PII detection

AI Security for Apps (formerly Firewall for AI) can detect personally identifiable information (PII) in incoming LLM prompts. There are two approaches to PII detection, and you can use them together for layered protection:

• Fuzzy detection (AI-powered) AI Security for Apps uses an AI model to identify common PII types in the prompt content. This approach catches PII even when it appears in natural language or unexpected formats.
• Exact detection (regex) You write a WAF custom rule with a regular expression on the raw request body. This approach is ideal for organization-specific identifiers with a known, predictable format.

When AI Security for Apps is enabled and a request arrives at a cf-llm labeled endpoint, it scans the prompt for PII and populates two fields:

• LLM PII detected (cf.llm.prompt.pii_detected) true if any PII was found.
• LLM PII categories (cf.llm.prompt.pii_categories) An array of the specific PII types found.

The detection is based on Presidio , a data protection and de-identification SDK. Refer to the cf.llm.prompt.pii_categories field reference for the full list of recognized categories.

Supported PII categories

The cf.llm.prompt.pii_detected field returns true when any PII category is detected including broad categories like PERSON, DATE_TIME, and LOCATION that frequently appear in normal conversation. Blocking based on this field alone will produce a high false-positive rate for most applications.

Instead, build rules against cf.llm.prompt.pii_categories and list only the categories that matter for your use case. For example, a customer support chatbot may need to block credit card numbers and SSNs but can safely ignore person names and dates. Start with the narrowest set of categories, monitor matches in Security Analytics, and expand only as needed.

• When incoming requests match:

  Field	Operator	Value
  LLM PII Detected	equals	True

  Expression when using the editor:
  (cf.llm.prompt.pii_detected)

• Action: Block

• When incoming requests match:

  Field	Operator	Value
  LLM PII Categories	is in	Credit Card

  Expression when using the editor:
  (any(cf.llm.prompt.pii_categories[*] in {"CREDIT_CARD"}))

• Action: Block

Create two custom rules:

1. A rule with action Block and the following expression:
   (any(cf.llm.prompt.pii_categories[*] in {"CREDIT_CARD" "US_SSN"}))

2. A rule with action Log and the following expression:
   (any(cf.llm.prompt.pii_categories[*] in {"EMAIL_ADDRESS"}))

If you need to detect custom PII formats specific to your organization such as internal employee IDs, patient record numbers, or proprietary account identifiers you can create a WAF custom rule using a regex match on the raw body (http.request.body.raw field).

This approach complements fuzzy detection by covering formats the AI model does not natively recognize.

In the following example, an organization uses employee IDs in the format EMP- followed by exactly six digits (for example, EMP-482910).

Create a custom rule with the following configuration:

• When incoming requests match:

  Field	Operator	Value
  Raw request body	matches regex	EMP-[0-9]{6}

  Expression when using the editor:
  (http.request.body.raw matches "EMP-[0-9]{6}")

• Action: Block

• With response type: Custom JSON

• Response body: { "error": "Request blocked: employee ID detected in prompt." }

Scope to a specific endpoint

To limit this rule to only your LLM endpoint, combine it with a path condition:

Expression when using the editor:
(http.request.uri.path eq "/api/chat" and http.request.body.raw matches "EMP-[0-9]{6}")

• Cloudflare Plan requirement. Regex operators (matches and ~) require a Business or Enterprise plan.
• Body size limit. The http.request.body.raw field inspects a limited portion of the request body. The exact limit varies by plan.
• JSON payloads. The raw body includes the full JSON structure. Your regex should account for the fact that the prompt text is nested inside a JSON string.
• Performance. Complex regex patterns can impact rule evaluation time. Keep patterns as specific as possible.

You can use fuzzy and exact detection together for layered protection:

(cf.llm.prompt.pii_detected or http.request.body.raw matches "EMP-[0-9]{6}")

This rule blocks requests where either the AI model detects any built-in PII category or the regex matches your custom identifier format.