How to define cloud security in large data age

In the big data age, how does cloud security define, what the cloud security content includes, I believe many people can understand, and from the interaction between large data and cloud computing, only the most stringent security standards for data, large data can continue to enjoy the cloud provided by the scalability, flexibility and automation, This is also the big data to maintain long-term development important impetus.

In the current Internet domain, cloud computing and big data can be described as the hottest two technologies, but what is the connection between them? Cloud Computing provides the infrastructure for large data, and large data requires a flexible computing environment, which can be extended quickly and automatically to support massive amounts of data. The infrastructure cloud can deliver these requirements precisely.

When referring to cloud security policies in large data use cases, we hope that any security solution will provide the same flexibility as the cloud without impacting deployment security. However, flexibility and security are sometimes not balanced, so how to achieve a balance between security and flexibility is the need for cloud providers and large data providers to think deeply.

Deploying cloud encryption measures is considered the first step, but they are not suitable for all solutions. Some cryptographic solutions require local gateway encryption, which does not work well in a cloud-wide data environment. In addition, cloud computing providers provide cryptographic technology that allows users to keep their keys in their own hands while enjoying the benefits provided by the infrastructure cloud solution, keeping the key in a secure state. In order to get the best encryption solution for your large data environment, it is recommended that you use key encryption.

In large data, each component of the structure should be extensible, and cloud security solutions are no exception. When choosing a cloud security solution, users need to make sure that they can play a role in all the trans-regional cloud deployment points. In addition, they must be able to scale efficiently in large data infrastructures. However, because hardware security modules are not extensible and flexible enough to accommodate cloud patterns, they are not suitable for large data use cases. To achieve the necessary scalability, it is recommended to use cloud security solutions designed specifically for cloud computing.

In order for cloud security policies to be as automated as possible, users should choose virtual tool solutions rather than hardware solutions. Users need to understand that the available APIs are also part of the cloud security solution. Virtual Tools plus unused APIs provide the flexibility and automation needed in cloud-wide data usage cases.

When it comes to large data security, users should classify them according to the sensitivity of the data, and then take appropriate measures to protect them. Not all large data infrastructures are safe, and users may need to find alternatives if the data at risk is very sensitive or regulatory data.

We are talking about data security, in fact, large data security also includes the following aspects:

Scale, real-time, and distributed processing: The essential characteristics of large data (which enable large data to be resolved beyond the data management and processing requirements of previous data management systems, such as capacity, real-time, distributed architecture, and parallel processing) make it more difficult to secure these systems. Large data clusters are open and self-organization, and enable users to communicate with multiple data nodes simultaneously. It is difficult to verify which data nodes and which customers should access the information. Don't forget, the nature of large data means that new nodes are automatically connected to the cluster, sharing data and query results, and solving customer tasks.

Embedded security: In crazy contests involving big data, most development resources are used to improve the scalability, ease of use, and analysis of large data. Only a few features are used to add security features. However, you want to get the security features embedded in the large data platform. You want developers to be able to support the functionality they need during the design and deployment phases. You want security features to be scalable, high-performance, and self-organizing like large data clusters. The problem is that open source systems or most business systems generally do not include security products. And many security products cannot be embedded in Hadoop or other non relational databases. Most systems provide minimal security features, but not enough to cover all common threats. To a large extent, you need to build your own security policy.

Applications: Most applications that target large data clusters are Web applications. They take advantage of web-based technology and stateless, rest-based APIs. While it is beyond the scope of this article to fully discuss the issue of large data security, web-based applications and APIs pose one of the most significant threats to these large data clusters. They can provide unrestricted access to data stored in large data clusters after they are attacked or compromised. Application security, user access management, and authorization control are essential, as are the security measures that focus on securing large data clusters.