Cloud

'Sovereign washing' refers to the misleading marketing practice where cloud service providers brand their offerings as 'sovereign' or compliant with digital sovereignty requirements, but in reality, they do not provide full legal and technical control over data. This often happens when US-based companies claim to offer 'sovereign clouds' in Europe, but their services remain subject to US laws like the CLOUD Act, allowing US authorities access to data regardless of its physical location.

True digital sovereignty requires full legal and technical control over data, including where it is stored and who can access it. Major global cloud providers, especially US-based ones, often operate under laws like the CLOUD Act and FISA, which can compel them to provide data access to US authorities regardless of data location. Additionally, their cloud architectures are designed for security but not necessarily for sovereignty, making it challenging to guarantee compliance with specific regional regulations and to prevent unauthorized data access or control.

Enterprises often encounter challenges such as cloud sprawl and security risks. Cloud sprawl refers to the uncontrolled proliferation of cloud services, leading to inefficiencies and increased costs. Security risks arise from inadequate management of access controls and data protection, which can expose organizations to cyber threats.

To regain control, enterprises should develop a comprehensive cloud strategy that includes a clear migration plan, strong governance frameworks, and robust security measures. Implementing automation for network management, ensuring proper network segmentation, and leveraging AI for operational efficiency are also crucial. Additionally, understanding the shared responsibility model with cloud service providers is essential for maintaining security.

Cloud repatriation is the process of transferring data, applications, or workloads from a public cloud environment back to on-premises data centers or private clouds. Organizations pursue repatriation primarily to gain better control over their IT infrastructure, reduce unexpected or escalating cloud costs, improve performance and latency, enhance data security and compliance, and avoid vendor lock-in. This shift often reflects challenges encountered with public cloud solutions such as cost overruns, performance limitations, and regulatory compliance needs.

AI workloads often require predictable, high-performance computing environments that can be more cost-effectively managed on-premises, prompting organizations to repatriate these workloads. Cost concerns arise from unexpected cloud expenses such as data transfer fees and scaling costs, which can exceed initial estimates. Security and compliance requirements, especially in regulated industries, motivate organizations to keep sensitive data on-premises to maintain tighter control over data residency, lifecycle management, and regulatory adherence. Together, these factors contribute to a hybrid cloud approach where organizations balance public cloud benefits with on-premises control.

Shelby is a decentralized hot-storage network and protocol designed for continuous, high-frequency Web3 workloads such as streaming video, AI pipelines, and social timelines. Unlike traditional cloud storage, which often locks developers into closed systems, Shelby offers cloud-like speed with full decentralization and verifiable data flows, enabling developers to build next-generation applications without compromising performance or openness.

Shelby was developed through a collaboration between Aptos Labs and Jump Crypto. Aptos Labs brings experience from scaling consumer-grade blockchain infrastructure and projects like Instagram at Meta, providing fast coordination and flexible logic. Jump Crypto contributes expertise in real-time systems and instant trading, ensuring Shelby delivers low-latency reads and parallel data flows, combining Web3 economics with Web2 speed.

Autonomous cloud computing refers to cloud systems that operate with minimal or no human intervention by using machine learning and artificial intelligence to automate tasks such as tuning, security, backups, and updates. Unlike traditional cloud computing, which requires manual management and oversight, autonomous cloud systems self-manage, self-optimize, and self-heal to improve efficiency, security, and reliability.

The main benefits of autonomous cloud computing for businesses include enhanced security by reducing human errors, improved performance through continuous self-tuning, cost reduction by automating routine management tasks, and enabling staff to focus on higher-level strategic priorities. Autonomous cloud systems also provide greater reliability and flexibility, adapting rapidly to changing conditions and maintaining system stability.

Cloud repatriation refers to the process of moving workloads and data from public cloud environments back to private clouds, on-premises data centers, or colocation facilities. Organizations consider repatriation due to factors such as escalating costs, data privacy and sovereignty concerns, security, compliance requirements, performance issues, and the desire to avoid vendor lock-in. Many companies that rapidly adopted public cloud services during the COVID-19 pandemic are now reassessing their strategies to better align with evolving business needs and to gain more control and predictability over their infrastructure and data.

The transition towards cloud repatriation is progressing slowly due to the complexities involved in moving workloads from public clouds back to private or on-premises environments. These complexities include technical challenges of migration, integration with existing systems, ensuring compliance with data sovereignty laws, managing performance and latency requirements, and balancing cost efficiency. Additionally, many organizations are still dependent on cloud-native applications and services, which can complicate repatriation efforts. The shift requires careful planning and strategic alignment with business goals, which contributes to the gradual pace of adoption.

The responsibility for securing data in cloud databases is shared between the cloud service provider (CSP) and the customer. CSPs are responsible for securing the cloud infrastructure, while customers must ensure the security of their data and applications within the cloud.

Using cloud security tools does not automatically ensure compliance with security regulations. While cloud providers may offer compliant services, proper configuration and management by the customer are necessary to maintain compliance.

Organizations often face unexpected management complexities and cost overruns in cloud environments, which can significantly exceed initial projections. Challenges include cloud cost variance, overprovisioning, poor scalability, and difficulties in measuring the value and ROI of cloud investments. Many business leaders and CFOs lack confidence in their ability to accurately measure cloud ROI, making it hard to justify or optimize cloud strategies.

Agentic AI—AI systems capable of autonomous decision-making and action—can help organizations better manage cloud environments by automating cost optimization, resource allocation, and anomaly detection. By leveraging predictive analytics and real-time data processing, agentic AI can reduce cloud waste, improve scalability, and provide clearer visibility into cloud spending and ROI, addressing the core issues that currently hinder cloud financial management.