Cloudflare AI Security for Apps

Cloudflare AI Security is a security solution designed to protect organizations as they deploy and scale Large Language Models (LLMs) and generative AI applications. The solution provides a unified defense layer that secures the entire AI lifecycle—from the underlying infrastructure and APIs to the application logic and end-user interactions—without compromising the performance or usability of AI-driven workflows.

The Challenge: The Expanding AI Attack Surface
As enterprises integrate LLMs into their business processes, they introduce significant new security risks that traditional security stacks are unequipped to handle, including:

• Prompt Injection: Malicious inputs designed to bypass safety filters or manipulate model outputs.
• Data Leakage: Accidental or intentional exposure of sensitive corporate data through model responses.
• Insecure Output Handling: Vulnerabilities where LLM-generated content triggers downstream system commands or scripts.
• Model Poisoning & Supply Chain Risks: Risks associated with the ingestion of untrusted training data or third-party model components.

Core Capabilities
Cloudflare AI Security leverages the global scale and intelligence of the Cloudflare edge to provide real-time protection across three critical vectors:

• Input/Output Inspection (Prompt & Response Guardrails):

  • Prompt Injection Detection: Analyises incoming user prompts in real-time to identify and block adversarial patterns intended to hijack model behavior.
  • Sensitive Data Detection (DLP): Scans model outputs for PII (Personally Identable Information), PHI (Protected Health Information), and company-specific secrets to prevent accidental data exfiltration.
  • Content Moderation: Enforces enterprise-grade safety policies by filtering toxic, offensive, or non-compliant content at the edge.

• API & Infrastructure Security:

  • API Protection: Extends existing Web Application Firewall (WAF) and API Shield capabilities to the specifically structured requests used in AI environments (e.R., JSON-based inference requests).
  • DDoS Mitigation: Protects AI endpoints from volumetric and application-layer attacks that could disrupt model availability.

• Visibility and Governance:

  • Centralized Observability: Provides deep visibility into AI usage patterns, identifying anomalous behaviors and potential security threats through unified logging and analytics.
  • Policy Enforcement: Allows security teams to deploy standardized security postures across all AI applications, regardless of where the model is hosted (SaaS, Cloud, or On-Prem).