Leveraging Secured Ai-Driven Data Analytics For Cybersecurity: Safeguarding Information And Enhancing Threat Detection
Oladele J Adeyeye, Ibrahim Akanbi, Isaac Emeteveke, Oluwatobi Emehin · International Journal of Research Publication and Reviews · 2024
As cyber threats become increasingly sophisticated, leveraging secured AI-driven data analytics has emerged as a critical strategy for enhancing cybersecurity measures.This article explores the transformative role of AI-driven data analytics in the realm of cybersecurity, emphasizing its applications in anomaly detection, threat intelligence, and predictive analysis.By harnessing the power of AI, organizations can proactively identify potential threats and respond effectively, thereby safeguarding sensitive information.However, the implementation of AI models also necessitates robust security measures to protect the data utilized in these analytics.This discussion encompasses essential practices such as data anonymization, federated learning, and adherence to data protection regulations, which ensure the privacy and security of user information.Additionally, the article presents case studies that illustrate the effectiveness of secured AI-driven data analytics in real-world scenarios, demonstrating how organizations have successfully identified and mitigated cyber threats while maintaining user trust and compliance with privacy standards.Ultimately, this article aims to provide insights into the best practices for integrating secured AI-driven data analytics into cybersecurity frameworks, highlighting the balance between advanced threat detection capabilities and the imperative of protecting user privacy.