Automating AI in Cybersecurity: A Comprehensive Literature Review

Abstract

The increasing complexity of cyber threats has exceed traditional security responses requiring intelligible, sophisticated approaches to securing digital assets and infrastructures. It investigates the role of Artificial Intelligence (AI) automation in cybersecurity, while machine learning and data analytics are employed to monitor real-time threats, manage predictive vulnerabilities, and automate incident response. Improved response time, problem scalability, and increased resource efficiency are some of the key advantages. The significant difference that this ongoing research brings to the already existent ones is in the introduction of novel strategies for integrating ethical frameworks aimed at minimizing algorithmic biases and providing transparency into AI-driven security programs. The text also takes into account, case studies and emerging trends while tackling such critical challenges as adversarial attacks, data integrity problems, and system integration complexities. The findings offer useful and pragmatic policy recommendations for developing elegantly adaptive and resilient cyber-secure ecosystems further embodying intervention policies in the interest of harmonizing technological innovation and ethical governance.