Every day we go to our workplaces and access to our systems using the credentials given to us by the company. Credentials are meant for keeping our devices secure. Sometimes the same credentials are used illegally for taking out the big data from the locked databases and used by invaders leading to security issues. Hence, security has become an important necessity in the corporate world today.
The professionals working in database management all work day and night in managing the database and upgrading the same. While some operations need executing to new queries. Even a data breakage may lead to data security issues where data might be lost.
Today every second, invader wait for data fracture, in order to take a chance to steal user credentials so as to attack to confidential company data, hence strict confidentiality of the user database credentials is a must.
With the increase of the invaders, data security needs to be thickened so as to save the data from being snatched by enemies. Conventional and simple security measures which we have been following for years are no longer a relevant protective measure for this. The old and traditional measures for controlling data security are no more in use today because the extent till what they provide security actually makes no sense in front of massively increasing number of attackers. Today we need a specialized way so as to give a strict defense to our databases.
Today newer and advanced methods are required in the data warehouse so as to guarantee 100 percent security to its databases. Such an approach is the Big Data User behavior analytics. This approach is an implementation of big data with machine learning algorithms so as to strengthen the node of security. This approach uses modeling in order to show what normal human behavior looks like.
Here data from the past and present are analyzed in order to find out how it will act in the future. Data from different sources are gathered, integrated, and then analyzed. It takes data from HR applications like access to server and accounts, notifications for data security issues, the manner of transactions, resources, normal session time period and networking. From the data sources, User Behaviour Analytics find out what will be the users’ action while accessing the same in the near future. And if it comes out to be different, then it will notify that something wrong has taken place and immediately the database management can take preventive measures.
Figuring Out Risks by the User Behavioural Analytics
Whenever the user behavior seems to deviate from the usual one, then that activity is called as a risk. If such irregularities are seen in user behavior in simple tasks like answering phone calls and emails then it is not usually considered as a danger. But when irregularity prevails in important sections like in account sections and important data transactions, then those irregularities are considered to be a risk. Such risks always do carry a higher impact.
The big data analytics in order to check out the risks follows these particular formulae:
Risk = Likelihood x Impact.
- Here, Likelihood tells about the possibility of the irregularities found from the user behavior. Modeling algorithms are used to find out these likelihoods.
- And the impact tells about the influence made by the irregularities of the user behavior and also on the data saved in the databases.
The risks completely depend on the misuse of credentials, access to databases without permission, user helplessness, etc. The occurrence of these irregularities more often results in huge risks of the data stored in your database servers.
In the end, UBA brings together, shows a relationship, and analyzed thousands of such characteristics, including situational data and also a third-party risk of data. The consequence is a well-off, background-conscious large dataset.
Bottom of Form
The user behavior analytics not only exposes the hidden invaders and removes the risks of data security, but also modifies the standards, calculations and also a total risk to keep the data safe and secure.
User behavior analytics for Hadoop should be mostly used in the important parts like the financial transactions and handling of confidential data. It should not be used in useless areas. For that, the user behavioral analytics should be kept offline for areas which do not require it.
Misplacing of company’s important data, stealing of the important trade strategies, frauds made during investments of huge bulks of money, all can be detected by using user behavioral analytics.
Lastly, if any user is caught to perform such risk activities that are harmful to the company, then his or her access to the certain database is immediately declined and his or her credentials will be obviously restricted.
Thus User behavioral analytics are great in the management of data security and detecting fraud as it allows companies to find out if the company’s confidential data are being unwrapped by outsiders or even by employees with cruel intentions.