Together, the consumerization of AI and advancement of AI use-cases for security are creating the extent of trust and efficacy needed for AI to start out making a real-world impact in security operation centers (SOCs). Digging further into this evolution, let’s take a more in-depth take a look at how AI-driven technologies are making their way into the hands of cybersecurity analysts today.

Driving cybersecurity with speed and precision through AI

After years of trial and refinement with real-world users, coupled with ongoing advancement of the AI models themselves, AI-driven cybersecurity capabilities are not any longer just buzzwords for early adopters, or easy pattern- and rule-based capabilities. Data has exploded, as have signals and meaningful insights. The algorithms have matured and might higher contextualize all the data they’re ingesting—from diverse use cases to unbiased, raw data. The promise that now we have been waiting for AI to deliver on all these years is manifesting.

For cybersecurity teams, this translates into the power to drive game-changing speed and accuracy of their defenses—and maybe, finally, gain an edge of their face-off with cybercriminals. Cybersecurity is an industry that’s inherently depending on speed and precision to be effective, each intrinsic characteristics of AI. Security teams have to know exactly where to look and what to search for. They depend upon the power to maneuver fast and act swiftly. Nonetheless, speed and precision aren’t guaranteed in cybersecurity, primarily because of two challenges plaguing the industry: a skills shortage and an explosion of knowledge because of infrastructure complexity.  

The truth is that a finite number of individuals in cybersecurity today tackle infinite cyber threats. In accordance with an IBM study, defenders are outnumbered—68% of responders to cybersecurity incidents say it’s common to reply to multiple incidents at the identical time. There’s also more data flowing through an enterprise than ever before—and that enterprise is increasingly complex. Edge computing, web of things, and distant needs are transforming modern business architectures, creating mazes with significant blind spots for security teams. And if these teams can’t “see,” then they will’t be precise of their security actions.

Today’s matured AI capabilities can assist address these obstacles. But to be effective, AI must elicit trust—making it paramount that we surround it with guardrails that ensure reliable security outcomes. For instance, if you drive speed for the sake of speed, the result’s uncontrolled speed, resulting in chaos. But when AI is trusted (i.e., the information we train the models with is freed from bias and the AI models are transparent, freed from drift, and explainable) it could drive reliable speed. And when it’s coupled with automation, it could improve our defense posture significantly—routinely taking motion across your complete incident detection, investigation, and response lifecycle, without counting on human intervention.

Cybersecurity teams’ ‘right-hand man’

Considered one of the common and mature use-cases in cybersecurity today is threat detection, with AI bringing in additional context from across large and disparate datasets or detecting anomalies in behavioral patterns of users. Let’s take a look at an example: