Sanitize Sensitive Data
Before Using AI.
Secure your industry-specific data before using LLMs with our zero-trust, local-only sanitization engine.
Executive Summary: AGENTS
The next wave of AI is autonomous agents (RAG, LangChain, AutoGPT), but these systems create permanent data trails as they chain prompts together. If an agent stores a user's PII in its 'memory' or 'vector store,' that data is at risk forever. PrivacyScrubber is the foundational tool for Secure Agentic AI. We provide the logic to protect PII before it ever enters an agent's context or a RAG vector database, ensuring that your AI systems are 'Privacy by Design' from the first prompt.
Privacy Checkpoints
- Vector Privacy: Don't index PII in your RAG databases.
- Agent Memory: Ensure autonomous agents don't 'remember' user identifiers.
- Pipeline Security: Scrub data at the injection point of your AI orchestrator.
- Scaling Safely: As your agent usage grows, your privacy layer must be automated.
Identified Risks & Solutions
PII Detection Matrix
| Entity Type | Exposure Risk | Local Edge Control |
|---|---|---|
| Contextual Data | High (Persistence) | Pre-Sanitization |
| Vector IDs | Medium (Linkage) | Attribute Masking |
| Agent History | High (Leakage) | Session-Wipe Logic |
The Agents AI Privacy Gap
Data Persistence
Raw sensitive inputs are often stored by AI vendors for model training.
Compliance Liability
Uploading unredacted PII violates industry-specific global privacy mandates.
Shadow AI Risk
Employees using unvetted AI tools create invisible data leakage vectors.
Raw Input: Sensitive Information here
Sanitized: Sanitized [PII_1] here
Secure Agents AI Workflow
Enable high-performance AI without client data leaving your machine
Import Files
Upload documents locally into the PrivacyScrubber sandbox.
Local Masking
Identify and tokenize sensitive strings entirely within browser memory.
Analyze with AI
Submit sanitized prompts to ChatGPT or Claude for processing.
Reverse Scrub
Bring back original data into the AI response locally for the final draft.
Hardened Audit Standards
Satisfying strict global security frameworks for Agents data.
Article 25
Privacy by design and by default.
Confid.
No data persistence on unauthorized infrastructure.
Data Priv.
State-level compliance for consumer masking.
A.8.11
Data masking standards for secure processing.
Implementation Guides
Explore specific PII redaction workflows for Agents Teams
Secure AI Agent Memory
AI agents that retain memory can accumulate PII. Here is how to protect before storing.
Agentic AI Data Leak Prevention
Multi-step AI agent workflows compound PII exposure risk. Protect at each input stage.
RAG Privacy
Retrieval-augmented generation (RAG) indexes your documents. Protect PII before it enters the vector store.
Zero-Trust AI Data Pipelines
Design AI data pipelines that never expose raw PII. Local protection as a pipeline stage.
LLM Fine-Tuning Privacy
Fine-tuning LLMs on private data requires de-identification. How to scrub training datasets locally.
Self-Hosted Agent Systems PII Protection
Even self-hosted or open-source AI agent systems require strict PII protection to prevent lateral data movement and internal exposure.
Make and Zapier AI Privacy
Make (Integromat) and Zapier pass real customer data through AI steps. Here is how to protect PII before each AI action in your workflow.
n8n AI Workflow Privacy
n8n lets you build powerful AI automations — but each node that touches real data is a PII leak point. Here is how to protect at every stage.
Deploy Secure Agents AI Today
Satisfy compliance requirements, eliminate disclosure risks, and innovate at the speed of AI.