InfoWatch Prediction

The main articles are:

• UBA (User Behavior Analytics)
• Security Information and Event Management (SIEM)

InfoWatch Prediction is a UBA system based on artificial intelligence technologies. It helps to notice those violations that are still outside the "visibility" of DLP, to identify deviations from the normal course of business processes. The UBA system is particularly relevant in times of instability and profound change, as employee motivations may be less predictable and consequences even more sensitive.

2023: InfoWatch Prediction 2.2 with the ability to find deviations in business processes and employee actions

On February 28, 2023 InfoWatch Ledger , they announced the release of the next version of DLP InfoWatch Traffic Monitor the 7.6 system, as well as the InfoWatch Prediction 2.2 InfoWatch Vision and 2.8 update, which use the predictive visual analysts InfoWatch Traffic Monitor 7.6 and other InfoWatch products for DLP systems and data.

The version of the UBA predictive analysis and prediction system InfoWatch threats INFORMATION SECURITY Prediction 2.2 allows you to find deviations business processes in and actions of employees. Through the use of technology artificial intelligence , millions of DLP events are automatically checked against hundreds of criteria and a rating of suspicious employees is formed with details by risk groups that require verification in the first place. In particular, people can get into groups of suspicious employees, the analysis of whose behavior tells the system about their imminent dismissal, abnormal actions, atypical external communications, etc.

In this version, a function has appeared to notify about changes in employee ratings with suspicious behavior when the risk level increases, which allows information security specialists to more quickly respond to increased risks for specific employees. Notifications are sent instantly - when the rating goes beyond the established threshold.

Analysis information by information security employees, putting employees under control and initializing service investigations are simplified when using reports in xlsx format, the unloading of which is available in this version of the product. They are formed according to the general rating or risk groups. It is also possible to make an upload data about the dynamics of anomalies. InfoWatch Prediction 2.2 allows you to control employees who have started using those that applications they have not previously applied. This innovation makes it possible to stop attempts or data theft , frauds as well as identify the facts of the use of unincorporated. software As an example, here we can cite, programmer which began to often use a graphic editor or a manager who to sales conducts videoconferences Skype with clients instead. In these protected video conferencings cases, InfoWatch Prediction 2.2 will change the employee's rating, which may serve as a reason for checking his actions by the service, safety noted Rustam Farrakhov, Director of the Product Development Department of InfoWatch Group of Companies.

Read more here.

2022: Adding Risk Groups "Performance Degradation" and "Deviation from the Business Process"

On November 22, 2022, the GC InfoWatch announced the release of an updated version of its products for visual both predictive analytics data DLP the system: and InfoWatch Traffic Monitor InfoWatch Vision InfoWatch Prediction.

The updated InfoWatch Prediction takes into account the needs and trends of the market, including automation of information security services and increased decision-making speed. Among the changes: updated capabilities for working with risk groups and support for installing all InfoWatch products on one server.

In general, UBA the InfoWatch Prediction system, which uses technologies, machine learning is designed to prevent incidents, information security reduce potential damage and manage information security risks. Predictive data analysis identifies deviations business processes in employee behavior and behavior, allowing you to predict information security threats. Artificial intelligence In the UBA system, it analyzes millions of DLP events by several hundred parameters and automatically generates a critically ranked list of risks that the information security specialist should pay attention to first.

In the presented version, risk groups "Performance reduction" and "Deviation from the business process" appeared. Both risk groups take into account data, including the Activity Monitor module for monitoring employee actions. The Productivity Reduction risk group allows you to identify deviations in employee behavior from typical behavior that can affect labor productivity. The risk group "Deviation from the business process" takes into account communication paths and also data on monitoring the actions of employees and allows you to identify deviations in behavior that may indicate an impending leak or collusion with third parties.

Predictive, analytics first of all, allows to reduce the risks associated with the human factor. Difficult from the point of view, cyber security time with an exponential increase in information security threats, you need to be able to anticipate risks as a result of deliberate or unintentional actions of personnel. InfoWatch Prediction collects data from the InfoWatch Traffic Monitor DLP system, including information about an employee's actions in the workplace, allowing you to pay attention to patterns of behavior that look risky or suspicious. noted Rustam Farrakhov, Director of the Product Development Department of InfoWatch Group of Companies.

InfoWatch Prediction supports domestic operational systems, and is also closely integrated with InfoWatch Vision and the InfoWatch Traffic Monitor DLP system, installed on a single server, which optimizes hardware resources. 

Read about the changes to the InfoWatch Vision product here.

2018: InfoWatch Prediction release announcement

In May 2018, InfoWatch, a Russian developer of comprehensive information security (IS) solutions for organizations, announced the release of InfoWatch Prediction in the UEBA (User and Entity Behavior Analytics) class at the GISEC-2018 Forum in Dubai, UAE. The analytical tool is designed for automated solution of applied problems based on forecasting of information security risks related to personnel and financial policy, identification of insider information, compromise of accounts, as well as other processes critical from the point of view of personnel management in the organization. The basic scenario in the first version of the product was the early determination by the system of employees who are going to quit. The commercial release of the solution is scheduled for 2018.

"The ideology of InfoWatch Prediction is aimed at solving specific problems in the field of corporate information security with the ability to check the result," said Andrey Arefiev, Head of Advanced Development at InfoWatch Group of Companies. - A key feature of our product is that it is built on a strict mathematical model and allows you to prevent specific risks, as well as check the accuracy of the solution. We provide the company with a tool that allows you to determine with high accuracy the employees who plan to leave the staff in advance, and thereby minimize the associated information security risks. "

The solution analyzes the company's information flows (Big Data) and, based on models built using machine learning methods, calculates the likelihood of dismissal of company employees. InfoWatch Prediction calculates the individual rating of each employee, which can be positive or negative. A positive rating indicates that a person is at risk, and the higher the indicator, the more likely he is to leave.

InfoWatch Prediction has passed the necessary tests in the infrastructure of a number of large companies, analyzing tens of thousands of events every day. According to industrial tests, the accuracy of determining employees who are going to quit was 90%. In addition, the product allows the customer to quickly verify the effectiveness of the evaluation based on a retrospective sample of data.

"We can demonstrate the performance of the system to the client almost instantly, although most other information security products take months to collect evidence of effectiveness, and the client is forced to spend his resources on this: equipment, time, money," said Andrei Arefiev. "The product only needs to analyze the data received from the mail server or DLP system in the company over the past year, after which it identifies the employees who quit, and the client has the opportunity to compare this result with the real data from the human resources department."

For the security officer of the organization, identifying a quitting employee allows you to apply special security policy settings, establish additional control to his actions and communications. In addition, the solution allows not only to minimize information security risks, but also will be useful for the implementation of management, financial and personnel accounting in the company.

According to Andrei Arefiev, the costs of losing an employee for the organization are equal to his annual salary. They consist of many factors: the low performance of the employee intending to quit, the various payments upon his departure, the resources and time spent on finding new personnel, as well as their subsequent adaptation, he added.