AI-DRIVEN GOVERNANCE FOR MULTI-CLOUD COMPLIANCE: AN AUTOMATED AND SCALABLE FRAMEWORK ↔ AI-BASED CLOUD GOVERNANCE FOR MULTI-CLOUD COMPLIANCE MANAGEMENT

https://iaeme.com/Home/journal/IJCC 1 38 editor@iaeme.com International Journal of Cloud Computing (IJCC) Volume 2, Issue 1, Issue 4, January-December 2021, January-June 2024, pp. 1-13, 38-51, Article ID: IJCC_01_04_001 IJCC_02_01_003 Available online at https://iaeme.com/Home/issue/IJCC?Volume=1&Issue=4 https://iaeme.com/Home/issue/IJCC?Volume=2&Issue=2 Impact Factor (2024): 1.80 (Based on Google Scholar Citation) Journal ID: 2563-4512; DOI: https://doi.org/10.34218/IJCC_01_04_001 https://doi.org/10.34218/IJCC_02_01_003 © IAEME Publication AI-DRIVEN AI-BASED CLOUD GOVERNANCE FOR MULTI-CLOUD COMPLIANCE: AN AUTOMATED MULTI- CLOUD COMPLIANCE MANAGEMENT Narayana Gaddam Department of Technology AND SCALABLE FRAMEWORK Omkar Reddy Polu Los Angeles CA, Innovation, City National Bank, USA. ABSTRACT This paper aims at providing a means to manage compliance and governance in a multi cloud environment using artificial intelligence (AI). Increasingly, Enterprise are landscapes. The first objective of this study is to design a governance framework based on AI for automated compliance management of a set of diverse cloud infrastructure, that is free from human errors and operational overhead. Machine learning algorithms and AI driven automation is what the methodology for the proposed application uses to optimize compliance workflows. It does a comprehensive scoping review of the imperative tools and cloud native services that can be used to support the governance and compliance processes in an effective way. The cloud environment. According to one of the key finding suggest that by integrating Ai in cloud governance regulatory compliance is improved, risk exposure is reduced and resource allocation is optimized. Specifically, the unified control frameworks can facilitate Omkar Reddy Polu Narayana Gaddam https://iaeme.com/Home/journal/IJCC 2 39 editor@iaeme.com standardization of governance process and reduce overhead of regulation [1], regulation, and the automated compliance blueprinting allows more accurate and consistent results [5]. results.

complex multi cloud environment while attending to the variations in the regulatory requirements. Keywords: Multi-cloud Governance, AI-driven Compliance, Cloud Native Applications, Machine Learning, Regulatory Compliance, Automation, Containerization, Cybersecurity Cybersecurity. Cite this Article: Omkar Reddy Polu. (2021). AI-Driven Narayana Gaddam. (2024). AI-Based Cloud Governance for Multi-Cloud Compliance: An Automated and Scalable Framework. Multi- Cloud Compliance Management. International Journal of Cloud Computing (IJCC), 1(4), 1–13. DOI: https://doi.org/10.34218/IJCC_01_04_001 1. 2(1), 38–51. https://iaeme.com/MasterAdmin/Journal_uploads/IJCC/VOLUME_2_ISSUE_1/IJCC_02_01_003.pdf I. INTRODUCTION With The rapid expansion ever increasing adoption of multi-cloud computing environments has resulted in serious questions in matters multi cloud strategies by the organizations to deliver high performance, lowest cost and to avoid vendor lock in, it is becoming more complex to manage cloud governance and abide by compliance across different cloud environments. With the use of governance, multiple cloud service providers, including AWS, Azure, and Google Cloud, compliance and risk management, caused policy enforcement. Because of different configurations, security policies and regulatory requirements imposed by different providers, compliance management in such heterogeneous environments is particularly difficult. Gaps in governance come as a result of this and are represented by security misconfiguration, data breaches, violation of compliance standards such as ISO 27001, SOC 2, GDPR. the high level traditional cloud compliance frameworks are highly dependant on the static, manual rule enforcement which tends to suffer from inability of complexity adapting with the cloud environments which are highly dynamic in nature. All these methods evolve slowly, contain error prone, and heterogeneity need constant human intervention, making them ineffectual for operating extensive, gradually developing cloud infrastructure. This research proposes an AI based cloud governance framework that is designed to provide an automatic compliance monitoring and policy AI-Based Cloud Governance for Multi-Cloud Compliance Management https://iaeme.com/Home/journal/IJCC 40 editor@iaeme.com enforcement across multi (multiple) cloud platforms which has been developed to overcome these limitations. This framework proposes the improvement of such systems [4], [9]. Increasingly, organizations are utilizing multiple cloud providers to achieve benefits compliance accuracy, speed, and scalability through the integration of cost efficiency, scalability as well as resilience, yet this trend compromises compliance, because each cloud service provider has different governance standards that prevent unified management [10]. The regulatory demands reliable [1], [8]. Artificial Intelligence (AI) offers a solution that can automate the governance tasks, optimize the compliance workflows, and proactively detect the risks with a great efficiency gain in terms of operational and compliance [2], [5], [6]. Nowadays, control model unification [1] and [5] as well as compliance blueprint optimization [8] based on machine learning models, NLP, and AI have demonstrated significant opportunities scalable cloud native architectures. This system allows detection and resolution at real time for non compliant configurations, thus helping the organizations to integrate AI keep the robust security models on the top, reduce costs in Operations, align with Industry standards, in the realm of multi-cloud management. Nevertheless, there still exist various research gaps, e.g., the lack of a complete and systematic architecture with AI for proactive compliance to span through a heterogeneous multi-cloud system [4]. Therefore, this research tries to come up with a robust, AI based framework for https://iaeme.com/Home/journal/IJCC 3 editor@iaeme.com organizations in being able to manage changing regulations as well as attain resilient, efficient and automated governance solutions. highly dynamic cloud environment. II. LITERATURE SURVEY The construction of cloud governance in multi cloud environment becomes increasingly complex due to the popularity of distributed cloud infrastructures like multi cloud and dynamic compliance requirements. Researchers have researched different ways to achieve robust cloud compliance management. The governance aspects have been contributed to by automated policy enforcement, AI driven anomaly detection and cloud native security framework, which are more efficient in their applications. Research exists that demonstrates that integrating the cloud security frameworks with the machine learning models can aid in the detection of threat and reduce the number of manual intervention [1]. In addition, the progress on the compliance automation framework has proven to be more accurate in enforcing security controls and meet industry standards like ISO 27001, SOC 2 and GDPR [2]. Recently, some rule based, traditional solution due to their success have shown similar success in dynamic cloud environment and recent advancements in AI based solutions harnessing the power of learning adaptive models (driven by feedback) along with predictive analytics, have also performed better [3]. Five major areas of this literature review are the AI based compliance automation, NLP for policy interpretation, real time cloud monitoring tools, scalable cloud native architecture and multi cloud data protection. 1. AI-Based Compliance Automation The compliance frameworks powered by AI have paved their way and changed the governance in multi cloud infrastructures. This proves that machine learning models outperform in identifying and fixing the security misconfigurations. For instance, Kumar et al. [2] propose a compliance automation system that uses decision trees and random forests as configuration of the cloud. They then showed that with testing their system and their different approaches, they were able to give a 20% reduction in false positives compared to traditional Narayana Gaddam https://iaeme.com/Home/journal/IJCC 41 editor@iaeme.com approaches. In addition, the cloud governance platforms utilize deep learning models such as CNNs and RNNs to predict the violation of compliance ahead of time [4]. These models are quite effective in detecting the high risk configurations in AWS, Azure and GCP by analyzing metadata logs, firewall settings and IAM policies. Patel et al. [3] then introduced a Omkar Reddy Polu https://iaeme.com/Home/journal/IJCC 4 editor@iaeme.com reinforcement learning based compliance engine that dynamically adapts to the changes in the policy, driven by a partial reimbursement model and was able to reduce the number of violations by 30% of deductions. Hence, integration of AI model has been important to improve the accuracy of compliance and reduce the overhead of operations. 2. NLP-Driven Policy Interpretation Through Interpretation of complicated Regulatory Policies and Compliant creation, Natural Language processing (NLP) shows to be a powerful tool. Smith et al. [1] proposed an NLP based framework to extract security controls from compliance documents which utilizes transformer models to perform this task. Consequently, they could accurately identify 92% of key security mandates among ISO 27001, HIPAA and PCI-DSS standard. This innovation was one of the solutions to this challenge because it solved the challenge of manual policy mapping so that cloud platforms can automatically generate compliance rule generation. Roy et al. [5]

data. This proves that NLP models can be used to streamline cloud governance workflows. 3. Real-Time Cloud Monitoring Tools For strategic governance to be effective, it needs to have adequate monitoring tools to enable the status of compliance to be taken at real time. Some advanced cloud native solutions are cloud security posture and automated configuration tracking solutions, e.g. AWS Config, Azure Policy, Google Cloud Security Command Center. Kumar et al. [2] further improved these monitoring tools by implementing AI based anomaly detection models to detect anomaly from compliance baseline. In fact, their system could even detect suspicious activities like unauthorized port access and misconfiguration of storage permissions with 95 percent accuracy.

Gupta et al.[6] also developed graph based anomaly detection models for multi cloud systems that visualize the resource dependencies to quickly identify the non compliant services. To ensure cross cloud infrastructure consistent governance based on the cloud services, real time monitoring tools have been well integrated. 4. Scalable Cloud-Native Architectures In The multi cloud environment, the scalability of compliance frameworks is still an important aspect. Thanks to Kubernetes, Docker Swarm and OpenShift, it is seamless and AI-Based Cloud Governance for Multi-Cloud Compliance Management https://iaeme.com/Home/journal/IJCC 42 editor@iaeme.com dynamic at the time of scaling and making policies. A research by Lee et al. proved that Kubernetes can effectively orchestrate multi cloud compliance management by distributing rule evaluation workloads to different nodes [7]. Offloading was more resilient to the system and with negligible effect on peak usage latency, we improved the peak usage latency of the system by 40%. Further to this, Patel et al. [3] leveraged scalable serverless computing solutions and compliance engines to provide low cost deployment and resource optimization. These AI-Driven Governance for Multi-Cloud Compliance: An Automated and Scalable Framework https://iaeme.com/Home/journal/IJCC 5 editor@iaeme.com advancements allow cloud governance framework to easily function regardless of the constant variations in cloud traffics with minimal performance degradation. 5. Multi-Cloud Data Protection Strategies In the data security, multi cloud governance framework is still critical. The research is done on encryption models, secure key exchange protocols and zero trust architectures to address the issue of data breach. Wong et al. [8] proposed a hybrid encryption framework in cloud-natives environment applied to improve on data security by applying AES-256 together with lattice based encryption models. This made their data well protected as the system proved to be resistant to quantum attacks. Roy et al. [5] also presented another example of such a framework, which was based on homomorphic encryption for sensitive data protection under transit between AWS, Azure, and GCP. When it comes to dealing with sensitive enterprise workloads spanning over multi cloud infrastructures, these techniques are indispensable for the data confidentiality they provide and help ensure confidentiality. III. MATERIALS AND METHOD The implementation of the AI Based Cloud Governance framework for mult cloud compliance management has been carried out using advanced hardware configuration, specialized software framework and a well structured experimental setup. It is based on the system architecture that incorporates distributed computing resources together with cloud platforms to achieve the maximum performance in multi cloud environments. The architecture of the framework consists of three layers of a multi layered architecture, namely, data ingestion, compliance analysis and automated policy enforcement.

processing. In particular, the system was deployed on a workstation with AMD Ryzen 7 5800H CPU with Radeon Graphics and 16 GB RAM, for carrying out computationally intensive AI Narayana Gaddam https://iaeme.com/Home/journal/IJCC 43 editor@iaeme.com Inference tasks. The choice of GPU significantly impacted GPU acceleration of the speeds of inference of the model, and the system was capable of analyzing complex compliance policies in a large scale cloud environment. Python 3.10 was used with the libraries that build deep learning models that help in intelligent decision making for compliance management, which are TensorFlow and PyTorch.

Furthermore, the framework included utilising cloud native tools such as K8s (for orchestrating containerized service over multiple cloud providers). Kubernetes was a good choice because it Omkar Reddy Polu https://iaeme.com/Home/journal/IJCC 6 editor@iaeme.com accommodated good workload management and auto scaling which are the two major aspects of a real time governance in a dynamically changing cloud ecosystem. The system makes use of the OpenCloudSecurity (OCS) toolkit to enable secure API interactions with cloud services and can be easily plugged into major cloud providers such as AWS, Azure or Google Cloud Platform [1]. However, this integration enabled the system to retrieve compliance data from the cloud resource configurations in real time. The experimental setup was used to evaluate the system according to its efficiency, accuracy, and scalability. The system is tested for its activities in a simulated multi-cloud environment by emulating various compliance scenarios. Because of this, we used multiple cloud instances hosted across AWS EC2, Azure Virtual Machines, and Google Cloud Compute Engine for the experimental environment. In these cases, these different security settings, access controls, and networking policies were deployed together in order to make them feel like real cloud deployments (and governance issues). The compliance framework was deployed in these environments and real time data extraction, rule enforcement and anomaly detection were carried out. To aid in the comprehensive data collection, the system used a centralized logging architecture. All cloud service logs, system events and compliance rule evaluations (including those done by CM) are collected and stored into a scalable data warehouse powered by Amazon Redshift. This simplified to query compliance violation and plot the occurrence of violations w.r.t to compliance violations of ISO 27001 and NIST 800-53 [2]. The system also used Apache Kafka based data pipeline for continuous ingestion of data with low latency. In dealing with policy violations, the framework was able to process compliance events almost in real time using this streaming architecture. The compliance rule engine was implemented as combination of natural language processing (NLP) models and a decision tree classifier. NLP models were used to process compliance policy documents to extract actionable rules and decision tree was used to classify the violations based on pre defined thresholds. For training the model, the dataset was composed AI-Based Cloud Governance for Multi-Cloud Compliance Management https://iaeme.com/Home/journal/IJCC 44 editor@iaeme.com of over 20,000 compliance policy samples gathered from publicly reachable repositories spanning breadth of industry standards, and regional regulations. The dataset was tokenized, stemmed and lemmatized to get a better model accuracy. The system was evaluated during experimentation with respect to different metrics such as precision, recall, and F1-score. Traditional compliance management solutions were surpassed since the framework could achieve average precision of 94.3% and recall score of 91.8%. To assess the system scalability, the system capacity to support concurrent compliance AI-Driven Governance for Multi-Cloud Compliance: An Automated and Scalable Framework https://iaeme.com/Home/journal/IJCC 7 editor@iaeme.com evaluations was measured by launching multiple concurrent instances of cloud services. I was able to scale up to manage more than 5K cloud resources with near no performance degradation.

multi cloud environments. Security teams using visual analytics could find the most critical compliance gaps and thus where to best start to remediate that problem. In addition, alerts were configured in Prometheus to notify administrators if breaches of high risk policies are occurring in order to react proactively. Finally, it is shown that the AI Based Cloud Governance framework is robust to keep the compliance management real time in the multi-cloud environment. “Having advanced AI models, efficient and scalable cloud native tools, data pipes, to name a few, made the combination of all of these key to such accurate and efficient compliance monitoring.” Future implementations, which integrate reinforcement learning models for generation of adaptive compliance rule generation and continuous learning in ever changing cloud ecosystems, will use it as a basis. [3]. IV. RESULTS AND DISCUSSION To demonstrate the effectiveness of the proposed AI based cloud governance framework in the compliance management aspect, the proposed framework was tested rigorously in a real time multi cloud environment. We deployed the system on AWS, Azure, and Google Cloud with different security policies and controls including access. There was a great improvement on the compliance accuracy, response time, and violation detection on the results using it, in comparison with the traditional rule based frameworks. With respect to the compliance accuracy, the average precision rate of the AI enhanced system is 94.3% and the average recall is 91.8%, superior than existing compliance automation Narayana Gaddam https://iaeme.com/Home/journal/IJCC 45 editor@iaeme.com frameworks which are mainly based on static rules [2]. However, after all, controlling complex security requirements in dynamic cloud ecosystems, traditional methods, such as AWS Config and Azure Policy, have proven only to be slightly useful. The proposed system combined NLP based policy interpretation with decision tree based classifiers to translate regulatory standards to actionable security rules while accomplishing policy enforcement as late as possible but as early as needed in real time. The use of this improved precision considerably decreased the false positive frequencies, which has always been one of the main problems in standard compliance frameworks [5]. In addition, the system was adjusted to be amenable to new security mandates, Omkar Reddy Polu https://iaeme.com/Home/journal/IJCC 8 editor@iaeme.com and there was no significant impact on delay in applying updated compliance rules on revisions of the policy. Another critical metric that was evaluated during the experiments was the system’s response time. On average, the policy enforcement latency in the framework was 2.3 seconds when it was tested to high workloads with more than 5,000 concurrent cloud instances. It contributed 35% decrease in enforcement delays than traditional cloud governance tools such as AWS Security Hub and Azure Defender [6]. This is because Kubernetes was used to integrate to enable efficient workload orchestration and ensure that resource intensive compliance checks were distributed across multiple nodes [7]. Scalable cloud native architecture was also adopted and the responsiveness of the system increased further by scaling to use dynamic resource adjustment with high demand. As a consequence, we observe better performance in practice on those large scale cloud environments that often encounter dynamic workload changes.

The experimental results further showed that the proposed system had better scalability in the multi-cloud environment. This system proves itself resilient to an ever increasing load being applied, inflicting over 10,000 concurrent API requests, with near zero impact on overall performance and latency. On the contrary, conventional compliance monitoring tools performed poorly under the same conditions [3]. With Kubernetes-based orchestration and serverless functions the framework was able to execute complex compliance evaluations at the expense of the optimal utilization of resources. It is crucial to have this scalability for organizations that might be managing multiple distributed workloads on different cloud platforms, while they need to stay compliant as a consistent policy enforcement mechanism and yet need to maintain the overall system performance. In real world testing, the framework shows practical benefits in proactively finding high risk misconfigurations of AWS IAM roles and Azure NSG (Network Security Group) rules. As an example, under a particular testing scenario, the system was able to signal an unauthorized public access to sensitive AWS S3 buckets in under 1.2 seconds. Although traditional tools like AI-Based Cloud Governance for Multi-Cloud Compliance Management https://iaeme.com/Home/journal/IJCC 46 editor@iaeme.com AWS Config worked, it took them several minutes to generate an alert in similar conditions [2].

of the attack surface. NLP driven interpretation of policy for policy scrutiny reduced the compliance workflow. The system gained the compliance rules for the security controls by parsing regulatory documents, ISO 27001, GDPR and PCI-DSS, and translating them into enforceable compliance rules. This approach resulted in 40% reduction of effort compared to what is AI-Driven Governance for Multi-Cloud Compliance: An Automated and Scalable Framework https://iaeme.com/Home/journal/IJCC 9 editor@iaeme.com typically involved in configuring manual rule definition based policy. They reduced human error with this automation and implemented a more consistent policy enforcement that is critical for organizations performing in industries with strict compliance mandates [1].

What's more, dynamic compliance changes are to be superiorly resilient to the proposed system. While performing an experiment, which consisted of regular updates to AWS IAM roles and Azure storage permissions, the framework was successful in dynamically reevaluating the policy as well as enforce a updated security rule without the intervention of humans. The adaptability of this model is due to the fact that the reinforcement learning models that are part of the system were able to continually optimize policy enforcement strategies with evolving cloud configurations [3]. Adaptive learning models that were utilized by the framework guaranteed alignment with compliance in the long run even in the complex and rapidly changing environments. Further allows to enhance governance with the system’s data visualization capabilities to enable security administrators with more detailed information on compliance violations and trends. The framework gave non compliant resource, potential risk zone and policy violation visualization option in multiple cloud platforms using Grafana dashboards. Real time visibility made resources allocation more efficient for security team and enabled to respond promptly to critical threats [6]. Although the framework claims success, it has some shortcomings. Although, effective in the management of large scale compliance tasks, it degraded in performance when processing over 15,000 cloud resources, with complex rule hierarchies, simultaneously. Future improvements may include optimizing the speeds of model inference and tying the distributed computing techniques to enhance the performance under extreme workloads. In addition, the NLP based rule extraction achieved high accuracy, but misinterpreting some ambiguous regulatory statements also occurred. Better language models and larger training data from diverse compliances frameworks can be used to refine this component further [5]. Narayana Gaddam https://iaeme.com/Home/journal/IJCC 47 editor@iaeme.com In contrast to existing work, this system has 3 main features: AIdriven rule enforcement, time performance. Previous studies [2][3] have proposed the compliance automation models which are effective but do not include dynamic workload orchestration, adaptive learning models and real time visualization features that make up the proposed framework that is a complete solution for multi-cloud governance. These advances are of practical use to companies looking to enforce compliance in the cloud, but which have complex infrastructures. Omkar Reddy Polu https://iaeme.com/Home/journal/IJCC 10 editor@iaeme.com that increases accuracy of governance, lowers operational expenses, increases All in all, our proposed framework attempts to connect the reach of the dots between traditional rule- compliance department and enables governance as a service. In future, the system can be extended to include hybrid encryption models and decentralized identity management frameworks to improve the data security in multi cloud environments [8].

V. CONCLUSION AND FUTURE ENHANCEMENT An AI based cloud governance framework proved that the integration of AI model, NLP based rule extraction and scalable cloud native architectures ensured the multi cloud compliance management was proposed by us. The system is evaluated with real experimental results in real time cloud environments, it has better compliance accuracy, scalability and times with less sophistications. With an average precision of 94.3% and average recall of 91.8%, it surpassed the conventional compliance tools that are highly dependent on the static rule configuration [2]. NLP techniques in the framework helped automate compliance rule generation and did not require manual rule enforcement effort that reduced manual policy enforcement effort by 40% [5]. It also helped a lot with aligning the complex cloud configurations, which was a very tedious task usually, to the security mandates like ISO27001, SOC2 and GDPR.

Moreover, the response time resulting clearly proved the practical relevance of the system. During high demand conditions, the framework was able to identify and mitigate aspects of non compliant configuration in 2.3 seconds on average as policy enforcement latency. This performance gain in comparison with its traditional governance tools including AWS Security Hub and Azure Policy, allowed threat detection to happen more quickly and thereby reduced the risk of longer exposure to security vulnerabilities [6]. By doing so, the AI-Based Cloud Governance for Multi-Cloud Compliance Management https://iaeme.com/Home/journal/IJCC 48 editor@iaeme.com system demonstrates high resilience when scaling compliance evaluation with Kubernetes based workload orchestration and serverless computing for scaling (up to 10K concurrent cloud resources are handled without performance degradation or verbosity) [7]. This scalability is very important for the enterprises that are dealing with the complex distribution of the workload over the different cloud platforms. In real time implementation, the system benefits in handling security misconfigurations in AWS, Azure, and Google Cloud were demonstrated. In particular, the framework was tested on detecting unauthorized access of AWS S3 buckets, which was detected in less than 1.2 seconds compared to the conventional tools that took several minutes to find a similar AI-Driven Governance for Multi-Cloud Compliance: An Automated and Scalable Framework https://iaeme.com/Home/journal/IJCC 11 editor@iaeme.com unauthorized access [2]. By catching such incidents early, an organization is enabled with advanced incident response capabilities, and less time to exploit via the window of vulnerability of business critical cloud resources. Through the automated detection and correction mechanism, security team can proactively mitigate risks and hence the probability of such cloud service disruptions becomes low. However, some limitations were observed on the system in the dynamic cloud environment. Though the NLP driven policy interpretation module achieved 92% accuracy in the extraction of security controls, the security controls contained some false positives, as the policy interpretation module misinterpreted some ambiguous policy statements [1]. That said, this limitation dictates the need for a trained language model, on a number of regulatory documents, which is capable of improving the accuracy of rule extraction. In addition, stress tests were performed to make sure the system will handle more than 15,000 concurrent resources and many complex hierarchical rules inside the system while suffering only moderate performance degradation. The research can be extended to study how distributed computing techniques or optimized AI inference pipeline could be used to increase the efficiency of model deployed at very large scale. Another possible limitation is that the system relies on the use of predefined compliance datasets to train the model. In the course of experimentation, the dataset consisted of over 20k compliance policy samples, but the concentration on the key standards such as ISO27001 and GDPR can limit its utility to other emerging regional regulations or industry specific mandates. Adaptive learning model may be integrated in future research that is capable to learn and update compliance rules by itself in [3] thus resulting in broader regulatory coverage and better adaptive behaviour against evolving security frameworks. This research has wide practical impact to industries who rely on cloud computing, including industries with very strict security requirements such as healthcare, financial and Narayana Gaddam https://iaeme.com/Home/journal/IJCC 49 editor@iaeme.com governmental. The system reduces the administrative overhead and builds policy enforcement and monitoring compliance of the organization without manual intervention through automated policy and compliance monitoring. It also gives security teams improved response time as well as better capability of detecting threats, allowing teams to deal with and eradicate crucial risks faster, thus enhancing cloud infrastructure resilience.

ecosystems. For instance, hybrid encrypted frameworks based on AES-256 and lattice underlying encryption models have been shown to provide attractive solutions to this new threat Omkar Reddy Polu https://iaeme.com/Home/journal/IJCC 12 editor@iaeme.com [8] of quantum based security attacks. The proposed compliance framework can be integrated with such encryption techniques to further strengthen the data protection strategy of the enterprises working in the security sensitive domain. In addition, the NLP capabilities of this system can be expanded to include multi lingual policy interpretation models, making the system applicable in a global enterprise with a variety of regulatory frameworks. Compliance documents of different regions can be trained on the NLP model NLP Model and future iterations of the framework should be able to learn adaptability to emerging international security mandates. Additionally, incorporating a blockchain solution may also enable the compliance audit trail to be improved by offering the ability to immutably log inject each activity related to enforcement of policy. Therefore, records thus integrated would also contribute to the enhancement of regulatory transparency in the case of industries such as finance or healthcare concerning verifiable compliance records. On the whole, the AI driven cloud governance framework was able to handle the key challenges of compliance management for multi cloud using advanced AI models, scalable architecture and automatic enforcement of the policy. The system is scalable, detects in real time and has a high accuracy of the system; this is a very useful solution for any organization that wants to improve the cloud security posture without introducing huge operation overhead. By analyzing current limitations of the above mentioned technology and studying possible