An Intelligent Bayesian Security Architecture: AI Threat Assessment and Real-Time Lakehouse Risk Intelligence in Low-Data Cloud Environments
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806813Keywords:
Bayesian Security Architecture, AI Threat Assessment, Dynamic Bayesian Models, Real-Time Risk Intelligence, Data Lakehouse, Low-Data Environments, Cloud Security, Streaming Analytics, Probabilistic Inference, Threat Intelligence, Security Automation, Digital Risk ManagementAbstract
Enterprises operating in low-data or fragmented cloud environments face significant challenges in detecting threats, assessing risks, and maintaining continuous situational awareness. This paper introduces an intelligent Bayesian security architecture that integrates Dynamic Bayesian inference with AI-driven threat assessment to enhance security decision-making under uncertainty. The proposed system unifies real-time streaming intelligence with lakehouse-based risk analytics, enabling scalable ingestion, harmonization, and probabilistic evaluation of diverse security signals. By leveraging hierarchical Bayesian models, the architecture continuously updates risk probabilities, compensates for sparse or incomplete data, and delivers adaptive alerts with quantifiable confidence levels. Real-time lakehouse analytics ensure seamless integration of structured, semi-structured, and event-driven telemetry, while cloud-native orchestration supports rapid scaling across distributed environments. This approach significantly improves threat visibility, reduces false positives, and provides a robust decision framework for modern enterprises operating in data-scarce or high-variability threat landscapes.
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