Zero-Trust Security
for AI Agents.
Sanitize data before your autonomous bots consume it. Protect multi-agent workflows from leaking customer data or ingesting malicious prompt injections.
Systematic Privacy Risks
Autonomous Over-sharing
A customer service bot inadvertently sending a user's full account history to the OpenAI API for summarization.
RAG Prompt Injections
Malicious inputs tricking an internal HR agent into revealing salaries because the source Vector DB was not masked.
Multi-Agent Cascade Failures
One compromised agent leaking an unprotected database URL across a fleet of inter-communicating models.
Vector DB Sync: Raw Data Upload
Vector DB Sync: Tokenized [NAME_1] Object
Zero-Trust Operational Flow
Offline compliance methodologies for scalable data
Offline Staging
Load formats natively into edge memory.
Regex Enforcement
Execute deterministic entity detection locally.
Clean API Call
Safely interact with external models post-masking.
Key Reconstruction
De-tokenize dynamically returning context.
Regulatory Trust Framework
Satisfied
Verifiable bounds for AI agents
Satisfied
Agent architecture access controls
Satisfied
Machine-to-Machine security
Satisfied
Automated decision privacy constraints
AGENTS Intelligence Deep-Dive
Can I use PrivacyScrubber in my LangChain bot?
Do agents really pose a higher privacy risk?
Does tokenization stop prompt injection?
Ready to Defend Your IP?
Stop relying on APIs. Encrypt entities directly at the edge.
DEPLOY PRO — $9.99 ONE-TIME