Implications of inadequate logging exploitation by malicious artificial intelligence agents

Abstract

Robust application logging is a critical component for securing systems. However poor logging practices, such as inconsistent event tracking and fragmented data, can introduce significant vulnerabilities within cybersecurity frameworks. Malicious Artificial Intelligence agents exploit gaps in system observability created by these deficiencies, hindering effective detection and response to threats. This paper explores the implications of inadequate logging infrastructure on system accountability and its potential for exploitation by Artificial Intelligence driven threats. By incorporating research from the domains of cybersecurity, artificial intelligence, anomaly detection, and digital forensics, it emphasizes the risks posed by inadequate logging in environments increasingly reliant on autonomous systems. Furthermore, this paper proposes strategies to enhance logging infrastructure to mitigate these risks, including real time anomaly detection, semantic log structuring, and improved auditability.