Abstract:
This study investigates cybersecurity issues related to insider threat detection and mitigation in small and medium businesses (SMBs), which face unique challenges due to constrained budgets, legacy systems, and insufficient cybersecurity staff. We propose an automated framework for identifying insider threats in resource-poor SMBs using real-time behavioral monitoring, automated risk scoring, and intelligent alerting to predict potential security breaches before they occur. The system focuses on behavioral indicators such as unauthorized software execution, unusual access patterns, and suspicious system activities, enabling SMBs to maintain strong security postures without enterprise-level tools. Expert validation demonstrates the framework achieves 89% accuracy in detecting various insider threats including malicious insiders, negligent users, and compromised accounts, while improving employee security compliance by 27% and reducing unauthorized system usage by 65% in controlled testing environments. This research provides SMBs with an affordable, efficient solution for combating insider threats while advancing understanding of behavioral security indicators in resource-constrained environments.
