Volume 14, Issue 03 (March 2025)

AI Driven Hybrid Multi Cloud Governance Strategy

DOI : 10.17577/IJERTV14IS030192

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Sunil Kattikar, 2025, AI Driven Hybrid Multi Cloud Governance Strategy, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 14, Issue 03 (March 2025),

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AI Driven Hybrid Multi Cloud Governance Strategy

Sunil Kattikar

Denver, USA

Abstract Cloud computing has become the backbone of modern enterprises, yet uncontrolled cloud spending and governance challenges persist. This paper presents an AI-driven model for cloud optimization and governance, integrating AI-powered FinOps for cost forecasting and optimization, AI-driven policy enforcement for governance compliance, and a self-adapting governance model that dynamically adjusts cloud configurations. The framework addresses data complexity, cost management, security risks, compliance, and ethical AI challenges while considering trends in generative AI, multimodal AI, and AI- driven cloud and edge computing. The proposed model delivers ROI through cost-effective AI deployment, continuous monitoring, and ethical AI practices. Implementation includes an AI security and privacy framework, optimized AI performance, and compliance assurance.AI-based multi-cloud governance leverages AI/ML to optimize cloud operations, enforce policies, enhance security, and ensure compliance across AWS, Azure, GCP, and on-premises environments. The strategy consists of various components, each addressing a specific governance challenge. The AI-driven DevEx Cloud Governance Model aims to enhance developer experience (DevEx) by integrating AI into cloud governance. It improves DevOps, quality engineering, and platform engineering for Kubernetes-based multi-cloud workloads. This model ensures seamless development workflows, automated policy enforcement, optimized workload distribution, and enhanced security. The paper includes an architecture diagram, a flow diagram, and real-world implementations.

KeywordsAI, Nutanix, AWS, Vmware, GCP, Azure, Finance, Cloud governance framwork.

INTRODUCTION

Cloud computing provides scalability, flexibility, and efficiency but introduces challenges such as uncontrolled spending, governance enforcement, and security risks. AI- driven solutions offer a promising approach to address these challenges by providing cost forecasts, enforcing governance policies, and dynamically adapting configurations. This research presents a comprehensive AI model integrating FinOps, policy enforcement, and self-adaptive governance to optimize cloud operations while ensuring compliance, security, and ethical AI deployment.

CURRENT CHALLENGES IN CLOUD GOVERANCE

  1. Interoperability Issues: Different cloud providers have distinct APIs, pricing models, and governance frameworks, making unified management complex.

  2. Lack of Standardized Policies: Organizations struggle to enforce consistent security, compliance, and cost governance policies across multiple cloud environments.

  3. Data Residency and Compliance: Regulatory requirements for data storage vary across regions, complicating governance in multi-cloud setups.

  4. Visibility and Monitoring Limitations: Managing and monitoring workloads across multiple cloud providers often leads to fragmented visibility, making it difficult to optimize resources and enforce policies.

  5. Cost Management Complexity: Cloud pricing structures and billing models differ between providers, creating challenges in cost forecasting, allocation, and optimization.

  6. Security and Risk Management: Varying security controls across cloud platforms increase the risk of misconfigurations, data breaches, and compliance violations.

  7. Dynamic Workload Distribution: Multi-cloud environments require dynamic workload orchestration to balance cost, performance, and compliance, which is difficult without AI-driven automation.

OPPORTUNITIES FOR HYBRID MULTI CLOUD GOVERNANCE

  1. Enhance Developer Experience: Leverages AI to write better, faster code, improved testing & agile faster releases.

  2. AI-Driven Unified Governance: Leverages AI to create a centralized governance framework across multiple cloud providers.

  3. Automated Compliance and Policy Enforcement: Uses AI to standardize policies and automate compliance checks.

  4. Cross-Cloud Cost Optimization: AI-powered FinOps enables real-time cost tracking and automated cost savings recommendations.

  5. Improved Security Posture: AI-driven anomaly detection enhances security monitoring across hybrid and multi- cloud environments.

  6. Adaptive Workload Distribution: AI automates workload placement decisions to optimize cost, performance, and regulatory requirements.

  7. Enhanced Visibility and Monitoring: AI analytics provide real-time insights into cloud resource utilization and governance status.

  8. Self-Healing Cloud Governance: AI dynamically adjusts governance policies based on evolving compliance requirements and business needs.

    AI CLOUD GOVERNANCE USE CASES

    1. Uncontrolled Cloud Spending: AI-powered FinOps forecasts costs, detects anomalies, and recommends optimizations.

    2. Governance and Compliance Challenges: AI-driven policies enforce regulations and security controls automatically.

    3. Cloud and On-Prem Cost Management: AI optimizes hybrid and multi-cloud operations for cost efficiency.

    4. Performance and Scalability: Adaptive governance ensures optimal resource allocation.

    5. Security and AI Model Risks: AI-driven cybersecurity mitigates threats and ensures secure deployments.

    6. Compliance and Ethical AI Challenges: Ensures AI fairness, transparency, and regulatory adherence.

    7. Enhance Developer Experience: Leverages AI to write better, faster code, improved testing & agile faster releases.

      RELATED WORK

      AI-driven governance models have been explored extensively in cloud computing and DevOps automation. Various research initiatives and industry implementations highlight AI's role in automating policy enforcement, optimizing workload distribution, and enhancing security monitoring. Related studies include:

      AI-based policy engines for multi-cloud governance. Predictive analytics models for cost optimization in hybrid cloud environments.

      AI-driven Kubernetes workload management strategies. Existing solutions from major cloud providers, such as AWS Control Tower, Azure Policy, and Google Anthos, provide governance frameworks, but integrating AI for self-adapting governance remains an area of active research and development. Existing cloud cost optimization strategies focus on rule-based policies, but they lack adaptive AI-driven insights. Prior research on AI in governance addresses compliance but does not integrate FinOps-based forecasting. This paper extends these approaches by combining AI-driven FinOps, policy enforcement, and dynamic governance for holistic cloud optimization.

      The AI-Driven Project Governance Toolbox" by Ken Martin

      [2]: This book explores how AI can enhance project management and governance, offering tools and methodologies for integrating AI into governance frameworks. It provides practical guidance on leveraging AI to improve decision- making and project outcomes.

      Multi-Cloud Handbook for Developers" by Subsh Natarajan and Jeveen Jacob [3]: This comprehensive guide delves into

      Operationalizing Multi-Cloud Environments: Technologies, Tools, and Architectures" by Kevin L. Jackson and Scott Goessling [4]: This book discusses various aspects of the multi-cloud paradigm, focusing on the motivations for adopting multi-cloud strategies, the challenges involved, and the technologies and tools that facilitate effective multi-cloud operations.

      Data-Driven Governance Through AI, Digital Marketing, and the Interplay with Data" by Bhuvan Unhelkar [1]: This resource covers topics such as behavioral governance, digital transformation, and the surveillance economy, making it a valuable guide for policymakers, technology professionals, and industry leaders interested in the intersection of AI and governance.

      Cloud Service Management and Governance" by Enamul Haque [5): This book provides insights into managing and governing cloud services, discussing frameworks and best practices essential for effective cloud governance. It serves as a practical guide for implementing governance strategies in cloud environments.

      ARCHITECTURE OVERVIEW

      The proposed AI model consists following core components:

      1. AI-Driven FinOps Optimization: Predicts usage trends, suggests cost-saving opportunities, and automates cloud resource right-sizing.

      2. AI-Driven Policy Enforcement: Ensures governance compliance through automated policy enforcement.

      3. Self-Adapting Governance Model: Dynamically adjusts cloud configurations based on performance, cost, and compliance factors.

      4. Improved Security Posture: AI-driven anomaly detection enhances security monitoring across hybrid and multi-cloud environments.

      5. Adaptive Workload Distribution: AI automates workload placement decisions to optimize cost, performance, and regulatory requirements.

      6. AI-Driven Unified Governance: Leverages AI to create a centralized governance framework across multiple cloud providers.

      7. Hybrid and Multi-Cloud Governance Complexity: Manages governance across AWS, Azure, GCP, and on-prem environments while ensuring interoperability and compliance.

designing, deploying, and managing cloud-native applications viii. Enhanced Visibility and Monitoring: AI analytics provide

across multiple cloud platforms, including AWS, Azure, and GCP. It emphasizes best practices for achieving interoperability and optimizing workloads in multi-cloud environments.

real-time insights into cloud resource utilization and governance status.

  1. Self-Healing Cloud Governance: AI dynamically adjusts governance policies based on evolving compliance requirements and business needs.

    Fig.1 High-Level Architecture AI Cloud Goveranance

    LOGICAL ARCHITECTURE OVERVIEW

    1. Data Ingestion Layer: Collects cloud usage data from AWS, Azure, and GCP APIs.

    2. AI Processing Layer: Uses machine learning models for cost forecasting, anomaly detection, and optimization.

    3. Governance Adjustment – Policy Enforcement Layer: Implements compliance rules and governance policies dynamically.

    4. Hybrid and Multi-Cloud Governance Layer: Ensures seamless policy enforcement across multiple cloud providers and on-prem environments.

    5. Security & Anomaly Detection: AI enhances security monitoring and automates incident response.

    6. Monitoring & Reporting: AI dashboards provide real- time insights and automated alerts.

    7. Action Layer: Suggests and executes cost-saving recommendations, policy updates, and security configurations.

      Fig.2 High-Level logical Architecture AI Cloud Goveranance

      AI CLOUD GOVERNANCE MODEL

      AI-Driven Cloud Governance Components:

      1. AI-Driven FinOps Optimization

        1. Predicts usage trends using AI/ML algorithms.

        2. Suggests cost-saving opportunities based on past consumption patterns.

        3. Automates cloud resource right-sizing and scaling.

      2. AI-Driven Policy Enforcement

        1. Ensures governance compliance by continuously monitoring cloud usage.

        2. Implements automated policy enforcement based on regulatory and security standards.

        3. Detects policy violations and remediates them autonomously.

      3. Self-Adapting Governance Model

        1. Dynamically adjusts cloud configurations based on performance, cost, and compliance factors.

        2. Uses reinforcement learning to refine governance rules over time.

      4. Improved Security Posture

        1. AI-driven anomaly detection enhances security monitoring.

        2. Uses behavior analytics to identify and mitigate threats.

        3. Automates incident response and security policy adjustments.

      1. Adaptive Workload Distribution

        1. AI automates workload placement to optimize cost, performance, and compliance.

        2. Uses predictive analytics to forecast demand and reallocate resources dynamically.

      2. AI-Driven Unified Governance

        1. Leverages AI to create a centralized governance framework across multiple cloud providers.

        2. Automates governance enforcement across AWS, Azure, GCP, and on-prem environments.

        A multinational corporation implemented AI-based governance across AWS, Azure, and GCP, reducing policy violations by 40% using automated policy checks and remediation.

      3. Hybrid and Multi-Cloud Governance Complexity

        1. Ensures interoperability across different cloud platforms.

        2. Automates compliance tracking for hybrid environments.

        3. Uses AI to optimize multi-cloud cost management.

    8. Enhanced Visibility and Monitoring

      1. AI analytics provide real-time insights into cloud resource utilization and governance status.

      2. Uses intelligent dashboards to visualize cost, security, and compliance data.

      3. Implements AI-driven alerts for anomalies and inefficiencies.

        A financial institution utilized AI-powered monitoring dashboards to detect unauthorized cloud access, leading to a 30% improvement in security incident response time.

    9. Self-Healing Cloud Governance

      1. AI dynamically adjusts governance policies based on evolving compliance requirements and business needs.

      2. Detects misconfigurations and remediates them automatically.

      3. Uses AI-driven automation for proactive governance enforcement.

AI CLOUD GOVERNANCE CODE

Fig.3 High-Level Code example AI Cloud Goveranance

AI-DRIVEN DEVEX CLOUD GOVERNANCE

The AI-driven DevEx Cloud Governance Model aims to enhance developer experience (DevEx) by integrating AI into cloud governance. It improves DevOps, quality engineering, and platform engineering for Kubernetes-based multi-cloud workloads. This model ensures seamless development workflows, automated policy enforcement, optimized workload distribution, and enhanced security.

Fig,5 DevEx cloud governance

AI-Based Developer Experience Enhancement:

  1. Automates development workflows and optimizes CI/CD pipelines.

  2. AI-driven insights to improve code quality and performance.

AI-Enhanced DevOps:

  1. Automates infrastructure provisioning and policy enforcement.

  2. Predictive analytics for proactive issue resolution.

IJERTV14IS030192

Quality Engineering Optimization:

  1. AI-powered automated testing and anomaly detection.

  2. Continuous monitoring for performance and security compliance.

Platform Engineering for Kubernetes Multi-Cloud:

  1. AI-driven workload placement and resource scaling.

  2. Ensures interoperability across AWS, Azure, and GCP Kubernetes clusters.

    CONCLUSION

    This paper presents the AI-driven hybrid multi cloud, DevEx Cloud Governance Model enhances developer experience, optimizes DevOps workflows, improves quality engineering, and streamlines platform, data engineering across Kubernetes-based multi-cloud environments. By leveraging AI, organizations can achieve.

    Improved efficiency through automation of workflows and infrastructure management.

    Enhanced security and compliance with AI-driven anomaly detection and policy enforcement.

    Optimized resource utilization using AI-powered workload distribution and predictive analytics.

    This model represents a significant step towards autonomous cloud governance, where AI continuously refines policies, enforces security, and optimizes resource allocation across hybrid and multi-cloud environments.

    REFERENCES

    1. Data-Driven Governance Through AI, Digital Marketing, and the Interplay with Data" by Bhuvan Unhelkar.

    2. The AI-Driven Project Governance Toolbox" by Ken Martin

    3. Multi-Cloud Handbook for Developers" by Subash Natarajan and Jeveen Jacob

    4. Operationalizing Multi-Cloud Environments: Technologies, Tools, and Architectures" by Kevin L. Jackson and Scott Goessling:

    5. Cloud Service Management and Governance" by Enamul Haque [5).

IJERTV14IS030192