Azure Confidential Computing Reviews

I have utilized Amazon MQ for my Python automation projects. It serves as a middleware layer service for my projects, particularly when using Amazon MQ. I can state that it is one of the essential services for most projects, making it a unique and very important service.

I have private data and want to prevent others, including Microsoft, from seeing it, I have control. By setting up a landing zone for sovereignty, the data is stored in MCFS landing zones. This data is encrypted in use, at rest, and in transit. While the data is in use, no third-party user or Microsoft can view, capture, or read it. 

These landing zones are for specific customers. When I enter an MCFS zone, my data remains strictly confidential, and unauthorized users cannot see it. This is the basic principle. 

Confidential computing is used for data in use. There are three types of data: in-use, in-transit, and at-rest data. At rest means the stored data is encrypted. In-use data means when the data is in memory, it remains confidential to other users, cloud users, or hackers, preventing data leakage or hacking. None can capture my data.  

Confidential computing involves data in use. This serves as a reference architecture, not just a unique service. Compliance and governance are core concepts of sovereignty. Sovereignty combines compliance rules with local compliance regulations. 

Each country can develop its compliance rules and integrate them into Azure. Globally, countries have developed their compliance rules and sovereignty compliance packs for Azure. For example, GDPR is implemented in some countries. In Turkey, there is a regulation named Kaveh Kaka. In Italy, there are specific data classification and compliance rules. They package their computing and compliance rules for Azure, allowing access to Italian compliance rules in Azure. If I implement Microsoft Cloud for Sovereignty landing zones, I see specialized and customized computing packages.

There is an improvement area for data in use. It is still a research subject globally. Scientists are working to improve the efficiency and latency of data in use algorithms. For example, they are exploring homomorphic cryptography for encrypting data in memory. I should decrypt it to read all memory contents, which causes latency during data processing. Developing algorithms to address this is an improvement area for confidential computing worldwide.

Over the past twelve months, I have used Azure more often than AWS.

It is highly stable.

The solution is scalable.

They provide enormous support to MCFS, and their support is very high.

Neutral

If I count the benefits of the MCFS, the first is confidentiality. This cannot be counted as profitability or money-related benefits, however, the benefit of Microsoft Cloud for sovereignty is boundless. For example, data is strictly protected, making the benefit of MCFS very high.

The setup contains an overhead, especially for the nodes used in implementing a landing zone, MCFS landing zone. These nodes are specialized for confidential computing, resulting in some overhead. It is naturally more expensive compared to the general use cloud mode. Comparing it to other standard cloud compute nodes, it is comparably expensive.

This is not just a setup of the classical cloud platform servers; it is an architecture that combines many services. I need to dissect Microsoft Cloud learning zones first and then set up confidential computing zones. It is not a basic service implementation and may take days to weeks for those implementing MCFS.

Although I still rarely use AWS, Azure is used for customer products. For observability purposes, I am using Insight, and for Microsoft Cloud sovereignty and confidential computing services, I also use container apps. This is a combination of services, not a single service. 

For projects, MQ is important, and using Amazon MQ is almost mandatory when using AWS. 

The current overall product rating is seven out of ten.