Abstract

The frequency and proficiency of Cyber-attacks have been increasing lately, this transition necessitates a more robust and protected security defense practices such as near-immediate detection, analysis, and defense by cyber security and forensic specialists as the conventional defense mechanisms founded on the principles of empirical and pattern-based methods is failing to match the pace. With the constant rise in technology and inventions, data consumption and is expected to touch the mark of 181 zettabytes by the end of 2025. Cybersecurity is a sincere concern to a lot of organizations because most of them are using Internet-connected data devices paving the way for cyber attackers. Cyber threat intelligence (CTI) analyzes the data to show the patterns of potential cyber-attacks and forecast the behaviors of bad actors. Based on the depth of intelligence and targeted audience, there are three major CTI types; strategic, tactical, and operational. While CTI practices are not completely eminent it is an iterative process; therefore, it lets organizations enhance their defense approach against emerging cyber threats. Not only the frequency but also the complexity of attacks has increased over the years resulting in successful intrusions with more severe forms of security breaches, this calls for outstanding threat intelligence within the cyber sphere requiring the knowledge base of threat information and a meaningful approach to express this language. In the past, CTI has been treated as a reactive defense measure used after the fact, security teams would collect and store threat intelligence to analyze an attack that had already happened, hoping to glean insights for future similar attack scenarios but in recent times the cybersecurity approach has changed from reactive to proactive. However, as technology advances, defenders can now unlock the power of automation and AI, enabling companies to move into a new era of proactive threat intelligence in which cyber defenders can take advantage of security signals in near real-time. The integration of Artificial Intelligence (AI) with Cyber Threat Intelligence (CTI) marks a transformative era in cybersecurity, addressing the increasing sophistication and frequency of cyber-attacks. The traditional security defenses were mostly reliant on empirical and pattern-based methods, and they are slowly becoming inadequate against the dynamic nature of cyber threats. Studying the AI's capability to automate and enhance the CTI cycle from requirement gathering to feedback presents a modern proactive approach to cybersecurity. Organizations can significantly reduce the detection time of cyber intrusions, automate threat responses, and refine their defense mechanisms against emerging threats by leveraging AI which shows promising impact towards proactive defense practices. Through AI Cybercrooks will benefit from widespread deployment of advanced AI tools before their targets can set up AI in their own defense. By meticulously using innovations such as machine learning, deep learning, and natural language processing we can enable the identification of malicious patterns and unusual activities with unprecedented speed and accuracy. However, this technological advancement also necessitates a secure implementation of AI to thwart adversaries from exploiting AI-powered systems.

Paper Number

tpp1366

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