Preventing Social Engineering Attacks With Behavior Driven Authentication in Digital Workspaces
Keywords:
Social Engineering, Behavior-Driven Authentication, Cybersecurity, Digital Workspace, User Behavior Analytics, Phishing Prevention, Anomaly Detection, Access ControlAbstract
Social engineering attacks remain one of the most dangerous vectors in cybersecurity, exploiting human behavior rather than system vulnerabilities. As workforces transition to digital and hybrid models, traditional security protocols are no longer sufficient. This paper explores the use of behavior-driven authentication (BDA) to prevent such attacks in digital workspaces. By analyzing users' behavioral biometrics and contextual patterns, BDA systems can detect anomalies and mitigate unauthorized access attempts. This approach, when integrated into digital workspace security architectures, offers a resilient barrier against evolving social engineering threats.
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Copyright (c) 2025 Maree Girdwood (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
