Enterprise security teams are increasingly deploying inference security platforms to combat a new wave of AI-driven runtime attacks, with 2026 seeing a surge in adoption as Chief Information Security Officers (CISOs) grapple with rapidly shrinking windows of vulnerability. The shift is driven by attackers exploiting weaknesses in AI agents operating in production environments, where breakout times are now measured in seconds, far outpacing traditional security measures.

The urgency stems from the speed at which adversaries are now able to weaponize vulnerabilities. According to Mike Riemer, field CISO at Ivanti, AI has dramatically accelerated the process of reverse engineering patches. "Threat actors are reverse engineering patches within 72 hours," Riemer stated in a recent interview with VentureBeat. "If a customer doesn't patch within 72 hours of release, they're open to exploit. The speed has been enhanced greatly by AI."

CrowdStrike's 2025 Global Threat Report highlighted the severity of the situation, documenting breakout times as low as 51 seconds. This means attackers can move from initial access to lateral movement within a network before security teams even receive an alert. The report also revealed that a significant majority, 79%, of detections were malware-free, indicating adversaries are increasingly relying on "hands-on keyboard" techniques to bypass conventional endpoint defenses.

Traditional security models are proving inadequate in this new threat landscape because they lack the visibility and control needed to monitor and protect AI agents at runtime. The problem is compounded by the fact that many enterprises still rely on manual patching processes, which can take weeks or even months to complete, leaving them vulnerable to exploitation.

Inference security platforms address this challenge by providing real-time monitoring and protection for AI models in production. These platforms can detect and prevent attacks such as model evasion, data poisoning, and adversarial reprogramming, which are specifically designed to target AI systems. By providing visibility into the behavior of AI models at runtime, these platforms enable security teams to identify and respond to threats before they can cause significant damage.

The adoption of inference security platforms is expected to continue to grow in the coming years as AI becomes more prevalent in enterprise environments and attackers continue to refine their techniques. The ability to protect AI models at runtime is becoming a critical requirement for organizations looking to secure their data and maintain their competitive advantage.