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.
“If you don’t know where the data is going and who has access to it, effective response to any data breach becomes almost impossible.”
Get Clear on Data Governance
With more than 30 years of experience in cybersecurity, Thomas Kranz, Founder and CISO of Thomas Kranz Consulting, has seen organizations contend with successive waves of new technology and the security challenges that follow.
“The lack of data governance is where security debt starts and where the real danger lies,” he says. “Security teams aren’t aware of what data users are inputting into AI tools, how it is being used or where it is being processed. Apart from the obvious GDPR and regulatory implications, this makes breach response incredibly complex.”
For Kranz, those visibility gaps become particularly consequential when something goes wrong.
“If you don’t know where the data is going and who has access to it, effective response to any data breach becomes almost impossible,” he says. “Shadow AI use, where everyday reliance on an AI tool has been normalized, makes the problem even worse—especially with BYOD mobile devices that have access to both corporate data and external noncorporate AI tools.”
Kranz believes security leaders can draw on lessons organizations have already learned from another difficult-to-control technology trend.
“We’ve learned the lessons from shadow IT on how to address this,” he says. “Understand business needs, deliver and centrally manage corporate AI tools that address business needs, and put DLP and access management solutions in place to spot shadow AI usage.”
Bring Shadow AI Into View
Kumar Ritesh, Founder, Chairman and CEO of CYFIRMA, brings experience spanning national intelligence, cybersecurity research, consulting and enterprise security leadership. He sees risk growing alongside AI integrations that organizations may not fully know or understand.
“AI risk debt is accumulating fastest in shadow AI: employees plugging unsanctioned tools into workflows, agents granted broad data access without governance, and third-party models embedded in SaaS products nobody vetted,” Ritesh says. “Each integration quietly expands the attack surface and data exposure faster than security teams can inventory it.”
Waiting for conventional governance processes to catch up will allow those exposures to keep accumulating.
“Security leaders should act now,” Ritesh says. “Build a live AI asset inventory and visibility covering models, agents and data flows; enforce least-privilege access for agentic tools; require security review before AI integrations go live; and monitor data leaving the environment through AI systems.”
That work, he adds, can’t be treated as a project with a fixed endpoint.
“Treat AI governance as a continuous discipline, not a one-time audit, so debt doesn’t compound faster than visibility improves.”
Prepare Response Teams for AI Speed
Ken Grohe, President of LeverageGTM, Inc., has more than 35 years of experience across SaaS, IT and security. He focuses on what security leaders can do right now to reduce risk, beginning with establishing clear oversight.
“Create an executive-led council to map your AI census, define autonomous boundaries and approve agent deployment thresholds,” he says.
Grohe also emphasizes the importance of bringing key stakeholders together before an incident puts response protocols to the test.
“Poor internal coordination creates more disruption during a cyber crisis than the attack itself,” he says. “Security teams must align legal, compliance and executives on a unified command hub.”
Grohe also shares a technology solution organizations can leverage to help their response processes operate at the speed of emerging threats.
“Use governed AI orchestration layers to dynamically interpret intent and structure response steps,” he says. “This ensures that when fast-moving AI anomalies or breaches occur, your response operates at machine speed rather than waiting on human-slow administrative workflows.”
Preparation, Grohe stresses, also requires moving beyond obsolete exercises and processes that can’t keep pace with rapidly changing AI environments.
“Move away from static spreadsheets and outdated training models,” he says. “Continuously test cross-functional escalation pathways using live, threat-aligned simulations built for autonomous and rapid-fire incident scenarios.”
How to Start Paying Down AI Risk Debt
- Make AI data flows visible. Security teams need to know what information is entering AI tools, where it’s being processed and who can access it if they want to govern risk effectively.
- Give employees secure, sanctioned AI options. Centrally managed tools, supported by data loss prevention and access controls, can help reduce the appeal and risk of shadow AI.
- Maintain a live inventory of AI systems and integrations. Models, agents, third-party tools and connected data flows should be continuously tracked as the organization’s AI footprint changes.
- Apply least-privilege access and security review to AI deployments. Agentic tools should receive only the access they need, while new integrations should be evaluated before they move into production.
- Establish cross-functional AI oversight before problems emerge. Security, legal, compliance and executive leaders should agree on boundaries, escalation paths and decision-making authority before an incident tests them.
- Practice for incidents that move at AI speed. Replace static plans and outdated exercises with realistic simulations that test how teams coordinate and respond to fast-moving AI-related events.
Keep AI Risk From Compounding
AI risk debt grows when adoption outpaces an organization’s ability to see, govern and secure what’s being deployed. Reducing that debt starts with fundamentals: clearer data governance, better visibility into AI use, tighter access controls and stronger oversight of models, agents and integrations.
But this isn’t a cleanup effort with a finish line. As AI systems become more autonomous and more deeply embedded in business operations, security leaders will need to treat governance, monitoring and incident readiness as continuous disciplines. Organizations that build those habits now will be better positioned to keep AI risk from compounding faster than they can control it.
