes through network monitoring and machine learning.

Datasets. AI models require numerous datasets for effective prediction and protection. Acquiring these datasets may require a substantial investment of time and money.

1 Segal, 2022, “The Impact of AI on Cybersecurity”