AUTOMATED METADATA ANALYSIS AS AN INFORMATION LEAKAGE VECTOR

Abstract

This paper investigates a fundamental problem of modern cybersecurity—the automated collection and analysis of metadata, which functions as the “invisible digital DNA” of electronic documents, media files, and network communications. The study challenges the dangerous “illusion of security,” whereby users and organizations focus primarily on the cryptographic protection of visible content while overlooking hidden transit, descriptive, and structural artifacts. The paper provides a comprehensive analysis of the specific architectural vulnerabilities of popular formats (Microsoft Office, PDF, and JPEG) and, based on real-world incidents, demonstrates how the integration of OSINT tools with artificial intelligence algorithms transforms these digital traces into instruments for deanonymization, physical reconnaissance, and the preparation of highly targeted spear-phishing attacks. To empirically validate the theoretical framework, the paper presents the results of an extended experimental study involving a sample of 100 digital objects across 9 different formats. The research revealed a critical level of background sensitive-information leakage, with successful author deanonymization achieved in 77% of cases. To algorithmize protection processes, a mathematical model for quantitative threat assessment, the Metadata Information Risk Score (MIRS), was developed and statistically validated, with its accuracy confirmed by an F1-score of 98%. The study also demonstrates that conventional basic data-scrubbing techniques are ineffective against deep XML structures in office documents. In conclusion, the paper substantiates the need for multilayered defensive strategies to counter these threats, ranging from comprehensive content decomposition technologies such as Content Disarm and Reconstruction (CDR) and NIST de-identification standards to the fundamental implementation of the “Privacy by Design” concept.