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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:

AI-based detection — 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.

AI-based PII detection

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 powered by an AI-based Named Entity Recognition (NER) model. Refer to the cf.llm.prompt.pii_categories field reference for the full list of recognized categories.

Supported PII categories

Category	Description
`BANK_ACCOUNT`	Bank account number
`CREDIT_CARD`	Credit card number
`DATE_TIME`	Date or time expression
`DRIVER_LICENSE`	Driver license number
`EMAIL_ADDRESS`	Email address
`IP_ADDRESS`	IPv4 address
`LOCATION`	Physical location or address
`PASSPORT`	Passport number
`PERSON`	Full or partial name of an individual
`PHONE_NUMBER`	Phone number
`TAX_ID`	Tax identification number
`US_SSN`	US Social Security Number
`URL`	URL

Be specific to reduce false positives

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.

Example rules — AI-based detection

Block any request containing PII

When incoming requests match:

Field Operator Value

LLM PII Detected equals True

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

Field	Operator	Value
LLM PII Detected	equals	True

Block only specific PII categories

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

Field	Operator	Value
LLM PII Categories	is in	`Credit Card`

Log email addresses but block credit cards and SSNs

Create two custom rules:

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

Exact PII detection (regex)

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 AI-based detection by matching predefined patterns, including organization-specific identifiers.

Example: Detect employee IDs

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." }

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

Scope to a specific endpoint

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

Field	Operator	Value	Logic
URI Path	equals	`/api/chat`	And
Raw request body	matches regex	`EMP-[0-9]{6}`

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

More regex examples

Custom PII type	Example format	Regex pattern
Employee ID	`EMP-482910`	`EMP-[0-9]{6}`
Patient record number	`PAT/2024/00391`	`PAT/[0-9]{4}/[0-9]{5}`
Internal account ID	`ACCT-XX-99999`	`ACCT-[A-Z]{2}-[0-9]{5}`
Custom API key prefix	`sk_live_abc123...`	`sk_live_[a-zA-Z0-9]{20,}`

Considerations for regex rules

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.

Combine both approaches

You can use AI-based 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.