Large data-triggered security revolution: Identity Management in the Digital World (1)

Today, data is growing exponentially. Some experts have recently suggested that the rate of growth is equivalent to placing a grain of rice on the first grid of a chess board, the second of which is the square grain of its number, and the third to place its number at three square meters. When placed in the 64th lattice, the last grain of rice will be equal to 1000 times times the annual output of the whole world. 498) this.width=498 ' OnMouseWheel = ' javascript:return big (This) ' style= ' width:320px; height:187px "border=" 0 "alt=" large data triggered by the security revolution: in the digital World of Identity Management "Width=" "height=" "src=" http://s2.51cto.com/wyfs02/ M00/2a/95/wkiol1oesluj1qsgaaa0_n1jy1e518.jpg "/> Today's industries, including healthcare, finance, retailing and government agencies, are facing the question of how best to use the vast amounts of data they collect. The large data service providers provide a variety of applications to facilitate the analysis of large amounts of data, the extraction of great value insights, and thus able to cross departments, cross business functions to help the development of all walks of life. This convergence has led to a rapid increase in the volume of large data use in all sectors of the economy. However, it is clear that as large data applications connect to the database through the network environment, the most sensitive information of the enterprise is not secured by the same level of priority. To ensure that hackers are protected from the risk of data theft, companies should take the advantage of moving to the full use of large data, as well as the need for relevant security measures to protect the integrity of their data assets. Process automation to get valuable insights from large data analysis, large datasets are divided into smaller, higher-fiber analysis components, processed separately by a Hadoop cluster, and then regroup to produce valuable information. The process is almost completely automated, so a large number of machines are needed to communicate with the machine (Machine-to-machine) over the network. Several levels of authorization occur in the infrastructure of Hadoop; Ø Access to the Hadoop cluster Ø Cluster Access data sources these authorizations are often based on an SSH (Secure Shell) key and are ideal for using Hadoop. Automated Machine-to-machine communication is supported for its security level. Many popular cloud-based based Hadoop services also use SSH as a way to access the Hadoop cluster. Ensuring that identity in a large data environment is granted is a high priority, but it is also challenging. For those who wish to make use of large data analysis, the following questions should be considered: 1. Who should be responsible for establishing a large data analysis mandate? 2. Are these authorizations properly managed? 3. Who can access these specific authorizations? 4. What happens if the original authorized creator leaves the job? 5. "Need to Know" Security rules directly affect access authorization levels? These issues are not just for big data. In fact, these issues are becoming more important as data center automation business processes increase. Automated Machine-to-machine Transactions account for 80% of all communications in the data center, while most administrators focus on 20% of the traffic associated with the employee account. Some industries that rely heavily on data, such as finance and cloud-based services, typically have a 4:1 per cent ratio of machine-based identification to human-computer interaction. So why is this big identification set overlooked? It is clear that the sense of urgency in dealing with Machine-to-machine identity management has increased as the volume of large data has risen. 1 2 Next >> view full-text navigation page 1th: Process automation page 2nd: Business Crisis Original: Large data-triggered security revolution: Identity Management in the Digital World (1) Return to network security home