Enterprise security teams are increasingly deploying inference security platforms to combat a new wave of AI-enabled runtime attacks, driven by vulnerabilities that traditional security measures struggle to detect. The shift comes as attackers exploit weaknesses in AI agents operating in production environments, where breakout times are now measured in seconds, outpacing the ability of conventional security systems to respond effectively.

According to CrowdStrike's 2025 Global Threat Report, the speed at which attackers move from initial access to lateral movement within a network has dramatically decreased, with breakout times recorded as low as 51 seconds. This rapid exploitation often occurs before security teams can even generate an initial alert. The report also highlighted that 79% of detected attacks were malware-free, indicating a rise in adversaries employing hands-on keyboard techniques that circumvent traditional endpoint defenses.

The challenge for Chief Information Security Officers (CISOs) is no longer just preventing reverse engineering, but doing so within an extremely compressed timeframe. Mike Riemer, field CISO at Ivanti, noted the accelerating pace of weaponization following patch releases. "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 puts immense pressure on enterprises, many of which still rely on manual patching processes that can take weeks or months to complete.

Inference security platforms are designed to address these runtime vulnerabilities by providing visibility and control over AI agents in production. These platforms monitor the behavior of AI models, detecting anomalies and potential attacks in real-time. By analyzing the inferences made by AI models, these security solutions can identify malicious inputs, data poisoning attempts, and other forms of exploitation that traditional security tools often miss.

The rise of AI-enabled attacks represents a significant shift in the threat landscape. As AI becomes more integrated into enterprise operations, the potential attack surface expands, and traditional security measures become less effective. The deployment of inference security platforms is a response to this evolving threat, providing a more proactive and targeted approach to protecting AI assets. The industry anticipates further advancements in these platforms, including improved automation and integration with existing security infrastructure, as enterprises continue to grapple with the challenges of securing AI in production.