Privileged access is recognized as one of the most significant risks that organizations are facing, driving them to think about not only about compliance-based, but to risk-aware strategies, as well. Compromised privileged access makes it possible for the cyber attackers to easily breach a system by being disguised as an authorized user. That allows them to remain undetected and freely pass through systems and networks.

We know that the driving force for PAM implementation is regulatory compliance. PAM solutions take care of the basic Privileged accounts requirement – privilege creeps, password management, accountability and such. But nowadays, with the rise of new tools and technologies, machine learning capabilities bring a new comprehension of Privileged Access Management.

The world’s leading research company, Gartner, reveals that “Identifying all systems and the corresponding privileged accounts is important, because every privileged account is a potential source of risk. However, this is a major challenge, as it is easy for privileged or default system accounts to be forgotten and left out. This is exacerbated by virtualization and hybrid environments that include cloud infrastructure. In such a dynamic environment, systems and accounts can easily fall through the cracks of privileged access management.” By this statement, Gartner means that some better methodologies must be adopted to prevent potential breaches from occurring due to improperly audited and secured privileged accounts and entitlements.

How Does Machine Learning Improve Admin User Experiences?

It is hard for most organisations to manually maintain and to audit privileged account entitlements. In this case the solution for the enterprises is to rely on machine learning intelligence. The best approach is applying identity analytics and machine learning to discover privileged access that poses a security risk to the organization. In this way the undocumented and unnecessary permissions can be eliminated or identified for monitoring with behaviour analytics.

1. Analysis of users-behaviour

One of the strongest features of machine learning is that it is able to constantly analyse and looks for anomalies in users’ behaviour across every threat surface, device, and login attempt. When any users’ behaviour appears to be outside the threshold of restrictions defined for threat analytics, an additional authentication is requested. Meanwhile access to requested resources is denied until the identity is verified. So, what machine learning does, is to make adaptive preventative controls possible by tracking and analysing every potential security threat and attempted breach in a real time.

2. Prevent and contain privilege attacks at endpoints

According to the Technical Manager from Happiest Minds – Susmita Shankaran – the area that is most targeted by attackers today, is an enterprise’s endpoint. “While choosing your PAM solution, you should look for solution capabilities to prevent and contain privilege attacks at endpoints. That is why companies are in a great need of PAM solution with file-based machine learning algorithms that detect and control automatic installation of the malicious application. In addition to least privilege access management, RBAC and application control, these solutions should seamlessly integrate with powerful privilege analytics engine. A mature privilege analytics engine helps to identify and secure accounts that are potentially exposed to credential theft, automatically invalidating suspected stolen credentials and immediately quarantine or terminate high-risk privileged sessions by leveraging statistical modelling, machine learning, UEBA and deterministic algorithms to detect malicious activity.”

3. Delivering excellent admin user experiences.

Machine learning also provides adaptive, personalized login experiences at scale using risk-scoring of every access attempt in real-time. That capability contributes for improved user experiences.Machine learning enables the business to implement security strategies that adapt to risk contexts in a real-time. Another good feature is assessment of every access attempt across every threat surface, and generating a risk score in milliseconds, which is a prerequisite for delivering excellent admin user experiences. That is a great security approach for many enterprises from different industries, including financial services and insurance companies, which protect their resources and networks from privileged access abuse. 

Having in mind the above mentioned information, it is obvious that enterprises today will step by step turn to ML and AI based technologies. Organizations should remember that cyberattack makes them vulnerable to financial losses and corporate reputation risk. That is why it is necessary to undertake investigating, assessing, and rebuilding the compromised systems. By applying a machine learning-based approach for proper validation of every access request, the risk of privileged credential misuse could be significantly reduced.