AI in cybersecurity: Yesterday’s promise, today’s reality

Together, the consumerization of AI and the enhancement of AI use cases for security are creating the level of trust and efficiency needed for AI to start making real-world impact in control centers. security practices (SOC). Digging deeper into this evolution, let’s take a closer look at how AI-driven technologies are reaching the hands of cybersecurity analysts today.

Drive cybersecurity with speed and accuracy through AI

After years of testing and perfecting with real-world users, along with the constant advancement of AI models themselves, AI-driven cybersecurity capabilities are no longer just a buzzword for those who are not. early users or simple model and rule-based capabilities. The data has exploded, as well as meaningful signals and insights. Algorithms are mature and can better contextualize all the information they’re importing—from diverse use cases to raw, unbiased data. The promise that we have been waiting for AI to fulfill all these years is being realized.

For cybersecurity teams, this translates to the ability to accelerate the speed of game change and the accuracy of their defenses—and perhaps, ultimately, gain an edge in the face of crime. cyber crime. Cybersecurity is an industry that is inherently dependent on speed and accuracy for efficiency, both intrinsic to AI. Security teams need to know exactly where to look and what to look for. They depend on the ability to move quickly and act quickly. However, speed and accuracy are not guaranteed in cybersecurity, mainly due to two challenges plaguing the industry: a lack of skills and a data explosion due to the complexity of the infrastructure.

The reality is that a finite number of people in the cybersecurity field today face a multitude of cyber threats. According to an IBM learnsecurity forces outnumbered—68% of those responding to cybersecurity incidents said it was normal to respond to multiple incidents at once. There’s also more data flowing through a business than ever before—and that business is increasingly complex. Edge computing, internet of things, and remote needs are transforming modern business architecture, creating a maze of significant blind spots for security teams. And if these teams can’t “see,” they can’t be precise in their security actions.

Today’s mature AI capabilities can help address these obstacles. But to be effective, AI must build trust—it’s crucial that we surround it with protective barriers to ensure reliable security results. For example, when you drive for speed, the result is uncontrolled speed, which leads to chaos. But when the AI ​​is trusted (that is, the data we use to train the models is not biased, and the AI ​​models are transparent, non-drift, and explainable), it can Reliable speed boost. And when combined with automation, it can dramatically improve our defenses—automatically taking action across the entire incident detection, investigation, and response lifecycle without relying on into human intervention.

Cybersecurity team is ‘right hand’

One of the popular and mature use cases in cybersecurity today is threat detection, with AI providing additional context from large and disparate datasets or detecting anomalies in user behavior patterns. Let’s look at an example:


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