The CEO demonstrates a new productivity tool during an all-hands meeting. Engineering has already integrated a large language model into production systems to accelerate deployment. Marketing is using third-party AI tools to generate customer insights from raw data.
In many organizations, the CISO is not part of these decisions. They often learn about them later through informal channels, long after the tools are already embedded into workflows.
This is becoming the default operating model for AI adoption.
AI is often not being introduced through structured programs with clear ownership and governance. It is entering through business functions, embedded into everyday tools and enabled by default features in platforms already approved at the enterprise level. As a result, adoption is outpacing governance in a way traditional security models were never designed to handle.
An AI Cold War is emerging
What is emerging is an internal Cold War. On one side is AI velocity, driven by pressure to improve productivity, reduce cost and accelerate delivery. On the other is security integrity, which depends on visibility, control and accountability.
When these forces evolve independently, Shadow AI becomes unavoidable.
Unlike traditional Shadow IT, the issue is no longer limited to unmanaged tools. It extends to unmanaged influence. AI systems do not simply execute tasks. They generate outputs that shape decisions, guide actions and increasingly influence outcomes across engineering, customer operations, finance and risk functions.
This shifts the risk from infrastructure to decision-making itself.
In practice, organizations are not only losing visibility into where AI is used, but also how it is shaping decisions that carry operational and business impact. Once this occurs, traditional control frameworks such as asset inventories, approval gates and periodic audits become insufficient on their own.
The challenge is not just technical. It is that decision-making is now distributed across tools, teams and vendors without a matching governance model.
From control to orchestration: why becoming the ‘Department of How’ matters
Most security functions were designed as control points. Their role has traditionally been to approve, restrict or block. That model worked in environments where systems were centrally deployed and change was slow and visible.
AI breaks that assumption.
Today, AI capabilities are often embedded by default in enterprise platforms and continuously updated by vendors. In this environment, blocking adoption at the perimeter is no longer effective. It often leads to silent adoption without oversight.
This is why security leadership must evolve from the “Department of No” to the “Department of How.”
This is not a reduction of control. It is a repositioning of control where it actually works. In practical terms, this shift is important for three reasons.
First, it restores visibility. When security teams are involved early in AI adoption, they can identify where models are introduced, what data is exposed and how outputs influence decisions. Without this, risk becomes distributed but untraceable.
Second, it reduces shadow behavior. When governance is perceived as restrictive, teams bypass it. When it is enabling, use cases are more likely to be declared, assessed and managed.
Third, it allows risk to be shaped rather than simply reacted to. AI adoption is not slowing down. The only variable is whether it is structured or uncontrolled.
Becoming the “Department of How” also means recognizing that security cannot be the sole owner of AI risk. Ownership must extend into the business functions deploying AI. If marketing uses AI to generate customer profiles, it must own the associated risk. If engineering embeds an LLM into production systems, it must own operational impact. Security defines guardrails but accountability must be distributed.
This shift is reinforced by regulatory direction. Frameworks such as the EU AI Act require traceability, risk classification and demonstrable oversight of AI systems. These requirements cannot be met through periodic audit cycles alone. They require continuous governance embedded into operational workflows.
Ultimately, Shadow AI is not an emerging risk. It is already present in most enterprises, whether visible or not. The question is no longer whether AI is being used but whether its use is understood, governed and accountable.
Organizations that move early to the “Department of How” model will not only reduce risk exposure; they will also gain the visibility required to scale AI safely.
Those that do not will continue to make critical decisions inside systems they cannot fully see or govern.
Q&A Recap
What does Shadow AI refer to?
Shadow AI refers to the unregulated integration and influence of AI systems on decision-making processes within organizations, which can bypass traditional security models.
What are the primary forces driving the internal “Cold War” mentioned in the blog?
The internal Cold War is driven by the rapid implementation of AI for productivity and cost reduction versus the need for security and governance controls.
How does traditional security struggle with AI adoption?
Traditional security models struggle because they focus on infrastructure control, which is insufficient for AI systems that influence decision-making.
Why should security teams evolve from “Department of No” to “Department of How”?
Security teams should evolve to better enable and orchestrate AI adoption, ensuring early involvement for visibility, reducing shadow behavior and shaping risk rather than simply reacting to it.
How should ownership of AI risk be handled?
AI risk ownership should be distributed among business functions using AI, with security teams defining guardrails while operational accountability lies within specific departments.
