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Cato Networks announced generative AI controls for its Cloud Access Security Broker (CASB) on April 15, 2025. The launch focused on finding employees’ use of GenAI applications and applying policies to access and actions such as uploads and downloads. Cato’s later AI Security materials describe a broader service for public AI use, AI applications and agents; those capabilities should not be read back into the 2025 CASB announcement.
What Cato announced for CASB in April 2025
Cato’s April 15, 2025 announcement described GenAI security controls as part of Cato CASB, which the company said was a native feature of its SASE Cloud Platform. The announcement presented two central pieces: a shadow AI dashboard for visibility and a policy engine for controlling use.
Discovering shadow AI
The dashboard was described as a way to detect, analyze and understand GenAI application use, including distinguishing sanctioned from unsanctioned services and identifying and classifying applications. Cato reported a catalog of “950+ GenAI applications” at launch. That is Cato’s 2025 catalog figure, not an independently verified count or a statement of the catalog’s current size.
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Cato said administrators could control access to applications and specific actions, including uploads and downloads. The release also described limiting or preventing sensitive data from being uploaded to large language models in real time, with the aim of supporting corporate governance and compliance policies.
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These are vendor-described capabilities, not evidence that every route to an AI service is covered or that every data leak will be prevented. Cato’s current CASB product page describes a broader cloud-app control model: visibility into sanctioned and unsanctioned apps, application risk scoring, rules by app, user and context, and action-level controls. Its examples include allowing downloads while blocking uploads and restricting access to approved SaaS tenants. The page also describes assessing GenAI app risk and detecting sensitive-data violations in real time.
How the AI Security story broadened
Cato’s product positioning later expanded beyond employee use of public GenAI apps. Its AI Security solution page describes coverage for end users, AI applications and agents. Examples include discovering shadow AI and applying guardrails to prompts and responses; protecting AI applications and agents against prompt injection, data leakage and runtime attacks; and discovering MCP servers, profiling agent actions and tool calls, and maintaining an audit trail.
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The company’s AI Security page frames the scope in three areas: public GenAI governance, private AI models and agents, and AI security posture management. It describes monitoring and governing prompts and responses inline through APIs or a browser extension, as well as runtime protection for private AI applications.
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What the claims do—and do not—establish
Cato’s announcement quoted a Gartner forecast that “by 2027, more than 40% of AI-related data breaches will be caused by the improper use of generative AI (GenAI) across borders.” This is a forecast quoted by Cato, not an observed rate; Cato’s release identifies the underlying Gartner press release as dated February 17, 2025.
Cato vice president of product management Ofir Agasi said, “Enterprises need smart ways to govern GenAI,” describing the goal of discovering, classifying and securing enterprise GenAI use. The release also included a customer testimonial from CloudFactory’s head of security operations. Both statements are attributable to the vendor’s announcement, not independent product-performance evidence.
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The cited product materials describe features and positioning; they do not establish independent comparative performance, measured reductions in incidents, detection accuracy, or proof that the controls prevent all sensitive-data exposure. They also do not establish that a given customer has the required license, configuration or traffic path enabled.
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How to evaluate the controls for your environment
For an enterprise evaluating Cato or comparing products, test the controls against the organization’s actual applications, data flows and governance needs rather than relying on a general “AI security” label.
Map the coverage
- List the public AI services, AI-enabled SaaS, custom AI applications and autonomous agents employees use or may introduce.
- For each surface, establish whether the proposed controls provide visibility only, or can enforce a policy on the relevant traffic.
- Ask how approved and unapproved applications are identified and how application or tenant restrictions work in your deployment.
Verify enforcement and data protection
- Confirm which user actions can be allowed, blocked or restricted, including uploads, downloads and access to particular tenants.
- Ask what sensitive data is inspected, which channels and traffic paths are covered, and what actions a policy can take when a violation is detected.
- For prompt and response governance or custom application protection, request a demonstration of the actual inspection path and runtime controls relevant to your architecture.
Check governance and operations
- Review how administrators create and maintain policies, what user and application activity is visible, and what audit evidence is available.
- Map the controls to your own compliance obligations; product descriptions of governance features do not certify a customer deployment as compliant.
- Compare platform-native controls with your existing security architecture, including integrations, administrative workflow and operational overhead.
Cato said the CASB controls were generally available to customers globally when announced in 2025. That launch-time statement does not confirm current availability, entitlement, pricing, exact license prerequisites, geographic or traffic-path coverage, retention, audit details or efficacy for a specific customer. Ask Cato to map the desired controls to the precise subscription and deployment, demonstrate the relevant paths, and explain what is inspected and logged.
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