AI-Powered Cybersecurity: The Future of Threat Detection

Abstract - This paper explores the transformative role of Artificial Intelligence (AI) in cybersecurity, focusing on its ability to enhance threat detection, automate responses, and mitigate evolving cyber threats. Traditional cybersecurity measures, reliant on predefined rules and signature-based detection, struggle to combat advanced threats like polymorphic malware and zero-day exploits. AI-powered solutions leverage machine learning, deep learning, and behavioral analytics to proactively identify and neutralize threats in real time. Key advantages include reduced human error, scalability, and predictive threat intelligence. However, challenges such as adversarial AI attacks, data privacy concerns, and ethical biases must be addressed. The study highlights real-world applications, emerging trends like Explainable AI (XAI), and the integration of AI with quantum computing. By analyzing current advancements and future prospects, this research underscores AI’s critical role in shaping the future of cybersecurity. Key Words: artificial Intelligence, Machine Learning, cybersecurity, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Network Security,

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-04-25
DOI
https://doi.org/10.55041/ijsrem45943
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Cybersecurity: The Future of Threat Detection

Sujeet Kumar Sharma
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Network Security and Intrusion Detection
article

AI-Powered Cybersecurity: The Future of Threat Detection

Sujeet Kumar Sharma
article en

Abstract

Abstract - This paper explores the transformative role of Artificial Intelligence (AI) in cybersecurity, focusing on its ability to enhance threat detection, automate responses, and mitigate evolving cyber threats. Traditional cybersecurity measures, reliant on predefined rules and signature-based detection, struggle to combat advanced threats like polymorphic malware and zero-day exploits. AI-powered solutions leverage machine learning, deep learning, and behavioral analytics to proactively identify and neutralize threats in real time. Key advantages include reduced human error, scalability, and predictive threat intelligence. However, challenges such as adversarial AI attacks, data privacy concerns, and ethical biases must be addressed. The study highlights real-world applications, emerging trends like Explainable AI (XAI), and the integration of AI with quantum computing. By analyzing current advancements and future prospects, this research underscores AI’s critical role in shaping the future of cybersecurity. Key Words: artificial Intelligence, Machine Learning, cybersecurity, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Network Security,

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(04)
Openalex Percentile: Top 92%
Network Security and Intrusion Detection
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AI-Powered Cybersecurity: The Future of Threat Detection — Sujeet Kumar Sharma · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2025) | TGRS Research Map | TGRS