AI Techniques to Counter Information Security Attacks

Abstract

In the rapidly evolving landscape of information security, traditional defence mechanisms often fall short against sophisticated cyber threats. Artificial Intelligence (AI) has emerged as a pivotal technology in enhancing the resilience of information systems against such attacks. This research article explores various AI techniques employed to fortify information security, including machine learning, deep learning, natural language processing, and anomaly detection. By leveraging these advanced methodologies, organizations can proactively identify, prevent, and respond to security breaches with greater efficiency and accuracy. The study conducts a comprehensive review of current AI-driven security solutions, analysing their effectiveness in mitigating different types of cyber threats such as malware, phishing, and insider attacks. Furthermore, the research examines the integration challenges of AI technologies within existing security frameworks and assesses the ethical implications associated with AI-driven decision-making in cybersecurity. Through a mixed-methods approach, including case studies and empirical data analysis, this paper highlights the strengths and limitations of AI techniques in information security. The findings suggest that while AI significantly enhances threat detection and response capabilities, it also introduces new vulnerabilities and requires continuous monitoring and updating to remain effective. This research contributes to the ongoing discourse on leveraging AI for robust information security strategies, providing actionable insights for practitioners and policymakers aiming to safeguard digital assets in an increasingly interconnected world.