InfoWatch readies Digital Technology and Cyber Security Training Centre
Middle East-based centre will provide education and advanced training in cyber security, IoT, big data, artificial intelligence and blockchain
InfoWatch Group, a global provider of end-to-end enterprise cyber security solutions used its appearnce at the Gulf Information Security Expo and Conference (GISEC) 2018, to unveil plans of its International Digital Technology and Cyber Security Training Centre in the Middle East.
The centre that will provide education and advanced training in cyber security, IoT, big data, artificial intelligence and blockchain.
According to InfoWatch Group, the International Digital Technology and Cybersecurity Training Centre is aimed at local government officials and business executives, channel partners, as well as students from field-specific universities. "InfoWatch Group specialists used GISEC to also talk about technology trends and promising solutions in enterprise cybersecurity,"said Kristina Tantsyura, managing director, Middle East at InfoWatch Group.
Tantsyura added that the company is already cooperating with the Government of Ajman and Ajman University and the agreement covers certain areas, including education initiatives, with InfoWatch creating its educational programmes for the training centre jointly with its academic partners Skolkovo Institute of Science and Technology (Skoltech) and National Research University Higher School of Economics (HSE).
As part of GISEC, InfoWatch Group also showcased InfoWatch Prediction, a user and entity behaviour analytics (UEBA) solution.
The vendor said the UEBA solution automatically predicts cyber security risks associated with HR and finance policies, malicious insiders, account compromises, as well as other HR-critical processes in a company.
According the Russia-based cyber security vendor, the first product version will become commercially available later this year, focusing on proactive detection of employees that are about to leave.
"InfoWatch Prediction is about verifiable completion of certain enterprise cyber security tasks," Tantsyura said.
"Its key feature is an underlying strict mathematical model, which helps prevent specific risks and check the prediction accuracy. We provide enterprises with a tool that precisely and proactively identifies leaving employees, thus minimising related cyber security risks."
InfoWatch Prediction analyses enterprise information flows (big data) and uses machine learning-based models to predict employee resignation probability by calculating individual rating (positive or negative) of each employee. Positive rating is assigned to high-risk employees (the higher the rating, the more likely an employee will leave).
InfoWatch said during performance tests within infrastructures of several large companies with tens of thousands of events analysed a day, InfoWatch Prediction identified employees who were about to leave with 90% accuracy. In addition, stated the DLP solutions vendor, customers can quickly check the prediction accuracy using retrospective data sampling.
"Unlike most other information security products that take months to prove their effectiveness and require customers' equipment, time and money, we can demonstrate our system's output almost instantly," Tantsyura explained. "Our product analyses data received from the company's mail server or DLP system for the last year to identify employees who left. Customers can compare the findings with data from their HR teams."
Once a leaving employee is identified, a security officer can apply special security policy settings and extra control over their actions and communications. Moreover, the solution not only minimises cyber security risks, but also contributes to personnel record keeping, as well as management and financial accounting.
Tantsyura explained that the cost of losing an employee equals their annual salary and includes losses caused by under performance of a leaving employee, their severance pay, as well as efforts and time spent for new employee search and on-boarding.