Security teams are facing a challenging new reality: the detection-and-response approach to cyber threats was built for a different era. Yet most organizations are still relying upon this dated cybersecurity model. Gartner has reported that conventional detection and response limitations will transform IT security, and projects that by 2030, 50% of spending will shift to preemptive security due to the changes to the threat landscape in the AI era.
Attackers were already moving faster than security teams could respond, and the rise of
AI-enabled threats further expands attacker advantage. Threat actors are using AI to generate new malware variants at scale, evade traditional detection methods, and accelerate their operations, frequently compressing attack timelines from days or weeks to hours—or even minutes.
For defenders who are already overburdened, this creates even more alerts, triage, false positives, strain on overworked security teams, and higher risk of burnout. According to research from Total Assure, 76% of organizations report they cannot match the speed of
AI-powered attacks.
In our latest ebook titled “Why Detection and Response Is Being Outpaced by AI-Enabled Threats”, Trinity Cyber examines why the traditional detect-and-respond model is insufficient to combat AI-enabled threats, and practical steps security leaders can take now.
Inside the ebook, you'll learn:
The cybersecurity industry has spent decades trying to optimize detection and response. But as attack speed accelerates, organizations must ask a different question: how can we stop more threats in the network, without false positive and operational burdens, before they land in our environment, versus trying to respond after the fact?