Three-Layer Defense Framework for Secure LLM Applications

A layered security framework for LLM-enabled applications designed and evaluated to reduce prompt injection, unauthorized data access, and sensitive information leakage, implementing three defensive layers: Context Isolation (restricts model access to only the data a user is authorized to view), Prompt Sanitization (detects and filters malicious or manipulative user inputs before they reach the model), and Response Redaction (masks or blocks sensitive information if the model attempts to expose it in its output). Testing different layer combinations showed that a single control provides only limited protection, and that stronger security comes from combining multiple defenses around the LLM rather than assuming the model itself can enforce trust boundaries.

Skills demonstrated: LLM security architecture design, prompt sanitization, output redaction, layered/defense-in-depth security design

Frameworks: Defense-in-depth, Context isolation -- see Framework & Regulatory Knowledge

Outcome: Key finding: single-layer controls under-perform combined, defense-in-depth layering -- the same architectural pattern independently measured end-to-end in the LLM Data Leakage Lab.