Enterprise security teams are increasingly turning to inference security platforms as AI-driven runtime attacks expose critical vulnerabilities, according to a recent report. The shift comes as traditional security measures struggle to keep pace with attackers exploiting weaknesses in AI agents operating in production environments.
The accelerated speed of these attacks is a key driver behind the adoption of inference security platforms. CrowdStrike's 2025 Global Threat Report highlighted the rapid breakout times, with attackers achieving lateral movement in as little as 51 seconds after initial access. This speed renders traditional security measures, which often rely on detecting malware signatures, ineffective. The report also indicated that 79% of detected attacks were malware-free, utilizing "hands-on keyboard" techniques to bypass endpoint defenses.
Mike Riemer, field CISO at Ivanti, emphasized the shrinking window of opportunity for patching vulnerabilities. "Threat actors are reverse engineering patches within 72 hours," Riemer told 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." This rapid weaponization of vulnerabilities is forcing CISOs to seek real-time protection against attacks targeting AI models and their inferences.
Inference security platforms address this challenge by monitoring the behavior of AI models at runtime, detecting anomalies and potential attacks based on deviations from expected patterns. These platforms provide visibility and control over AI agents in production, enabling security teams to identify and respond to threats before they can cause significant damage.
The rise of AI-enabled attacks represents a fundamental shift in the threat landscape. Traditional security models, designed to protect against known malware and vulnerabilities, are ill-equipped to handle the dynamic and adaptive nature of these new threats. The industry impact is significant, as organizations across all sectors grapple with the challenge of securing their AI deployments. The deployment of inference security platforms is expected to continue accelerating throughout 2026 as organizations seek to mitigate the risks associated with AI-driven runtime attacks.
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AI-driven runtime attacks are outpacing traditional security measures, forcing CISOs to adopt inference security platforms by 2026. Attackers are leveraging AI to rapidly exploit vulnerabilities, with patch weaponization occurring within 72 hours, while traditional security struggles to detect malware-free, hands-on keyboard techniques. This shift necessitates real-time monitoring and protection of AI agents in production to mitigate risks.
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