AI-Powered Analytics Architectures for Enterprise Security and Operational Intelligence
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Abstract
Cloud services, applications, networks, endpoints, and business services all generate large amounts of non-uniform security and operational data that can reside as well as be utilized by and reach the environment. These variegated streams of data cannot be related by the traditional analytics design to identify looming risks and avail timely operational insights. The current research paper introduces an AI-based analytics platform to enterprise level operations and security intelligence platform which embraces machine learning, smart data processing and real-time analytical functions under one platform. The proposed architecture will integrate with multi-source data ingestion, data normalization, feature engineering, anomaly detection, which are founded on AI, behavioral analysis, the threat intelligence correlation, and operational event processing. The hierarchical analysis framework will enable the security and operation data to be transformed to the contextualized data and make appropriate choices and continuous monitoring. Machine learning models can be used to identify abnormal behavior, prioritization of security events, identification of potential threats and establishing a relationship between infrastructure and business processes. The architecture is also seen to have cloud-native observability, centralized observability, automated feedback and alerting to enhance analytical flexibility. This paper discusses the aspects of architecture, the flow of data and the prospects of AI, integration principles, as well as the benefits of the functionality of the proposed approach. Based on the findings, AI-based analytics will be able to increase the visibility of various business security activities, increase the promptness of detecting incidents, resultant heightened awareness of the operations, and manage complex and dynamic enterprise landscapes more proactively.
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