Securing Generative AI Adoption with SASE: Best Practices for Visibility, Risk Management, and Data Protection

Best Practices for Securing Generative AI with SASE

As generative AI tools become common in organizations, security and IT leaders face the challenge of adopting them quickly while keeping systems safe. Executives push for fast integration to stay competitive, but IT teams are often working on security strategies without full clarity on how AI will be used. This situation demands careful attention because generative AI introduces new security risks that need managing.

Employees use a growing range of AI capabilities—both approved and unapproved. AI agents may handle sensitive credentials and interact autonomously with internal resources. Meanwhile, sensitive data might be shared with AI tools even as existing compliance frameworks struggle to keep pace. Despite these challenges, managing AI use internally is achievable with the right approach.

SASE (Secure Access Service Edge) is a cloud-based network architecture that combines networking and security into a unified service. It securely connects employees to the internet and corporate resources, regardless of location. Extending SASE to address AI risks allows organizations to protect AI usage without slowing innovation.

Cloudflare’s SASE platform stands out because it operates across cybersecurity and AI infrastructure. From providing AI tools for developers to securing public-facing AI models and controlling AI crawlers' access to content, this broad expertise offers a unique perspective on AI governance. The platform lets organizations integrate various components to build a comprehensive AI and cybersecurity infrastructure.

Recently, Cloudflare introduced new AI Security Posture Management (AI-SPM) features, such as shadow AI reporting, AI provider confidence scoring, AI prompt protection, API CASB integrations, and tools for securing Model Context Protocol (MCP) deployments. These capabilities are built into the SASE platform, making it a single hub for managing AI security effectively.

What’s in this AI Security Guide?

This guide presents best practices for adopting generative AI securely using Cloudflare’s SASE platform. It starts with how to create an AI Security Strategy, then explains how to use both existing SASE features and the new AI-SPM tools. The guide focuses on three pillars: Visibility, Risk Management, and Data Protection for human users, followed by insights on deploying agentic AI with MCP. The goal is to help you align security with business objectives while enabling AI adoption—all managed through one platform and dashboard.

Develop Your AI Security Strategy

Begin by defining your organization's risk tolerance for AI use. Identify your main security concerns, relevant compliance rules, and data protection needs.

Consider these key questions:

• Which sensitive data must never be shared with certain AI tools? (e.g., PII, PHI, financial records, credentials, source code)
• Are there decisions employees should avoid making with AI assistance? (For example, some regulations restrict AI use in personnel classification.)
• Are you required to keep audit trails of AI tool usage, including prompts? (Healthcare, GDPR, and SOC2 have such rules.)
• Do you mandate use of enterprise-grade AI tools with stronger data protection versus consumer versions?
• Are certain AI tools banned due to reliability, review status, or geographic concerns?
• What security features do your sanctioned AI providers offer, and how will you prevent misconfigurations?
• What is your policy on autonomous AI agents?
• What’s your approach to adopting Model Context Protocol (MCP), which supports autonomous AI workflows?

Every organization’s requirements differ. Some embrace various AI tools broadly, others limit usage strictly. Some are ready for agentic AI; others are cautious. Cloudflare’s SASE platform is designed to flexibly support your specific AI Security Strategy.

Build a Solid Foundation for AI Security

Implementing AI security starts with a solid SASE deployment. SASE replaces fragmented security tools with a unified platform that controls application visibility, user authentication, Data Loss Prevention (DLP), and access policies for both internet and internal resources.

SASE helps you discover which AI tools employees use, enabling you to manage risks and support compliance by monitoring AI prompts and responses. DLP scans for sensitive data to prevent leaks, and Secure Web Gateway (SWG) can redirect traffic from unauthorized AI tools to approved ones or user education pages. Integration with MCP tooling helps secure agentic AI deployments. If you’re new to SASE, starting with a Secure Internet Traffic Deployment Guide is useful, but here we focus specifically on securing generative AI.

Gain Visibility into Your AI Landscape

Visibility is essential—you can’t protect what you can’t see. Understanding which AI tools employees use—both approved and shadow AI—is the first step.

Discover Shadow AI

Shadow AI refers to AI applications used without IT approval. It’s common: surveys show many employees use unsanctioned AI tools just to get their work done. Your goal is to identify Shadow AI and apply appropriate policies.

Inline Discovery with Secure Web Gateway

Cloudflare’s SWG lets you see which AI and chat applications employees use. Deploying the WARP client in proxy mode on devices enables detailed insights. Dashboards show application usage trends and allow you to mark apps as approved or unapproved, feeding into access policies. Soon, Application Confidence Scores will automate SaaS and AI app assessment to help you manage risk at scale.

Out-of-Band Discovery with CASB Integrations

If device clients aren’t feasible, Cloudflare’s Cloud Access Security Broker (CASB) integrates with Google Workspace, Microsoft 365, or GitHub to provide insight into AI app usage. Integrated with your SSO, it reveals which AI tools users authenticate to, giving you a non-invasive view of app adoption.

Implement an AI Risk Management Framework

With visibility in place, focus on managing AI-related risks. Cloudflare’s SASE platform enables prompt monitoring, granular policy enforcement, user coaching, and misconfiguration detection.

Detect and Monitor AI Prompts and Responses

Enabling TLS decryption allows you to inspect AI interactions fully. The AI prompt protection feature captures exact prompts and responses, showing what data employees share with AI. It integrates with DLP to detect sensitive data and supports blocking or monitoring based on your policies.

Build Granular AI Security Policies

Use Gateway to create fine-tuned policies based on app categories, approval status, user groups, and device posture. For example:

• Allow approved AI apps while blocking unapproved ones.
• Redirect users from unapproved to approved AI tools.
• Restrict high-risk apps to select users or secure devices.
• Enable prompt capture for sensitive groups like contractors without impacting others.
• Apply Remote Browser Isolation (RBI) to prevent data uploads or pasting into risky AI apps.

Control Access to Internal Large Language Models (LLMs)

Cloudflare Access lets you gate employee access to proprietary LLMs or models running on Workers AI. Policies can restrict access by identity, group, device security, and other factors, ensuring only authorized users interact with sensitive AI resources.

Manage Security Posture of Third-Party AI Providers

API CASB integrations with AI services like OpenAI, Anthropic, and Google Gemini offer visibility into usage and configuration. You can identify misconfigurations, enforce API key best practices, monitor data loss risks, and flag risky AI features like autonomous browsing or code execution.

Layer on Data Protection

Protecting data is essential when employees interact with AI.

Prevent Data Loss

Cloudflare’s DLP scans user interactions with AI tools to detect and block sensitive information such as social security numbers, phone numbers, or addresses. AI prompt protection classifies prompts into categories like PII, credentials, source code, and malicious code, letting you enforce policies that fit the context. For example, blocking non-HR staff from requesting PII but allowing HR access during compensation planning. TLS decryption is required for the most advanced DLP features.

Secure MCP and Agentic AI

The Model Context Protocol (MCP) enables AI agents to interact with APIs and datasets autonomously. MCP servers are critical security points because they mediate these interactions.

Control MCP Authorization

MCP servers use OAuth, inheriting user permissions. Over time, this can lead to excessive permissions (authorization sprawl), making agents attractive attack targets. Cloudflare Access applies Zero Trust principles to continuously verify every request, reducing risk.

Centralize MCP Server Management

The new MCP Server Portal centralizes management of MCP servers, providing users a unified endpoint and administrators centralized controls. This reduces risks from unmanaged connections, prompt injection, tool injection, supply chain attacks, and data leakage. Administrators approve MCP servers before users access them, enforcing least-privilege principles.

Implement Your AI Security Strategy in a Single Platform

Cloudflare’s SASE platform offers a complete set of tools to protect AI usage while supporting productivity. Secure Web Gateway provides inline controls and visibility; CASB offers out-of-band oversight; Cloudflare Access enforces Zero Trust for internal LLMs; and MCP security controls safeguard agentic AI. All features integrate into one dashboard, simplifying management and giving you a clear view of your AI security posture.

For leaders aiming to balance AI adoption with security and compliance, using a unified platform like this reduces complexity and accelerates safe AI integration across your organization.