Enterprise security teams are increasingly turning to inference security platforms as they struggle to defend against a new wave of AI-powered runtime attacks. These attacks exploit vulnerabilities in AI agents operating in production environments, where traditional security measures often lack visibility and control.
The shift is driven by the speed and sophistication of modern attacks. CrowdStrike's 2025 Global Threat Report revealed that breakout times – the time it takes for an attacker to move from initial access to lateral movement within a network – can be as fast as 51 seconds. This leaves security teams with little time to react, especially considering that patch windows can stretch into hours or even days. The same report also indicated that 79% of detected attacks were malware-free, relying instead on "hands-on keyboard" techniques that bypass traditional endpoint defenses.
Mike Riemer, field CISO at Ivanti, highlighted the accelerating pace of weaponization. "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 shrinking window of opportunity is forcing CISOs to re-evaluate their security strategies.
Inference security platforms are designed to address these runtime vulnerabilities by monitoring and analyzing the behavior of AI models in real-time. These platforms can detect anomalies, identify malicious inputs, and prevent unauthorized access to sensitive data. By providing visibility and control over AI agents in production, inference security platforms aim to close the gaps left by traditional security tools.
The adoption of inference security platforms represents a significant shift in the cybersecurity landscape. As AI becomes more prevalent in enterprise operations, the need to protect these systems from attack will only continue to grow. The ability to detect and respond to runtime threats in real-time will be crucial for maintaining the security and integrity of AI-powered applications.
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