AI in Endpoint Security: Predicting and Preventing Insider Threats Through Behavioral Analysis
Abstract
Insider threats are challenging to detect and can have significant security impacts. This paper presents an AI-driven endpoint security solution that monitors user behavior patterns to identify potential insider threats. Using anomaly detection techniques and behavioral analytics, the model flags unusual activities that may indicate data theft, unauthorized access, or sabotage. Our results, drawn from a variety of industry environments, show that the model can accurately predict insider threats, allowing organizations to intervene before a breach occurs. This study demonstrates how AI can provide early warning systems for enhanced internal security.
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References
Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.
Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
Balantrapu, S. S. (2021). A Systematic Review Comparative Analysis of Machine Learning Algorithms for Malware Classification. International Scientific Journal for Research, 3(3), 1-29.
Balantrapu, S. S. (2020). AI-Driven Cybersecurity Solutions: Case Studies and Applications. International Journal of Creative Research In Computer Technology and Design, 2(2).
