The great majority of data breaches are not caused by genius hackers breaking into networks. Most data breaches happen because passwords are stolen and privileged access credentials are abused. AI-powered machine learning systems employing Privileged Access Management (PAM) can help thwart these types of intrusions by identifying anomalies in real-time. High-risk events are identified, flagged, and elevated to the attention of security persons who can make a determination is the activity is legitimate.
Louis Columbus wrote about the promise of threat analytics in this article published in Forbes:
Machine learning algorithms enable threat analytics to immediately detect anomalies and non-normal behavior by tracking login behavioral patterns, geolocation, and time of login, and many more variables to calculate a risk score. Risk scores are calculated in real-time and define if access is approved, if additional authentication is needed, or if the request is blocked entirely.
Machine learning-based threat analytics also provide the following benefits:
- New insights into privileged user access activity based on real-time data related to unusual recent privilege change, the command runs, target accessed, and privilege elevation.
- Gain greater understanding and insights into the specific risk nature of specific events, computing a risk score in real time for every event expressed as high, medium, or low level for any anomalous activity.
- Isolate, identify, and track which security factors triggered an anomaly alert.
- Capture, play, and analyze video sessions of anomalous events within the same dashboard used for tracking overall security activity.
- Create customizable alerts that provide context-relevant visibility and session recording and can also deliver notifications of anomalies, all leading to quicker, more informed investigative action.
Threat analytics providers are capitalizing on machine learning to improve the predictive accuracy and usability of their applications continually. What’s most important is for any threat analytics application or solution you’re considering to provide context-aware access decisions in real time. The best threat analytics applications on the market today are using machine learning as the foundation of their threat analytics engine. These machine learning-based engines are very effective at profiling the normal behavior pattern for any user on any login attempt, or any privileged activity including commands, identifying anomalies in real time to enable risk-based access control.
