Secure your
LlamaIndex
Data Swarm
LlamaIndex security starts with verifying data-aware tool calls at the runtime level. SupraWall protects your RAG systems from prompt-injected data sources and unauthorized tool execution, ensuring your indices stay private and your agents stay safe.
RAG-Specific Security Layer
Unlike traditional LlamaIndex deployments that execute all retrieved tool calls blindly, SupraWall sits as a shim between the agent and the environment. Every retrieval result is scanned, and every tool execution is validated against your core security perimeter.
Query Engine Guard
Monitors and intercepts SQL, Vector, and Summary queries.
Tool Interceptor
Global shim for all LlamaIndex tool and function calls.
PII Scrubbing
Automatically redacts sensitive data from retrieval outputs.
Token Caps
Enforce deterministic budget limits across large RAG clusters.
EU AI Act Compliance (RAG)
Deploying LlamaIndex in production requires technical documentation (Article 11) and automatic logging (Article 12). SupraWall automates these requirements by providing tamper-proof audit logs for every data retrieval and tool execution session.
SupraWall's Technical Evidence Export fulfills Article 11 mandates by generating a full risk-assessment log of every high-stakes autonomous tool call.
Integration Checklist
# 1. Initialize the security shim
from suprawall.llamaindex import protect
# 2. Wrap your QueryEngine or ToolAgent
agent = OpenAIAgent.from_tools(tools)
secured_agent = protect(agent)
# 3. Intercept every data retrieval and tool execution
secured_agent.chat("query")
Query Validation
Intercepts complex RAG queries before execution for sensitive data leaks.
Tool Isolation
Restricts LlamaIndex tools to pre-defined execution environments.
Live Auditing
Real-time logging of document access and tool calls in the dashboard.
Budget Control
Cap token usage and tool execution costs across indices.