Check Point Software Launches Agentic Exposure Validation to Counter Frontier AI Models Now Capable of Autonomous Exploitation
Check Point Exposure Management introduces new AI agents that reason like attackers – proving what is actually exploitable and giving security teams the evidence to act before adversaries do 
Posted: Friday, May 29
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Check Point Software Launches Agentic Exposure Validation to Counter Frontier AI Models Now Capable of Autonomous Exploitation

REDWOOD CITY, CA. – May 28, 2026 Check Point® Software Technologies Ltd. (NASDAQ: CHKP), a pioneer and global leader of cyber security solutions, today launched Agentic Exposure Validation (AEV) for Exposure Management, to put defenders on equal footing with AI-driven attackers. As frontier AI models like Anthropic’s Mythos and OpenAI’s GPT-5.5 gain the ability to autonomously find thousands of exploitable vulnerabilities at scale, the question for boards and CISOs is no longer “are we patched?” but “what can attackers actually exploit right now? and how do we find it before they do?” AEV is the answer.

“The era of autonomous, AI-driven exploitation is here. Frontier AI models are attacking critical vulnerabilities at scale, without human steering,” said Yochai Corem, General Manager of Exposure Management at Check Point. “Security teams are already inundated and cannot effectively address that emerging threat. Agentic Exposure Validation is our answer: AI agents that reason like attackers reviewing your organisation digital surface from the outside with our unique threat intelligence context and prove what is actually exploitable and provides security teams the evidence and the remediation to act smartly and effectively before attackers do.”

Agentic Exposure Validation (AEV) uses AI agents that reason like attackers across the organisation’s specific environment, correlating exposure data, asset context, live exploit research, threat intelligence, and protection coverage to determine whether an exposure is truly exploitable. Rather than relying on static severity scores, AEV follows a safe proving loop: it analyses the relevant asset or CVE, enriches findings with live Check Point threat intelligence, checks whether existing controls already block the path, and builds a targeted validation that mirrors attacker reasoning without disruptive techniques. It then either proves the exposure with direct evidence, pivots to a new attack path when blocked, or discards the threat altogether.

AEV is a critical validation capability within Continuous Threat Exposure Management (CTEM) programs, helping organisations move from discovery and prioritisation into confident, evidence-based exposure reduction at AI scale.

Early customer engagements have already demonstrated this pattern, and AEV was able to create novel exploit for dozens of vulnerabilities that had no known exploit.

Agentic Exposure Validation is available now as part of Check Point Exposure Management. To learn more or to request a complimentary AEV scan, organisations can complete the demo request form here to see what an agentic attacker would uncover on their external attack surface.

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About Check Point Software Technologies Ltd.

Check Point Software Technologies Ltd. (www.checkpoint.com) is a global cyber security leader protecting more than 100,000 organisations worldwide. Its mission is to secure enterprises’ AI transformation. With a prevention-first approach and an open ecosystem architecture, Check Point helps organisations block advanced threats, prioritise exposures, and automate security operations across complex digital environments. The unified architecture simplifies protection across hybrid networks, multi-cloud environments, digital workspaces, and AI systems. Structured around four strategic pillars, Hybrid Mesh Network Security, Workspace Security, Exposure Management, and AI Security, Check Point delivers consistent protection and visibility across multivendor environments, enabling organisations to reduce risk, improve efficiency, and accelerate innovation without increasing complexity.

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