Ryan Windham, CEO, Forcepoint.
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I like to fly drones in my spare time. It’s a hobby I’ve enjoyed for years, and one that has unexpectedly shaped how I think about leadership and risk, particularly in conversations with CEOs and CISOs.
When a drone gains altitude, everything changes. From higher up, patterns emerge. You can see shifts forming before they’re obvious, and when the wind changes, you don’t cling to the original flight plan. You adapt or you lose control of the flight. The same clarity and the same urgency apply when you’re responsible for protecting sensitive information at scale.
The biggest shift in security right now isn’t a new threat actor or a new compliance mandate. It’s something more fundamental: AI has changed the behavior of data itself.
Data used to be predictable. It was created by people and lived and moved in known systems. That world no longer exists.
AI generates data continuously. It rewrites, summarizes and recombines information in real time. It spreads data across SaaS platforms, cloud services and AI-driven workflows instantly. AI has effectively animated data, bringing it to life and making it self-perpetuating. Once that paradigm shifted, the old ways of securing data stopped working.
Most traditional data security models rest on three core assumptions. AI breaks all of them.
Pre-assigned access works when data stays in known locations and changes slowly. AI-driven workflows don’t behave that way. Data now moves dynamically between systems, users and applications—often outside environments security teams explicitly approved.
Static classification assumes data sensitivity is fixed. AI continuously generates new versions, summaries and derivatives, many more sensitive than the original source. Identifying and managing that risk can no longer be a one-time exercise. Data discovery and classification must be continuous, increasingly powered by technologies like data security posture management (DSPM), which gives teams ongoing visibility into sensitive data risk across cloud, SaaS and AI-driven environments.
Traditional controls assume predictable behavior from users, devices and applications. AI introduces variability at machine speed. When data and risk change in real time, fixed rules will always lag behind reality.
When these assumptions break, the entire security model breaks with them. That's why so many CISOs say their stack can't keep up, not because they don't understand the problem, but because their tools were never designed for AI-driven, boundaryless data.
In response, many organizations have doubled down on visibility. They're investing in tools that help them find data and understand where risk exists. That's the right instinct. But visibility without action quickly becomes noise, especially when data loss prevention (DLP) enforcement—tools that automatically block unauthorized data transfers across channels like email, cloud apps and endpoints—still depends on manual review or delayed response. When insight is disconnected from enforcement, security teams see risk but cannot respond fast enough to stop it.
This gap between spotting risk and acting on it, which I think of as the visibility-to-control gap, is often the difference between business continuity and a breach, a regulatory fine or reputational damage.
AI doesn't just increase data volume. It compresses the time between creation, exposure and impact. Whether generative or agentic, AI tools have shortened decision timelines. Data now moves faster than humans can review, approve or intervene. In an environment operating at machine speeds, monitoring risk is not the same as mitigating it. That’s why the security industry doesn’t just need better tools. It needs a different model. Increasingly, leaders are describing this as self-aware data security: an operating model in which security is context-aware, adaptive and continuous, connecting awareness of risk directly to enforcement, without waiting on human intervention.
There’s a moment in every industry where the future becomes obvious—but only to those looking from the right altitude. Right now, everyone feels the turbulence: the hype cycles, the complexity, the AI chaos, the pressure to move faster. But from higher up, it’s clear where this is going. Data is dynamic now. AI is creating and transforming more information than people ever could. Static rules and delayed responses can’t catch up.
In my next article, I’ll explore what the self-aware data security model looks like in practice—and how leading organizations are closing the gap between visibility and control.
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