Adaptive AI for Autonomous Cloud Orchestration
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 integrate multiple services at the edge, fog, and cloud, posing diverse automation and monitoring challenges. Automation aims to simplify tasks and establish migration, scaling, and recovery rules to minimize manual intervention. Monitoring AIOps enables fault prevention and reduced Mean Time to Recovery (MTTR) while assuring service resilience. Hybrid AI blends machine learning (ML) models with symbolic reasoning and optimization techniques to formulate intelligent solutions for these scenarios. Use of such technology is gaining popularity for AIOps. The growing frequency of severe data breaches urges organizations to adopt a Data Loss Prevention (DLP) strategy to reduce the risk of sensitive data exposure. DLP aims to detect potential data breaches and prevent the unauthorized transfer of those assets. According to the Cybersecurity and Infrastructure Security Agency (CISA), configuring DLP controls can help organizations reduce threat surface visibility. Proper implementation and enforcement of DLP controls also offer organizations the ability to identify regulatory compliance issues early. Furthermore, detecting and mitigating data leakage incidents contribute to organizations' ability to comply with regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and reduce their impact.
Hybrid AI models enable intelligent automation and monitoring of workflows in distributed clouds by facilitating orchestration, supporting recurrent decision logic, providing data-driven insights, and automating response actions. The use of Hybrid AI for workflow automation is evaluated through automated elastic data ingestion pipelines (ADIPs) capable of ingesting streaming sensors data and migrating it to the cloud for storage, post-processing, and consumption. Moreover, an evaluation of AIOps in distributed cloud environments is performed, addressing data leakage prevention and intrusion detection incidents. The proposed solutions leverage different information sources such as network flows, endpoints, databases, and cloud resources and adopt supervised, unsupervised, and knowledge-based methods for detection, analysis, and mitigation. The presented approaches constitute practical use cases that explore benefits and limitations of Hybrid AI and provide guidance for its effective application.
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