Tutorials Prompt Engineering Tutorial
AI Data Leakage — Complete Guide
AI Data Leakage — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 75 of 100
AI Data Leakage
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 8: Prompt Security & Ethics
What is this?
Data leakage is when prompts, logs, or model outputs expose secrets, PII, or one customer's data to another — in prompts, logs, or replies.
Why should you care?
PromptVerse redacts PII pre-prompt, filters secrets in logs, and enforces tenant isolation in retrieval.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
before_llm:
text = redact_pii(user_input)
assert no_secrets(text) # block if API key pattern
log_store.write({ hash: prompt_hash, redacted: true }) # never raw secrets
chunks = retrieve(filter: { tenant_id })What happened?
- Redact before model sees text.
- no_secrets blocks accidental key paste.
- Logs store hash not full prompt with secrets.
Practice next
- Scan sample logs for email/SSN patterns.
- Add API key regex block.
- Verify tenant filter on retrieve.
- Tokenize PII replace with [EMAIL_1].
- DLP scan on outbound model text.
Remember
Redact PII pre-LLM. Never log secrets. Tenant isolate retrieval.
Log scrub
Dev pasted prod API key into chat.
Outcome: Block + alert; key rotated; log stores hash only.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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