Log Anonymization Filter for Sensitive Data Protection
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Solution Overview
Problem
Enterprise applications face vulnerabilities and compliance challenges due to sensitive information being written in verbose log files, with traditional approaches either risking human errors or leaving a window for cyberattacks during reactive data extraction.
Innovation Solution
A system and method for anonymizing logs that detects sensitive information using predefined search pattern policies and converts it into anonymous data within the application before writing it to a destination, ensuring compliance and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If sensitive data is extracted and removed from log files before they are available on a centralized logging platform, then data security is improved, but an attacking window is created where cyberattackers can steal sensitive information from the original log files during the extraction process
Solution Approach 1:
The system performs preliminary action by anonymizing sensitive information at the source logging point before the log data is written to persistent storage or transmitted to centralized platforms. The anonymization filter intercepts log data as it is generated and replaces sensitive fields (such as SSN, credit card numbers, email addresses) with anonymized values, eliminating the attacking window before data can be exposed.
2Object-affected harmful factors
If engineers manually mask or limit sensitive data in log files following programming best practices, then data protection is improved, but programming and human errors may make sensitive information world-readable
Solution Approach 1:
The system implements self-service by deploying an anonymization filter that automatically detects and anonymizes sensitive information in log data without requiring manual intervention from engineers. The filter uses predefined patterns and rules to identify sensitive fields and applies anonymization automatically, eliminating human error and ensuring consistent, reliable data protection across all log entries.
Solution Approach 2:
The system replaces the mechanical manual masking process with an automated computational system. Instead of relying on engineers to manually review and mask sensitive data, the anonymization filter uses pattern recognition algorithms to automatically identify and replace sensitive information, improving both reliability and consistency of data protection.
3Productivity
If verbose information is written in log files for application analysis and troubleshooting, then diagnostic capability is improved, but sensitive information in the same files creates vulnerability and compliance challenges
Solution Approach 1:
The system applies local quality by selectively anonymizing only the sensitive portions of log data while preserving the rest of the log entries for diagnostic purposes. The anonymization filter identifies specific fields containing sensitive information (such as personally identifiable information, financial data, or credentials) and applies anonymization only to those fields, maintaining the usefulness of the log files for troubleshooting and analysis while removing compliance vulnerabilities.
Data Source
AI summary
System and method for anonymizing logs generated in applications running in a computing environment detects log data being generated in an application and compares the log data to a set of predefined search pattern policies to find sensitive information contained in the log data. The sensitive information contained in the log data is converted into anonymous information to produce anonymized log data within the application. The anonymized log data is then written to a destination.