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Risk Mirror

Risk Mirror

AI Security, Without the Hallucinations

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It sits between your users and your LLM (OpenAI/Anthropic) and strips out sensitive data before it leaves your server. The Tech (Zero AI Inference): Instead of asking an LLM "is this safe?", I use: 152 PII Types: My custom engine covers everything from US Social Security Numbers to Indian Aadhaar cards and HIPAA identifiers. Shannon Entropy: To detect high-entropy strings (API keys, passwords) that regex misses. Deterministic Rules: 100% consistency. No "maybe."
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Risk Mirror gallery image
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Launch tags:Developer Tools•Artificial Intelligence•Security
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Flowstep
Flowstep
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I previously built the PII Firewall Edge API to handle high-fidelity PII detection without using ML models. Then I decided to package my API into a full-featured UI/Toolkit called Risk Mirror. AI Safety should be deterministic. If you rely on an LLM to police another LLM, you are stacking probability error rates (99% * 99% = 98%). What it does: It sits between your users and your LLM (OpenAI/Anthropic) and strips out sensitive data before it leaves your server. Risk Mirror exposes this engine as a developer toolkit. Stateless: No data retention. 152 Classifiers: From financial data (PCI-DSS) to healthcare (HIPAA). Zero AI: We use specialized algorithms to redact data.