Data Leakage Prevention with LLMs

For IT leaders: As teams adopt LLM-powered apps, assistants, and agents, sensitive data can leave the organization through prompts, file uploads, retrieval systems, conversation history, model outputs, and logs. This course gives your team a structured way to find and close those gaps before they become incidents or audit findings.

Role fit: Best suited for software engineers, AI engineers, security engineers, and platform teams building LLM features. Team Leads can assign the full course to the engineers who own AI data protection, and the introduction and the final release-review skill to anyone who approves AI tools.

Time investment: 15 skills totaling about 11.5 hours per learner, delivered in roughly 45-minute segments that fit into a weekly training block.

Change-management note: The curriculum follows the path sensitive data takes through an LLM application: detecting and classifying it in prompts, redacting it, protecting file uploads and retrieval, securing conversation history, agent tool calls, model outputs, and logs, then responding when a leak happens and reviewing the whole system before release. Each skill ends with a working control, so teams can put protections in place as they learn rather than waiting for a full rollout.

Training Managers can assign the course to the right people through Playlists and track completion with Team Reporting.

AI adoption often moves faster than data protection policy. This training prepares your team to put practical controls around the places where LLM apps actually leak data.

• Fewer accidental exposures: Teams learn to detect and classify sensitive data in prompts and file uploads, then redact it before it reaches a model.
• Safer internal knowledge assistants: Practitioners learn to protect RAG systems and apply permissions to AI retrieval, so users only get answers drawn from data they are allowed to see.
• Stronger resistance to manipulation: Teams test prompt injection for data leakage and secure multi-turn conversations and AI agents, which reduces the risk of attackers pulling out protected information.
• Faster, more organized incident handling: With output filtering, protected AI logs, and a structured response process for AI data leaks, your organization is better prepared to contain an exposure and show auditors how it was handled.
• Consistent policy and confident releases: Teams unify the individual controls under one shared data protection policy, so every boundary enforces the same rules without drift, then review the complete architecture against OWASP's AI Security Verification Standard and make an evidence-based READY or HOLD decision before shipping.

Knowledge

• How data leaks through AI applications, including prompts, uploads, retrieval, conversations, agents, outputs, and logs
• How to classify data by sensitivity to decide which protections apply
• How RAG systems and retrieval permissions affect what data an LLM can expose
• How prompt injection can be used to pull sensitive data from AI systems
• Why logs and multi-turn conversation history need their own data protection controls
• How individual LLM data protection controls combine into a layered defense

Ability

• Detect sensitive data in prompts and redact it before it is sent to a model
• Protect data in file uploads and apply permissions to AI retrieval
• Protect agent tool calls and tool results so sensitive data cannot leave through integrations
• Test AI applications for prompt injection that leads to data leakage
• Filter sensitive content from AI outputs and protect sensitive data in AI logs
• Respond to an AI data leak with a structured process
• Design an end-to-end LLM data protection system and review it for an evidence-based READY or HOLD release decision