AI Security with Cloudflare

Shadow AI is the use of unapproved AI tools by employees without IT or security oversight. The most common example is pasting confidential data (source code, contracts, customer records) into public LLMs like ChatGPT or Claude. Gartner estimates that 73% of employees use AI tools that have not been vetted or approved by their organisation. The risk is data leakage, compliance violations, and complete lack of audit trail.

Traditional API gateways manage routing and authentication. Cloudflare AI Gateway is purpose-built for LLM traffic: it logs every prompt and response, applies real-time DLP to detect sensitive data, enforces rate limits to control costs, caches identical responses to cut cost and latency, and provides content guardrails. It is a universal proxy compatible with 50+ LLM providers and requires minimal code changes to deploy.

Yes. AI Security for Apps scores every incoming request for prompt injection risk. It detects direct injections (jailbreak attempts), indirect injections (hidden instructions in external data), and adversarial prompts designed to manipulate model behaviour. Requests exceeding the risk threshold are blocked before they reach the LLM.

Cloudflare inspects the prompts and responses flowing through your AI applications in real time, flagging prompt injection, sensitive-data (PII) exposure and unsafe or jailbreak content before they reach your models or your users. At the network layer, AI-driven detection identifies and mitigates automated traffic and bots targeting your AI endpoints, with automated response rules. AI Gateway gives you full visibility and logging across every LLM call. Brixio designs, deploys and operates this detection and response configuration for your environment.

No. The recommended approach is not to block AI entirely but to govern it. Cloudflare Zero Trust Gateway allows you to block unsanctioned AI tools while redirecting employees to approved, governed AI environments. This preserves productivity while ensuring data protection and compliance.

AI Gateway logs the model, the AI provider, the timestamp, the request status, the prompt content, the response content, token usage, cost and duration. It does not log an identity on its own: attribution to a user, a team or an application comes from custom metadata attached to the call. All logs can be exported to your SIEM. Where the inference itself happens is governed by the provider and the model you route to, backed by contract: a gateway decides which provider receives a call and proves it in the log, it does not move the inference.

Brixio follows a phased approach: (1) AI usage audit to map current AI tool usage and data flows, (2) architecture design with policy definitions for Shadow AI control, AI Gateway configuration, and application protection, (3) deployment and testing, (4) documentation for compliance, (5) optional ongoing operations via managed services. Every configuration decision is documented for audit readiness.

Yes. AI Gateway supports both third-party LLM providers (OpenAI, Anthropic, Google, Mistral) and self-hosted models deployed on your infrastructure or in Cloudflare Workers AI. The same logging, DLP, and governance controls apply regardless of where the model runs.

AI security posture management is the inventory half of AI security: which models you run, which inference endpoints sit in front of them, who is allowed to call those endpoints, and where the training data lives. This page is the umbrella over the subject. The operational playbook, with the six Cloudflare data sources an inventory draws on and the four things it deliberately leaves out, is on AI security posture management.

An agent authenticates on its own and writes to systems without human review, so the control that bounds it is identity and scope rather than content filtering. That means one service identity per agent, a scope restricted to the destinations it actually calls, every call logged with the identity attached, and a tested revocation path. The same applies to what your MCP servers expose, tool by tool. The operational playbook is on agentic AI security.