Strategic Management of AI-Powered Cybersecurity Systems: A Systematic Review

Abstract

Artificial intelligence (AI) has changed the way we protect ourselves from cyber threats by giving us better tools for finding threats, lowering risks, and responding in real time. As cyber threats get more complicated and widespread, it is important to strategically integrate and manage AI-powered technologies in cybersecurity frameworks. This systematic review brings together the most recent studies on how to strategically manage AI-driven cybersecurity systems. It points out the best ways to use them across industries, as well as their pros and cons. 87 peer-reviewed articles from 2015 to 2024 were examined that were found in databases such Scopus, IEEE Xplore, SpringerLink, and ScienceDirect. We used the PRISMA standards to do this. The review finds five main themes: (1) AI algorithms for finding and classifying threats; (2) AI governance and risk management; (3) problems with integrating AI into security frameworks in organizations; (4) ethical and legal issues; and (5) strategic deployment and scalability. The results show that AI makes threat intelligence and adaptive reaction much better, but companies have problems with explainability, data privacy, and algorithmic bias. Human-AI collaboration, continuous learning loops, and regulatory compliance frameworks are all examples of strategic management techniques that make AI integration more effective. The evaluation also stresses that to make good AI strategy, you need to have knowledge in a variety of fields, such as data science, cyber law, and behavioral analytics. In conclusion, strategic management is very important for getting the most out of AI in cybersecurity. To create cybersecurity ecosystems that are strong, flexible, and ethical, we need to take a proactive strategy that includes aligning policies, training the workforce, and managing the lifecycle.