AI Data Leakage Prevention with LLMs

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UPDATED: October 6, 2026

Intermediate

Ensure your LLM applications are truly secure against data exposure. Taught by industry expert Jonathan Barrios, this hands-on course guides you through tracing sensitive data across your entire AI stack, from initial user prompts and RAG systems to agent tool calls and system logs. You’ll step into real-world scenarios using Google Colab notebooks to implement robust data protection controls layer by layer. Through practical exercises, you'll master prompt scanning, automated PII redaction, granular retrieval permissions, prompt injection mitigation, output filtering, and secure logging. By the end of this course, you will have built a complete, working data protection pipeline with synthetic data that you can adapt to your own applications or systems.

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What you'll learn with AI Data Leakage Prevention training

Detect and redact sensitive data before it reaches an LLM prompt
Classify data by sensitivity level and apply retrieval permissions in RAG systems
Trace how sensitive information leaks through conversation history, file uploads, and agents
Test your application for prompt injection vulnerabilities that expose private data
Respond to an AI data leak by containing the data path, preserving evidence, and tracing which boundary failed
Review a complete LLM architecture against OWASP AISVS and make an evidence-based READY or HOLD release decision

AI Data Leakage Prevention training FAQs

Do I need a security background to take this AI security course?

No prior cybersecurity experience is required! This course is tailored specifically for developers building AI applications. Basic proficiency in Python and familiarity with general LLM APIs are recommended. Core security concepts are explained clearly and demonstrated through direct code implementation, so you don't need familiarity with complex enterprise security frameworks.

How is this different from a general AI security or cybersecurity course?

This course focuses exclusively on solving one critical challenge: preventing sensitive data exposure in LLM applications. Rather than covering broad network security or theoretical compliance frameworks, it delivers practical, code-level defenses for the specific leak points unique to AI systems, including prompts, chat history, file uploads, RAG pipelines, agent tool calls, and monitoring logs.

What does the hands-on portion of this AI security course actually look like?

Each module pairs core concepts with interactive Google Colab notebooks. You will work through practical exercises, like scanning prompts for PII, redacting sensitive content, setting up RAG permission filters, and testing agent safety, all using realistic synthetic datasets. You'll be actively writing and running code to build real-world security defenses.

I already use tools like Presidio or a secrets scanner in my pipeline. Will this course cover anything new?

Standard tools like Presidio or basic secret scanners typically only cover one or two layers of security. This course addresses many distinct data exposure vectors across the LLM pipeline, including conversational context accumulation, RAG retrieval access controls, agent tool interactions, and log telemetry. You'll discover critical security gaps that basic scanners miss and learn how to close them.

Does this AI security course lead to a certification or credential?

While this course does not grant a formal certification, it equips you with actionable, real-world skills. You will finish with a complete, production-ready LLM data protection pipeline that you can immediately apply to your own software applications and engineering workflow.

Who is AI Data Leakage Prevention training for?

This course is designed for software engineers, AI engineers, and architects building or maintaining LLM-powered applications who want to ensure strict data security across their entire application stack. It is also ideal for experienced developers new to generative AI who want to establish security best practices before going to production.

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Study guide

Download the free AI Data Leakage Prevention study guide to complete this course in about 11 hours.

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