Context-Aware Anomaly Detection in Distributed Enterprise Information Infrastructures

Distributed enterprise infrastructures generate extensive telemetry from identities, endpoints, networks, cloud workloads, application services, and business processes. Anomalies in these environments cannot be assessed reliably from isolated measurements because the same event may indicate routine maintenance, capacity stress, policy change, or malicious activity depending on operational context. This article develops a conceptual architecture for context-aware anomaly detection that connects heterogeneous observations with asset roles, temporal dependencies, identity privileges, workflow states, and organizational risk. The discussion examines representations of context, feature construction, adaptive baselines, distributed correlation, alert interpretation, governance, and evaluation. Three comparative tables summarize contextual evidence, architectural trade-offs, and assessment criteria. Rather than reporting an experimental benchmark, the article proposes a reproducible analytical framework for designing and assessing detection pipelines under changing enterprise conditions. Particular attention is devoted to limiting false positives without concealing low-frequency attacks, handling incomplete telemetry, and supporting decisions that security analysts can audit. The resulting approach treats detection as a socio-technical process in which statistical deviation, causal plausibility, and operational consequences must be considered together.