Rachel Jin, chief enterprise platform officer at Trend Micro, explains how multiple forms of artificial intelligence (AI) will be used to predict and disrupt cyberattacks even as they grow in volume and sophistication.

As cyberattacks grow in scale, speed, and complexity, Jin argues that the security community can no longer afford to be purely reactive. The future lies in predictive and adaptive systems that spot patterns, anticipate threats, and act autonomously before damage occurs.

They highlight the convergence of multiple AI disciplines—machine learning, behavioral analytics, and generative AI—and how their combined capabilities can strengthen both detection and response. Rather than treating AI as a single monolithic technology, Jin describes it as an ecosystem of complementary models: one for anomaly detection, another for behavioral baselining, another for decision automation. Together, they form a layered defense that continuously learns from new data and evolves with the threat landscape.

Jin also stresses the importance of human-AI collaboration. Predictive systems excel at scale, but they require expert guidance to interpret intent, adapt to new attack vectors, and apply security controls responsibly. Building resilient defenses, she notes, depends on blending automation with human judgment—especially when adversaries are using AI themselves to generate polymorphic and evasive attacks.

There’s a broader shift underway in cybersecurity: from visibility to foresight. The goal is no longer just to detect breaches quickly, but to predict and prevent them altogether. As AI systems grow more sophisticated, organizations that can harness predictive intelligence to make faster, more precise security decisions will be the ones able to turn the tide in an increasingly asymmetric cyber battlefield.