INTELLIGENT RISK MANAGEMENT SYSTEMS FOR CYBERSECURITY IN ENTERPRISES

Abstract

With the rising complexity and frequency of cyber threats, enterprises require intelligent, proactive systems that can detect and mitigate risks in real time to safeguard critical infrastructure and data. This paper presents an attack taxonomy of the proposed system, mimicking real-world attacks such as Denial of Service (DoS), Man-in-the-Middle (MitM) scenarios, and insider attacks. Some of the principal components in the system architecture include the Intrusion Detection System (IDS), the decision-making system, and the response regimen. The outputs measured in throughput, latency, packet loss, detection accuracy, and resource utilization are used to formulate simulation KPIs. Outcomes show how the system can detect, counter, and prevent risks successfully and efficiently in the overall network. Some of the evident findings involve using AI in making decisions that would eliminate false positives and increase the detection percentage. Hence, the study recommends using easily scalable artificial intelligence models to support cybersecurity frameworks and expanding simulations in the real environment.