Cybersecurity best practices are greatly aided by using Artificial Intelligence (AI) and Machine Learning (ML) technology, as shown by this sector’s growth. According to one study, the market for artificial intelligence in cybersecurity is expected to reach $46.3 billion by 2027.


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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningAI drastically improves a business’s cybersecurity posture by applying the technology to help identify, isolate, or remediate potential cyber threats from penetrating a business’s network.

Read more: AI vs Machine Learning: What Are Their Differences & Impacts?

4 Benefits of Using Artificial Intelligence in Cybersecurity

Here are some of the tangible benefits AL/ML brings to cybersecurity:

The technology gets better over time: As AI/ML learns a business network’s behavior and recognizes patterns on the network over time, it becomes more difficult for hackers to penetrate a business’s network.

AI/ML can handle lots of data: NGFW firewalls scan hundreds of thousands of files daily with no degradation of service to the network users.

Faster detection and response time: Using AI/ML software in a firewall and anti-malware on a laptop or desktop is more effective and responsive to threats, limiting the need for human intervention.

Better overall security: AI/ML provides protection at the macro and micro levels, making it very difficult for malware to penetrate a business network. This frees up IT teams to deal with more complex threats, improving overall security posture.

How AI Stops Cyber Attacks

Artificial Intelligence helps cybersecurity at the macro and micro level. From a macro perspective, a good example is how next-generation firewalls (NGFW) protects the enterprise. Embedded ML algorithms detect and block suspicious files without using any type of historical, signature-based database to compare the new cyber threat against.

The ML algorithm is used to detect specific behaviors of a file; if the file meets specific thresholds, the file is isolated and analyzed.

The ML algorithm is used to detect specific behaviors of a file; if the file meets specific thresholds, the file is isolated and analyzed. Each time the ML algorithm is used, the NGFW firewall learns from previous analyzed behaviors and becomes more proficient at detecting suspicious files. In this way, NGFW firewalls don’t use any offline tools that slow down the network throughput, so users don’t experience a lag in network response time.

From the micro, device-level viewpoint, anti-malware software uses detection methodologies based in heuristic analysis. In short, AI identifies potential malware it has never seen before.

Antivirus software works differently. Antivirus software uses signature-based detection, which means it uses a previously identified signature comparison of a known virus in a signature database. If the antivirus software has never seen this virus, the antivirus software will not stop the cyber threat. […]

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