Artificial Intelligence in Cybersecurity

Abstract : The increasing sophistication and frequency of cyber-attacks have outpaced traditional security measures, creating an urgent need for Artificial Intelligence (AI) in cybersecurity. AI-driven solutions, powered by Machine Learning (ML) and Deep Learning (DL), enhance threat detection by analyzing vast datasets in real time, identifying anomalies, and predicting potential attacks before they occur. Unlike conventional rule-based security systems, AI continuously learns and adapts to evolving cyber threats, making it a crucial asset in combating malware, phishing, ransomware, and zero-day exploits. This study explores AI’s role in cybersecurity, assessing its effectiveness in automating threat detection, improving risk management, and strengthening digital defenses. AI-powered security tools utilize behavioral analysis and predictive analytics to minimize false positives and enhance incident response times. However, AI-based cybersecurity systems also face challenges, including adversarial attacks, data privacy concerns, biases in algorithms, and the need for continuous model updates. Addressing these challenges is essential to ensuring the reliability and fairness of AI-driven security solutions. Despite these limitations, AI continues to revolutionize cybersecurity by enhancing proactive defense mechanisms, reducing reliance on manual intervention, and creating a more resilient digital environment. As cyber threats evolve, the integration of AI in security frameworks will be essential in safeguarding sensitive data and maintaining trust in the digital age.

Keyword Cyber Threats, Neural Networks (NN), Cybersecurity Automation, Intrusion Detection Systems (IDS), Threat Intelligence, Natural Language Processing (NLP).