International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
www.irjet.net
Cybersecurity: Transitioning from Conventional Defense to Intelligent Threat Management Lavanya Jain Sacred Heart Convent School, Ludhiana, India --------------------------------------------------------------------------***-----------------------------------------------------------------------Abstract This paper charts the growth of artificial intelligence in cybersecurity, moving from the rigid, rule-based defenses of the past to the reflexive, adaptive response systems we see today. The account begins with the expert systems of the 1980s, which first automated the search for viruses, continued through the machine-learning gains of the early 2000s and culminated in modern platforms that harness deep neural networks. Supporting data reveal that AI-enhanced solutions now cut false positives in half and improve detection accuracy for actual threats by 85% when stacked against their legacy counterparts. The review then considers newly rising functions: fully automated incident response, user-behavior analytics, the identification of fresh zeroday exploits, and predictive threat intelligence powered by AI, collectively underscoring the widening reach of machine learning across every phase of the security lifecycle. Privacy implications, ethical issues with AI autonomy in security decisions and a critical analysis of the difficulties posed by adversarial AI attacks are conducted. The study focuses on new developments like explainable AI for compliance needs, quantum-resistant AI algorithms, and federated learning for privacypreserving security. This study shows that the incorporation of AI in cybersecurity signifies not only a technical breakthrough but also a fundamental paradigm shift toward proactive, intelligent defense mechanisms that can adjust to the ever-changing threat landscape, as cyber threats continue to grow in complexity and scale.
Keywords: Deep Learning, Neural Networks, Machine Learning, Threat Detection, Artificial Intelligence, Cybersecurity, and Behavioral Analytics
I. INTRODUCTION Cybersecurity has become one of the greatest challenges of the digital age, as cyberattacks have reached increasingly regular, complex, and severe levels. The traditional detection methods, which primarily rely on pattern-based detection and rule-based engines, are no longer sufficient to combat modern attackers that leverage high-level threats such as advanced persistent threats (APTs), zero-day exploits, and polymorphic malware [1]. The use of AI in cybersecurity has transformed threat monitoring from a defensive mindset (responding to incidents) to an offensive one (predicting and preventing attacks). 1.1 The Cybersecurity Challenge Today’s digital environment offers a whole new and distinctive 1/platform to engage and interact between kernel developers and the rest of the world. The bad guys use state-of-the-art methods, including machine learning and other AI techniques, to craft more targeted and successful attacks; and not only are attackers getting better and more efficient, they’re getting more numerous, as successful hustles breed a proliferation of copycats. The average cost of a data breach was $4.45 million in 2023, which increased by 15% in three years [2]. Moreover, it takes 277 days to spot and stop a breach – more than enough time for attackers to wreak havoc on your systems. 1.2 AI as a Game Changer The transformative capabilities of Artificial Intelligence are dealing with the core limitations of traditional cybersecurity. AI can process huge volumes of data in real time, help identify patterns that are not always visible to human analysts, and can be used to predict outcomes for specific situations such as hazards threatening people in the field.AI in cybersecurity market is expected to grow at a compound annual growth rate (CAGR) of 23.6%to a market size of $46.3 billion in 2027 [3].
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