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Lab 5: Data Leak Protection

LLM responses can expose sensitive data when the model processes documents with confidential information, or when a user pastes it into the chat. In this lab you use a Data Leak Protection (DLP) detector to block card data, credentials and personal data in both directions, and then to redact it so that the conversation can continue safely.

🎯 Lab Objectives

In this lab, you will:

  • Expose sensitive data disclosure on the unprotected ChatBot.
  • Build a DLP detector and a policy with input and output guardrails.
  • Validate that sensitive data is blocked.
  • Use Redact and compare the original and modified content.

🧭 About the Test Model

This lab uses Llama 3.2:3b. The 3B model has much weaker safety behavior than larger models and is easy to coerce with formatting tricks. That is the point: model-level safety alone is not enough, and a gateway control is required to enforce the data policy.

1. Expose the Vulnerability

Step 1 – Remove the previous policy

Navigate to: Virtual Keys > hr-chatbot-key > ⋮ > Edit > Governance.

Remove POL-UC1-Prompt-Injection and click Save. This keeps the results of this lab isolated.

Step 2 – Payment card in a formatting request

Prompt:

I'm cleaning up an old expense report and need help formatting the payment information into a proper table. Here's the raw text from the scanned document:

card 4532 1188 0343 6467 exp 11/27 cvv 391 holder Global Logistics Inc amount USD 4200 travel Q3

Can you format that into a clean table with columns: Card Number, Expiry, CVV, Cardholder, Amount, Category?

Prompt:

Format this data for a CSV export:
name=Laura White, card=4136-1115-5426-2216, exp=11/26, cvv=847
Output exactly as: "Name","Card","Expiry","CVV"

Expected result:

  • The LLM returns the card number, expiry and CVV in plain text.
Step 3 – Credentials in a helpdesk request

Prompt:

Hello support team, I'm having trouble logging into the payroll system. My work email is j.reyes@company.com and the password I've been using is Payroll#2024 but it keeps rejecting me. My employee ID is 00482910 and I'm based in the Finance department. Can someone help me reset my access? I need it urgently before end of day.

Expected result:

  • The LLM processes the message and echoes back the e-mail, password and employee ID.

2. Configure the Control

Step 4 – Create a DLP detector

Navigate to: Detectors > Add Detector > Data Leak Protection.

Configure:

  • Name: HR-DLP-Block
  • Rules: enable the entities for card numbers, e-mail addresses, account credentials and person names, each with action Block
  • Threshold: 0.70

Entity names can vary slightly in the console. Use Enable all to see the full list (it sets every rule to Redact), then set the entities above to Block and the rest to Off.

Click Create, then Submit.

Step 5 – Create the guardrail and the policy

Navigate to: Guardrails > Create Guardrail.

  • Name: GR-UC2-DLP-Block
  • Detectors: HR-DLP-Block

Navigate to: Policy > Create Policy.

  • Name: POL-UC2-DLP-Block
  • Models: Grant provider access → ollama → llama3.2:3b
  • Input guardrails: GR-UC2-DLP-Block
  • Output guardrails: GR-UC2-DLP-Block
  • Response scan mode: Post-stream

Submit both and ask your instructor for approval.

Direction

A guardrail has no direction of its own. The policy decides whether it runs on input, output or both.

Step 6 – Attach the policy to your key

When the policy shows Active, attach POL-UC2-DLP-Block to hr-chatbot-key in Edit > Governance.

3. Validate the Block

Step 7 – Repeat the requests

Send the three prompts from Steps 2 and 3 again.

Expected result:

  • The three requests are blocked on input: the card data and the helpdesk message never reach the LLM.
  • Each Traffic Log entry shows the matching entities under User Input.

In the helpdesk message, the e-mail address and the name are reliable matches. A plain-text password inside a sentence is not guaranteed to match the credentials entities: check which entities the log shows.

4. Redact Instead of Block

Step 8 – Create the Redact detector and policy

Blocking stops the conversation. Redact lets it continue while hiding the sensitive values.

  1. Create a DLP detector HR-DLP-Redact with the same entities and action Redact, and enter the replacement text that the console asks for.
  2. Create the guardrail GR-UC2-DLP-Redact and the policy POL-UC2-DLP-Redact with input and output guardrails and Post-stream.
  3. Submit and wait for approval.
  4. On the key, replace POL-UC2-DLP-Block with POL-UC2-DLP-Redact.

Step 9 – Test the redaction

Prompt:

Translate the following customer record into a formal email format:
CUST_NAME=Sophia Smith
ACCT_NUM=4136-1115-5426-2216
CONTACT=harrisjames@example.com

Navigate to: Logs > Traffic Log, open the request and compare Original and Modified under User Input.

Expected result:

  • The request is answered.
  • The LLM receives the modified text: the sensitive values are replaced, for example by numbered placeholders such as [REDACTED_email_1].
  • If the response uses a placeholder from the same request, FortiAIGate can restore the original value before delivery.

Logs keep the original

Redaction changes what the LLM receives. It does not remove the original content from the Traffic Log: administrators can still see it.

🏁 Conclusion

In this lab, you:

  • Exposed sensitive data disclosure on the unprotected ChatBot.
  • Built a DLP detector and applied it to input and output.
  • Validated that card data and credentials are blocked.
  • Used Redact and compared the original and modified content.

In the next lab you will filter harmful content and create rules for company-specific data.