AI in Cybersecurity: Yesterday’s Promise, Today’s Reality

1685027640 AI in Cybersecurity Yesterdays Promise Todays Reality | itkovian

Together, the consumerization of AI and the advancement of AI use cases for security are creating the level of trust and effectiveness needed for AI to start having real-world impact in the Security Operation Centers (SOCs). . Digging further into this evolution, let’s take a closer look at how AI-powered technologies are making their way into the hands of cybersecurity analysts today.

Drive cybersecurity with speed and accuracy through artificial intelligence

After years of experimenting and refining with real-world users, along with the continued advancement of AI models themselves, AI-powered cybersecurity features are no longer just buzzwords for early adopters or mere functionality based on models and rules. The data has exploded, as have the signals and meaningful insights. Algorithms have matured and can better contextualize all the information they are feeding, from different use cases to raw and unbiased data. The promise we’ve been waiting for AI to deliver all these years is unfolding.

For cybersecurity teams, this translates into the ability to drive game-changing speed and accuracy into their defenses and perhaps, finally, gain an edge against cybercriminals. Cybersecurity is an industry that inherently depends on speed and accuracy to be effective, both of which are inherent characteristics of AI. Security teams need to know exactly where to look and what to look for. They depend on the ability to move fast and act quickly. However, speed and accuracy are not guaranteed in cybersecurity, primarily due to two challenges plaguing the industry: a skills shortage and a data explosion due to infrastructure complexity.

The reality is that a finite number of people in cybersecurity today take on infinite cyberthreats. According to an IBM study, defenders are outnumbered: 68% of cybersecurity incident responders say it’s common to respond to multiple incidents at once. There is also more data flowing through an enterprise than ever before, and that enterprise is increasingly complex. Edge computing, the Internet of Things, and remote demands are transforming modern enterprise architectures, creating mazes with significant blind spots for security teams. And if these teams can’t « see, » then they can’t be precise in their security actions.

Today’s matured AI capabilities can help address these hurdles. But to be effective, AI needs to inspire trust, so it’s crucial to surround it with barriers that ensure reliable security outcomes. For example, when you drive speed for the sake of speed, the result is uncontrolled speed, which leads to chaos. But when the AI ​​is trusted (that is, the data we train models with is free from bias and the AI ​​models are transparent, drift-free, and explainable) it can drive reliable speed. And when paired with automation, it can significantly improve our defense posture by automatically taking action throughout the entire incident detection, investigation and response cycle, without relying on human intervention.

The « right hand » of cybersecurity teams

One of the common and mature use cases in cyber security today is threat detection, with AI introducing additional context from large and disparate datasets or detecting anomalies in user behavioral patterns. Let’s look at an example:

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