Hybrid Intelligence for Adaptive AIOps
Keywords:
Distributed Cloud Infrastructure, Edge Fog Cloud Continuum, Hybrid AI Models, AIOps Automation, Intelligent Workflow Orchestration, Elastic Data Ingestion Pipelines, Streaming Sensor Data Processing, Fault Prevention And MTTR Reduction, Service Resilience Engineering, Data Loss Prevention Strategy, Data Leakage Detection, Intrusion Detection Systems, Regulatory Compliance Monitoring, Hybrid Machine Learning And Symbolic Reasoning, Knowledge Based Security Analytics, Automated Incident Response, Cloud Security Governance, Privacy Preserving Monitoring, Cyber Risk Mitigation, Intelligent Cloud Operations.Abstract
Distributed cloud infrastructures span the edge, fog, and cloud, integrating multiple services and introducing diverse challenges in automation and monitoring. Automation seeks to simplify operational tasks by establishing rules for migration, scaling, and recovery that minimize manual intervention, while AIOps-driven monitoring supports fault prevention and reduces Mean Time to Recovery (MTTR), thereby strengthening service resilience. Hybrid AI — which combines machine learning (ML) with symbolic reasoning and optimization techniques — offers intelligent solutions to these challenges and is gaining increasing traction within AIOps.
At the same time, the rising frequency of severe data breaches is pushing organizations toward robust Data Loss Prevention (DLP) strategies to reduce the risk of sensitive data exposure. DLP focuses on detecting potential breaches and preventing the unauthorized transfer of critical assets. The Cybersecurity and Infrastructure Security Agency (CISA) notes that well-configured DLP controls can shrink an organization's threat surface, while proper implementation and enforcement also help identify regulatory compliance issues early. Detecting and mitigating data leakage further supports compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA), limiting the impact of any incidents that do occur.
This work explores how Hybrid AI enables intelligent automation and monitoring of workflows across distributed clouds — facilitating orchestration, supporting recurrent decision logic, delivering data-driven insights, and automating response actions. Its application to workflow automation is demonstrated through automated elastic data ingestion pipelines (ADIPs) that ingest streaming sensor data and migrate it to the cloud for storage, post-processing, and consumption. In parallel, we evaluate AIOps in distributed cloud environments through the lens of data leakage prevention and intrusion detection, proposing solutions that draw on diverse information sources — network flows, endpoints, databases, and cloud resources — and combine supervised, unsupervised, and knowledge-based methods for detection, analysis, and mitigation. Together, these use cases illustrate the practical benefits and limitations of Hybrid AI and offer guidance for its effective real-world application.
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