How to Close The AI Security Gap

Few large enterprises need to be convinced that AI matters. The harder question is how to scale AI without introducing new forms of operational and security risk, and this is where many organizations are getting stuck. AI adoption is moving faster than governance, and that mismatch will likely become one of the defining security challenges of the next decade.

Many leadership teams still frame the issue too narrowly. They think about AI security mainly in terms of stopping employees from uploading sensitive information into public generative AI tools. That matters, but it is only one part of the picture. AI is now showing up across software as a service applications, developer environments, autonomous agents, model connections, prompts, and data flows. Last year we uncovered more than 3,400 AI and machine learning applications driving enterprise transactions, a fourfold increase over the previous year, and saw a 93% increase in AI data transfer volume. Enterprises are not just managing a handful of tools—they are trying to govern a fast-expanding AI estate.

The good news is that the problem is solvable, but not with isolated controls or a collection of disconnected point products. It requires a Zero Trust approach applied across the full AI lifecycle, with the visibility and control to discover where AI is being used, govern how it is accessed, and protect how it behaves. That is the shift enterprises now need to make.

The Issue Is Scaling Securely

Most organizations are not short on AI vision or ambition. Business units want productivity gains, technology teams want to accelerate development, and the C-suite wants AI to translate into competitive advantage. It’s a lack of confidence that AI can be deployed safely at enterprise scale that slows progress.

The challenge is not only technical but organizational. Many chief information officers, chief information security officers, chief technology officers, and chief AI officers understand that the risk extends far beyond public chat interfaces, but they are still having to explain that reality internally. Not every key decision maker yet sees the same picture or recognizes how much visibility, policy, expertise, and operational discipline will be needed to secure this shift.

If leaders underestimate the scale of the problem, they will underinvest in the human and technical resources required to respond. The evidence suggests many already have. In a series of AI security assessments recently conducted across 38 large enterprises, Zscaler found that not a single organization had fully secured its AI tools from outside attack. Across all 38, corporate AI systems were publicly reachable without adequate access controls.

This tells us the answer is not just another tool layered onto an already fragmented environment—point solutions may address single issues, but they create handoff problems, policy gaps, and blind spots between teams. That is exactly the wrong model for a technology shift moving this quickly and becoming so strategically important.

What Zero Trust Looks Like For AI

At Zscaler, our view has long been that the safest way to secure users, applications, workloads, and devices is to make Zero Trust the foundation of the security model. AI does not change that; it requires Zero Trust to extend to a wider and more dynamic set of interactions.

In an AI context, that means not inherently trusting any user, application, prompt, connector, or agent action. It means establishing visibility first, then enforcing policy with context, and reducing the blast radius when something goes wrong.

This is where a platform approach matters. The most practical model is built around three core capabilities.

First, enterprises need to discover their full AI footprint. That includes sanctioned and unsanctioned applications, embedded AI features inside approved software, models, agents, and the data paths that connect them. Zscaler addresses this through AI Asset Management, which gives organizations a clearer picture of what is actually running across the environment.

Second, they need to control access to AI in a precise way. That means deciding who can use which tools, under what conditions, with what data, and from which devices. Zscaler delivers this through AI Access Security, applying Zero Trust policy to AI applications and services instead of relying on broad, static allowances.

Third, they need to protect AI systems through build and runtime. That includes testing for weaknesses, applying guardrails, and reducing the risk of harmful or unintended behavior in production. Zscaler does this through AI Red Teaming and AI Guardrails.

Why Platform Matters Now

This full-lifecycle approach is the real platform advantage. Most vendors address one slice of the AI security problem, but enterprises don’t experience these issues one slice at a time. They face them all at once: shadow AI, embedded assistants, prompt exposure, data leakage, agentic behavior, and governance pressure from the chief executive and board.

The leadership team and board are not looking for fixes to discrete AI risks. They need a security model that lets the business adopt AI with confidence. The answer starts with Zero Trust and scales with a platform that can discover, control, and protect across the entire AI lifecycle. Enterprises that make that shift will be in a far better position to move quickly without losing control.

AI adoption is inevitable. Insecure AI is optional.

Learn more about Zscaler’s approach to Security for AI and download our ebook: The AI Security Gap: A Zero Trust Implementation Guide for Security Teams