Machine Learning Protects Employee Privacy & Data Security

by Veriato | Nov 12, 2018

How Machine Learning Helps With Privacy and Data Security

Both data attacks and data security have evolved tremendously over the past few years. Notable advancements have been made in artificial intelligence that can improve your information security, while preserving privacy for your employees.

What is machine learning?

Machine learning is a category of artificial intelligence (AI) that uses programming and statistics to give computers the ability to “learn” from data without being given specific commands to complete each task. In other words, computers can be programmed to improve processes and make conclusions from data.

How can I implement machine learning to improve my data security?

User behavior analytics (UBA) is a form of machine learning focused on cyber security. UBA focuses on detecting threats to information based on user activity. Organizations can employ user behavior analytics to discover and prevent threats to their data and proprietary information.

When an organization deploys a UBA platform such as employee monitoring software, the program establishes baselines of user behavior. Based on that information, using machine learning, it can detect abnormal user activity or suspicious actions and send alerts to security teams. The security teams can then review recorded user behavior, assess the threat, and act to protect company data.

Employee monitoring software and UBA minimizes human error in detecting cyber security threats, and is more thorough. Additionally, it frees up staff to focus on higher value tasks and focus on actual threats that are brought to their attention, instead of spending hours trying to detect weak security points manually.

Employee privacy concerns with UBA

At the mention of employee monitoring software, privacy concerns usually arise. Employees don’t want to be recorded and they don’t perform as well when they feel they are being micromanaged. Employers want to foster a good environment for their employees and if workers are concerned about their privacy, employers usually want to alleviate their worries.

With UBA, privacy is maintained often more successfully than if a security team is personally managing insider threat detection. With software, no human is tracking their behavior, and a team won’t review it unless they are alerted to abnormal activity. At that point, only activity relevant to the incident needs to be reviewed.

Ultimately, activity is tracked, but not by people. Employee monitoring software actually prioritizes both employee privacy and information security. Employers should always be upfront about monitoring practices and educate their workforce about the importance of data security. Everyone appreciates knowing the reason for a policy.

As the amount of data that an organization has continues to grow, so does the risk of insider threats. By embracing machine learning and user behavior analytics, cyber security systems will be able to keep up with the constant need for threat monitoring.