<![CDATA[The high incidence of data breaches caused by security misconfiguration and improper public directory access has made Information Disclosure a critical threat to organizational digital assets. Conventional self-dorking monitoring methods are highly inefficient, prone to human error, and generate a massive amount of false positives without clear risk prioritization. This study aims to design, develop, and evaluate a web-based public information leakage monitoring tool that integrates automated Open Source Intelligence (OSINT). Developed using the Prototyping method with a Python Flask backend and MySQL database, the system utilizes the Serper.dev API to automate checklist-based Google Dorking. It implements a Pattern Matching algorithm based on Regular Expression (Regex) to filter out false positives and applies an Additive Weighting method for Risk Scoring based on four specific metrics. System evaluation through comparative accuracy tests demonstrates that the Regex implementation successfully validates vulnerability patterns with an extraction accuracy of 97.7%. Furthermore, the Risk Scoring algorithm precisely assigns severity labels (Critical, High, Medium, Low) that identically match manual mathematical calculations. In conclusion, this scanning tool effectively overcomes the limitations of manual searches by computationally transforming raw search engine data into structured, actionable security intelligence.Keywords - Information Disclosure, Google Dorking, Open Source Intelligence, Pattern Matching, Risk Scoring.]]>
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