26-04-2017, 02:26 PM
Data leakage is the unauthorized transmission of data or information from an organization to an external destination or recipient. Data leakage is defined as the accidental or intentional distribution of confidential or confidential data to an unauthorized entity. Confidential business and organizational data includes intellectual property, financial information, patient information, personal credit card data, and other information that is business and industry dependent.
A data distributor has given this confidential data to a set of allegedly trusted agents (third parties). Some of the data is filtered and found in an unauthorized place. The distributor must evaluate the probability that the filtered data comes from one or more agents, rather than having been obtained independently by other means. When sensitive data from the distributor has been filtered by the agents and, if possible, to identify the agent that filtered the data.
Data leakage is the big challenge facing industries and different institutes. Although there are a number of systems designed for data security by using different encryption algorithms, there is a big problem of the integrity of the users of those systems. It is very difficult for any system administrator to locate the flow of data between users of the system. It creates many ethical problems in the office work environment. The industry of leak detection data is very heterogeneous as it evolved from mature product lines of leading IT security vendors. A wide array of enabling technologies such as firewalls, encryption, access control, identity management, machine learning content detectors and context-based detectors have been incorporated to provide protection against various facets of the threat of data leakage.
The competitive benefits of developing a single-window-focused silver bullet data leak detection suite are primarily to facilitate efficient orchestration of the aforementioned enabling technologies to provide the highest degree of protection while ensuring optimum tuning Of specific technologies to detect data leakage The "landscape of threats" in which they operate. This landscape is characterized by types of leakage channels, data states, users and IT platforms.
A data distributor has given this confidential data to a set of allegedly trusted agents (third parties). Some of the data is filtered and found in an unauthorized place. The distributor must evaluate the probability that the filtered data comes from one or more agents, rather than having been obtained independently by other means. When sensitive data from the distributor has been filtered by the agents and, if possible, to identify the agent that filtered the data.
Data leakage is the big challenge facing industries and different institutes. Although there are a number of systems designed for data security by using different encryption algorithms, there is a big problem of the integrity of the users of those systems. It is very difficult for any system administrator to locate the flow of data between users of the system. It creates many ethical problems in the office work environment. The industry of leak detection data is very heterogeneous as it evolved from mature product lines of leading IT security vendors. A wide array of enabling technologies such as firewalls, encryption, access control, identity management, machine learning content detectors and context-based detectors have been incorporated to provide protection against various facets of the threat of data leakage.
The competitive benefits of developing a single-window-focused silver bullet data leak detection suite are primarily to facilitate efficient orchestration of the aforementioned enabling technologies to provide the highest degree of protection while ensuring optimum tuning Of specific technologies to detect data leakage The "landscape of threats" in which they operate. This landscape is characterized by types of leakage channels, data states, users and IT platforms.