ChatGPT, Copilot, and other AI tools have become part of everyday work. Employees use them to create content, review code, summarize information, and complete complex tasks more efficiently. For organizations, this represents a significant productivity gain. At the same time, a new security challenge emerges, often becoming visible only after sensitive data has already been transferred to external AI systems.
Today, very few organizations can confidently answer which AI tools are being used, what information is being entered into prompts, or whether confidential content is being processed further in AI-generated responses. This is exactly where Shadow AI becomes a challenge: AI usage is happening every day, often outside established IT approvals, security controls and governance processes.
This article explains why traditional security approaches struggle to address generative AI risks, the role SASE plays in modern AI security strategies, and how Cato AI Security helps organizations adopt AI securely, with greater visibility, control, and compliance.
Shadow AI and Data Leakage: The biggest risks of generative AI
Organizations use tools like ChatGPT every day. The challenge begins when nobody knows what data is being entered into these systems or what happens to that information afterwards.
In practice, employees often enter more information into AI tools than they realize. This may include source code, contract extracts, customer data, internal strategy documents, proposal details, or technical documentation. This rarely happens out of negligence. More often, employees simply lack a secure alternative, a clear policy, or technical controls that distinguish acceptable use from risky data exposure.
The result is a loss of control on several levels:
- Security teams cannot reliably see which AI services are being used.
- Compliance teams cannot verify which regulated or personal data has been processed.
- Business units maintain productivity but may operate outside approved processes.
- Intellectual property can unintentionally be exposed to systems that were never intended to process confidential corporate information.
For this reason, simply banning AI is not an effective solution. Restrictions often drive AI usage further into the shadows. Organizations need an approach that enables productivity while keeping risks manageable.
Why are traditional security solutions not enough for AI applications?
Traditional security solutions were designed for a very different type of traffic. They focus on applications, files, user behavior, or known threat patterns. Generative AI works differently. It operates through semantic and dynamic interactions, including prompts, responses, contextual information, agent actions and automated workflows.
A conventional DLP solution might detect sensitive information inside a file upload. The challenge becomes far greater when an employee copies confidential information into a prompt, rewrites that information, or combines it with additional context. The complexity increases even further with private AI applications and autonomous AI agents that independently retrieve information, support decisions, or execute actions.
New attack vectors add further complexity. Prompt injection can manipulate AI systems into executing unintended instructions. Model poisoning can compromise training or contextual data. Uncontrolled agent workflows can trigger processes before security teams even realize what has happened. At the same time, organizations face growing regulatory requirements driven by GDPR, the EU AI Act, and internal governance frameworks. Companies must not only prevent data leakage but also document how AI systems are being used.
As a result, isolated tools, manual approval processes and standalone DLP controls are no longer sufficient. Organizations need visibility, context and policy enforcement directly where AI traffic is created and processed.
How SASE and Cato AI Security protect AI usage in real time
SASE (Secure Access Service Edge) combines networking and security functions within a cloud-native architecture. Instead of tying security to individual locations, appliances, or disconnected tools, access, traffic and policies are managed centrally. This becomes increasingly important as employees work from anywhere, rely on cloud services and use AI applications beyond traditional corporate environments. Bringing networking and security capabilities together in a single platform provides the visibility and control required to secure modern cloud and AI workloads consistently.
Cato Networks extends this approach with Cato AI Security. The solution is natively integrated into the Cato SASE platform and addresses the gap between AI usage, data protection and governance. By integrating Aim Security technology and Cato Neural Edge with NVIDIA GPUs across its global backbone, AI traffic can be inspected inline and in real time.
5 essential capabilities for securing and governing generative AI
- Identify AI Usage
The platform discovers which generative AI tools, private models and AI agents are being used, making Shadow AI visible. - Analyze prompts and responses semantically
Instead of relying only on static patterns, the solution evaluates context and identifies whether sensitive data, intellectual property, or regulated information may be involved. - Enforce policies centrally
Security policies are applied within the existing SASE framework, considering users, devices, applications, locations, data classifications and risk levels. - Protect private AI applications and agents
Internal AI models and agent-based workflows can also be monitored and controlled, extending protection beyond public AI services. - Support compliance and AI Security Posture Management
Security teams gain better capabilities for risk assessment, reporting and regulatory compliance management.
The key point is: AI security is not introduced as another standalone tool. It becomes part of a unified security architecture.
The key benefits of an integrated AI Security platform
Greater visibility into Shadow AI
Organizations can only protect what they can see. Cato AI Security provides visibility into AI usage, data flows, and risky interactions. For CISOs and security engineers, uncertainty is replaced with actionable intelligence. This reduces blind spots and supports more effective governance. Not every AI interaction is risky: the critical capability is distinguishing acceptable usage from problematic behavior.
Protection of sensitive data and intellectual property
Prompts may contain confidential information even when no files are uploaded. This is what makes AI-related data leakage difficult to identify. Through semantic inline inspection, sensitive content can be understood in context before it reaches external services.
This helps protect customer information, source code, contracts, business strategies, and other forms of intellectual property. At the same time, productive AI usage remains possible because organizations do not need to rely on blanket restrictions.
Compliance without sacrificing productivity
Privacy regulations, GDPR, the EU AI Act, and internal policies require transparency and accountability. Organizations need to understand which AI applications are being used, what data is processed and which mitigation measures are in place.
With centralized policy management, transparency and AI Security Posture Management, compliance requirements become easier to operationalize. As a result, organizations are better positioned to adopt AI securely and in a structured manner, identify risks early, and maintain long-term governance.
Reduced complexity for security operations
When AI security is deployed as a separate solution, it usually adds another dashboard, another alert channel and another integration challenge. A SASE-integrated approach reduces this complexity because governance, monitoring, and detection operate within a unified platform.
Security Operations Centers benefit from clearer policies, reduced context switching and improved event prioritization, helping lower false positives and improve operational efficiency.
A scalable foundation for future AI Adoption
AI continues to evolve rapidly. Today, organizations focus on chatbots and copilots; tomorrow, they will increasingly rely on autonomous agents, internal models and AI-supported business processes. Security architectures therefore need to scale with new use cases instead of being redesigned from scratch every time. SASE provides that foundation by applying security consistently across users, traffic, cloud services and policies rather than tying controls to specific locations or applications.
Common AI Security scenarios and how organizations mitigate risk
Example 1: Developers using AI for Code Analysis
A developer wants to identify a software issue more quickly and copies internal source code into a generative AI tool. Without control, intellectual property could be exposed. AI Security operating within a SASE context can recognize sensitive code elements in the prompt. Depending on policy settings, the input can be blocked, sanitized, or flagged for review.
Example 2: Legal teams working with contract excerpts
A business team wants to summarize contractual language using AI. Personal data, pricing information, customer names, or confidential clauses may be included in prompts. Semantic inspection can detect risky content and prevent it from being transferred to unapproved AI services.
Example 3: Business teams testing their own AI Agents
A department develops an internal AI agent that connects information from multiple systems. Without appropriate controls, the agent may access excessive amounts of information or execute unintended actions. Runtime protection helps monitor agent interactions and enforce policies during execution.
The most important steps for successfully implementing AI Security
Organizations should not begin with restrictions. The first step is understanding which AI tools are already being used, what data is affected, and where the greatest risks already exist.
A structured implementation approach typically includes four steps:
- Assess current AI usage
Identify which public AI tools, copilots, private models and agents are already in use and where. - Classify risks and data
Define which information can be processed in AI systems and which categories require special protection. - Design Policies within the SASE framework
Policies should consider user roles, devices, locations, data types and risk levels rather than focusing solely on applications. - Monitor and adapt continuously
AI usage changes constantly. Policies must evolve alongside new business requirements and use cases.
Balance is critical. Policies that are too relaxed create risk. Policies that are too restrictive encourage workarounds. The most effective approach provides employees with safe ways to use AI while giving security teams the visibility and control they need.
Checklist: What should a modern AI Security solution include?
A strong AI security solution should do more than simply discover AI tools or block access. The following capabilities are particularly important:
- Visibility across public and private AI usage
The solution should be able to identify Shadow AI, approved AI services, private models and AI agent interactions. - Semantic analysis instead of pure pattern matching
AI-related risks often depend on context. Traditional DLP alone is therefore not enough. - Real-Time Inline controls
Risks should be identified before data is exposed or risky actions are executed. - Centralized policy enforcement
Policies must be applied consistently across users, locations, applications and data flows. - Integration into existing security architecture
The solution should reduce the workload for Security Operations rather than creating additional silos. - Compliance capabilities
Transparency, reporting and governance are essential for demonstrating compliance with GDPR, the EU AI Act and internal policies. - Performance and scalability
AI security must not slow down productive AI usage. Semantic inspection in particular requires an architecture that supports real-time processing and global scalability.
How does SITS support organizations with AI Security and SASE?
SITS helps organizations translate SASE, AI Security and governance into a sustainable security architecture. This includes consulting, architecture reviews, implementation, integration into existing security processes, and, if required, ongoing operations and support for Cato-based SASE environments.
That creates an approach that brings together technical controls, compliance requirements and productive AI adoption. Not as another isolated security initiative, but as part of a modern cybersecurity strategy.
Use AI securely instead of blocking it: Why AI Security is becoming essential
Generative AI is no longer an experimental technology. It is becoming part of the operational infrastructure of modern organizations. That is why it is not enough to allow AI usage based on intuition, block it entirely, or mention it briefly in a policy document.
The real challenge lies in maintaining control: Which AI tools are being used? Where does data go? What risks arise from prompts, responses, private models, and AI agents? And how can security measures be implemented without eliminating the productivity benefits that AI provides?
SASE and Cato AI Security provide an answer. Together, they combine visibility, semantic inline inspection, centralized policy enforcement and runtime protection in a scalable platform. For IT security teams, that means less loss of control, stronger compliance and a better foundation for using AI securely across the organization
Would you like to understand how secure your current AI usage really is?
Our experts can help you uncover Shadow AI, assess risks and build a secure SASE and AI security strategy.
FAQ
SASE stands for Secure Access Service Edge. The approach combines networking and security capabilities such as SD-WAN, Secure Web Gateway, Cloud Access Security Broker, Firewall as a Service, and Zero Trust Network Access within a cloud-native architecture.
AI applications are commonly used through cloud and SaaS services. SASE enables organizations to centrally manage traffic, users, applications, and policies, making AI usage easier to monitor, control, and secure.
Shadow AI refers to the use of AI tools outside approved IT and security processes. This can lead to data leakage, compliance risks and a lack of visibility.
Traditional DLP solutions typically rely on files, patterns, and predefined rules. AI interactions consist of dynamic prompts, responses, and contextual information. As a result, organizations need semantic analysis and real-time controls.
No. Blanket bans rarely solve the underlying problem. A more effective approach is to combine clear policies, technical safeguards, and secure ways for employees to use AI productively.
