Artificial Intelligence (AI) in Cybersecurity

Artificial intelligence's application to security has received a lot of attention. Some experts will claim that it is the single most important factor in success, while others will claim that AI is only marketing speak with no practical use. The reality, in our opinion, lies somewhere in the middle. Although AI can help with security, it is not a solution. Having said that, there are three main ways that businesses should use AI to enhance their security teams.

1. Attack Prevention

Given the current shortage of security professionals, the application of AI in security should be extremely concentrated on maximizing the efforts of security teams.

According to a recent JupiterOne survey, security teams are in charge of more than 165,000 cyber assets, including individuals, devices, network assets, apps, and cloud workloads. It would be difficult to defend so many assets. In fact, 61 percent of firms claimed they wouldn't be able to spot important threats without AI, according to research by Capgemini Research Institute.

By automatically filtering through data logs and identifying pertinent dangers, AI and machine learning can lessen the strain for analysts. Artificial intelligence, when applied successfully, will not only warn security professionals about threats but will also categorize different sorts of attacks, enabling security teams to plan effective defenses. With this kind of continuous, thorough examination of behavior patterns, analysts may handle even complicated threats with a great deal less manual labor, minimizing errors brought on by fatigue and burnout.

2. Intrusion Detection

Because all security experts are aware that a motivated and capable attacker will eventually gain access, prevention and detection go hand in hand. Anomaly detection, another area of security where AI excels, is crucial to finding such a breach. While sifting through the never-ending monotony of operations logs in search of unusual behavior, AI doesn't become bored and worn out as humans do.

When it comes to alert weariness, a lunatic fringe we made for ourselves, AI can be even more helpful. There are far too many security alerts as a result of our efforts to find every potential threat. The majority of warnings that reach the security operations center are false positives, though, and security professionals must sift through them to uncover real threats. By determining which alarms require immediate attention, which can wait, and which can be completely ignored, AI can help security teams allocate their time and resources wisely.

3. Application Security and Developer Productivity

One of the often-overlooked use cases for AI is application security. In today's competitive climate, companies are constantly launching new apps and updates. It's easy for AppSec teams to fall behind, and these challenges only snowball when vulnerabilities within code are discovered and both AppSec and development teams must divert their time and attention to remediating the issue.

The fundamental value proposition for AI in AppSec is acting as a force multiplier by taking on repetitive and menial activities, similar to attack prevention and intrusion detection. In order to establish relationships and gain an understanding of the danger a vulnerability represents, it is necessary to sift through hundreds or thousands of security findings. With each new application launch or update, AI can drastically cut down on the amount of time AppSec teams spend waiting, allowing them to concentrate on more demanding and crucial duties.

Conclusion

In the meanwhile, AI can be very helpful in assisting security teams in making sense of the mountains of data an organization creates and in ensuring that apps are launched securely. There may come a moment when AI saves security, but for the time being, it may be very helpful.

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