Thomas Kranz

expert panel

AI can create value quickly, but every new model, agent and AI-enabled workflow can also add another layer of complexity for security teams to understand and protect. When adoption—sanctioned or not—moves faster than visibility and governance, organizations can begin accumulating AI risk debt: unresolved exposures, unclear ownership and controls that may no longer match how the technology is actually being used. Recent research underscores that gap: Less than half of the security leaders surveyed said their companies knew all the AI agents operating on their networks, and less than half said they controlled those agents’ access to corporate data.The challenge is that this debt doesn’t necessarily show up as a single, obvious vulnerability. It can build quietly as AI systems become more capable, new uses emerge and dependencies multiply, leaving security programs trying to manage risk across a moving target. As AI agents evolve from tools that assist employees into systems capable of acting across enterprise environments, companies are confronting governance and security questions for which established practices are still developing.For security leaders, the question is where that growing gap between AI adoption and effective oversight poses the greatest danger—and how to close it before the debt becomes harder and more expensive to unwind. Members of the Senior Executive Cybersecurity Think Tank bring deep expertise in enterprise cybersecurity strategy, data breach prevention, risk management, regulatory compliance and modern security architecture. Below, they explore where AI risk debt is accumulating most dangerously and what security leaders can do now to bring it under control.

expert panel

Cybersecurity has long been a race between attackers trying to get in and defenders trying to keep them out. But frontier AI is changing the pace of that race. By helping threat actors discover vulnerabilities, conduct reconnaissance and experiment with attack techniques more quickly and at a greater scale, AI can shrink the time security teams have to recognize a threat and respond before it causes damage. Offensive activities that once took weeks can increasingly be compressed into minutes. That shift also challenges a more fundamental assumption behind traditional detection: that defenders will recognize enough of an attack to know what they’re looking for. Frontier AI can help adversaries vary techniques, combine attack methods and explore unfamiliar paths at a scale that makes relying primarily on established signatures, indicators and playbooks increasingly risky. Cybersecurity guidance is consequently evolving toward models that account for AI-enabled attacks while also using AI to strengthen defense and proactively address emerging threats.Preparing for that environment means looking beyond how quickly an organization can identify an attack to how much room for maneuver its systems leave an attacker in the first place. Members of the Senior Executive Cybersecurity Think Tank share deep expertise in enterprise cybersecurity strategies, data breach prevention, risk management, threat detection and modern security architecture. Below, they explore how security leaders should adapt their defenses for an AI-accelerated threat landscape and which capabilities are becoming most important.