AI-Driven Intrusion Detection Systems: A Business Analyst’s Framework for Enhancing Enterprise Security and Intelligence

Nowadays, it is more than ever that enterprises must confront an increasing number of cybersecurity risks, which are caused by the complexity of cyberattacks, the expansion of digital properties, and the dependence on connected systems. The conventional intrusion detection systems (IDS) offer the basic level of protection but have a tendency to be constrained with fixed rule sets, false alarms, and lack the flexibility to meet the emergent threat patterns. Artificial intelligence (AI) has turned out to be a change agent in increasing the features of IDS because it is able to detect anomalies in real-time, model predictive behavior, and adapt to changing cyber threat activities. In this article, the framework of a business analyst is introduced, which incorporates AI-powered IDS into enterprise security and intelligent strategies. In addition to technical efficiency, the framework also focuses on actionable insights and prioritization of risks and data-driven decision-making made by AI-enabled systems. Methodologically, the research design is a structured research design that uses AI modeling, comparison of IDS performance metrics and also integration with enterprise intelligence tools. The findings indicate that the accuracy of detection is improved significantly, less false alarms are witnessed, and the visibility of the threat landscapes are enhanced at the organizational level. The results indicate the twofold usefulness of AI-based IDS: enhancing technical defenses on one side and strategic intelligence on the other side to serve enterprises. This paper, by comparing AI-optimized security services with the ideas of business analysts, highlights the possibilities of IDS to protect the digital infrastructure, as well as to enhance the resilience of the whole enterprise, compliance, and informed decision-making.