The Necessity of Client-Side AI Guardrails
As engineering teams, legal professionals, and financial analysts integrate generative AI into daily workflows, unvetted prompts routinely cause severe data leaks. Commercial LLM providers log input prompts for inference monitoring, compliance auditing, and downstream model reinforcement learning.
1. Why Deletion is Worse Than Pseudonymization
If you completely erase variables from code or prompts, the AI model loses syntactic context and hallucinates missing parameters. By replacing sensitive entities with structured synthetic tokens (e.g. <API_KEY_1> or <EMAIL_1>), the model accurately analyzes logic while leaving real credentials unexposed.
2. In-RAM Luhn Checksum & Regex Validation
To avoid false positives, credit cards are verified client-side using the **Luhn Checksum Formula**:
$$\sum_{i=0}^{n-1} d_i \equiv 0 \pmod{10}$$This ensures random numeric strings are not unnecessarily masked while ensuring genuine payment details are caught.
🔐 Privacy Text Encryptor
Encrypt confidential text with AES-256-GCM via Text Encryptor Studio.
🛡️ Email Privacy Risk Checker
Audit email handle identity leakage with Email Risk Checker.
Frequently Asked Questions
Are prompts or code snippets uploaded to a server for scanning?
No. All scanning, regex evaluations, and token replacements run 100% locally in your device's browser memory (RAM).
How do I restore my original secrets after getting the AI response?
Keep this tab open while chatting with the AI. When the model returns its code or answer with the synthetic tokens (e.g. <API_KEY_1>), use the substitution map on the right to swap the real values back.