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Cencurity

Security gateway for LLMs that detects and masks sensitive data and risky code.

PricingFree
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AddedFeb 2026
Official URL
cencurity.com

Tool overview

Overview

Cencurity is a security gateway purpose‑built for Large Language Models (LLMs), giving engineering and security teams precise control over what data reaches AI systems and what leaves them. Sitting between your applications and LLM providers, Cencurity automatically detects sensitive information, masks or redacts it in real time, and flags risky prompts or generated code before it can reach production. This helps you unlock AI capabilities without exposing confidential data, secrets, or business‑critical logic. With developer‑friendly APIs, SDKs, and straightforward deployment options, Cencurity integrates seamlessly into existing backends, AI agents, and tooling. It analyzes both prompts and responses, scanning for PII, credentials, source code, and compliance‑relevant content while maintaining detailed logs for auditing and incident response. Policy‑driven controls let you define what is allowed, masked, or blocked, so you can enforce consistent data governance across all LLM use cases. Designed for modern AI stacks, Cencurity supports multi‑model workflows, streaming interactions, and complex agent architectures. Whether you are building internal copilots, customer‑facing chatbots, or automated code assistants, Cencurity helps you reduce data leakage risk, maintain compliance, and gain visibility into how LLMs interact with your systems—while keeping performance high and friction for developers low.

Features

  • Real-time sensitive data detection
  • Automatic masking and redaction
  • Risky code and prompt filtering
  • Policy-based security controls
  • LLM-agnostic gateway architecture
  • Developer-friendly APIs and SDKs
  • Comprehensive security logging
  • Easy integration with AI agents

Tags

AI Developer Tools
AI Agent
Large Language Models (LLMs)
Log Management

Use Cases

  • Protect customer PII in support chatbots by detecting and masking names, emails, phone numbers, and IDs before prompts are sent to external LLM providers.

  • Secure internal developer copilots by scanning generated code for secrets, tokens, and unsafe patterns before it is committed or deployed.

  • Enforce data governance for AI agents that access multiple internal systems, ensuring confidential records and logs are never exposed in model interactions.

  • Monitor and audit all LLM traffic across teams, creating a centralized log for security reviews, incident response, and compliance reporting.

  • Safely experiment with new LLM providers or models while maintaining consistent security policies and controls at the gateway layer.

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