Cencurity

Product Introduction

1. Definition: Cencurity is a specialized security gateway designed to proxy and monitor LLM (Large Language Model) and AI agent traffic. It operates as a real-time data protection layer, intercepting requests and responses between users, models, and tools to enforce security policies.
2. Core Value Proposition: Cencurity eliminates prompt leakage, unauthorized access, and sensitive data exposure in AI workflows. Its primary purpose is to enable enterprises to deploy LLM agents safely by automatically detecting, redacting, or blocking PII, secrets, and risky code patterns while generating audit-ready logs.

Main Features

1. Real-Time Data Protection: Scans LLM traffic using pattern-matching algorithms and policy engines to identify secrets (API keys, credentials), PII (emails, IDs), and unsafe code snippets. Blocks or masks violations before data reaches models or end-users.
2. Centralized Security Dashboard: Provides a unified view of all agent interactions, displaying real-time metrics like request/response payloads, latency, policy violations, redaction logs, and block events. Supports searchable audit trails for compliance.
3. Zero-Click Guardrails: Enforces security policies automatically without manual intervention. Integrates with existing LLM providers (OpenAI, Anthropic, etc.) and IDEs via API proxies, requiring no code rewrites.
4. Webhook Notifications & Dry Runs: Sends alerts to Slack/Jira for policy violations and offers a "dry-run" mode to simulate policy enforcement impact before deployment, reducing rollout risks.

Problems Solved

1. Pain Point: Prevents sensitive data leakage (e.g., proprietary code, credentials) in LLM prompts/responses and stops risky outputs (e.g., malicious code suggestions) from reaching users.
2. Target Audience: AI developers, DevOps engineers, and security teams in enterprises using LLMs; compliance officers needing audit trails for SOC 2/GDPR.
3. Use Cases:
   • Securing AI-powered coding assistants (e.g., GitHub Copilot) by redacting secrets in real time.
   • Auditing customer-facing chatbot interactions for PII compliance.
   • Enforcing governance in RAG (Retrieval-Augmented Generation) workflows to block unauthorized data access.

Unique Advantages

1. Differentiation: Unlike static API gateways, Cencurity combines LLM-specific traffic proxying, context-aware redaction, and compliance logging in one layer. Competitors lack real-time code-pattern analysis for AI agents.
2. Key Innovation: Patented policy-first detection engine prioritizes threats by risk severity and offers per-user API key isolation—each user gets a unique subdomain proxy and dashboard, eliminating shared credential risks.

Frequently Asked Questions (FAQ)

1. How does Cencurity integrate with existing LLM workflows?
   Cencurity proxies traffic via API endpoints compatible with major providers (OpenAI, Anthropic), requiring only configuration changes—no code modifications. Setup takes minutes using GitHub quickstarts.
2. Can Cencurity protect against prompt injection attacks?
   Yes, it detects and blocks malicious inputs/outputs using pattern-based policies, reducing prompt injection risks by sanitizing LLM traffic in real time.
3. Is Cencurity suitable for regulated industries?
   Absolutely. Its audit-ready logs, PII redaction, and role-based access (RBAC) support compliance with GDPR, HIPAA, and SOC 2 frameworks.
4. What happens during a "dry run" mode?
   Dry run simulates policy enforcement without blocking traffic, providing violation reports to assess impact before enabling live protections.
5. How does Cencurity handle user-specific data isolation?
   Each user receives a unique proxy subdomain and API key. Dashboards and credentials are accessible exclusively to the key holder, preventing cross-user data exposure.