Sudarsan Kannan

What factors contribute to a user's identity confidence (or) risk posture?

Blog Post created by Sudarsan Kannan Employee on May 31, 2019

Every customer who has adopted RSA SecurID Access’s risk engine capability to attain Identity Confidence, love and fully trust the capability.  Based on several discussions with you during the RSA conference and 1-1 sessions we realized that customers were looking for more visibility into the workings of risk engine to better understand how the risk engine can add value to your security policies. Specifically, you wanted to know why a user/group was challenged or what factors contributed to a user’s/group’s higher level of risk and hence lower identity confidence.

 

What did we do?

In May release, we have introduced a simple yet powerful capability through user event monitor to help solve visibility challenges. Below is a screenshot of the user event monitor for a user that sums up the entire feature. 

  • Confidence score  - Overall user identity confidence score. You can look at the aggregate confidence score across the entire user population (confidence Threshold) and benchmark a user’s confidence score against the aggregate score.
  • Category scores define what contributed to the overall confidence score. You can see if the low or high confidence score was driven more by the user’s device or by the location or by the user’s overall behavior or some combination thereof.  Category score consists of Device confidence, Behavior confidence, and Location confidence. 

The category scores (location, device & user behavior scores) are aggregated through a mathematical model to get the overall user level confidence score. 

 

 

 

For example, in the above screenshot user's confidence score is lesser than the aggregate score (confidence threshold) of the entire user population. In other words, the current user access request is riskier than the rest of the population and hence appropriate policy controls have to be in place to challenge the user with additional assurance. The reason for the user's lower confidence is more influenced by the lack of trust in the location from which the access request is coming from than the device from which the user request originates or the user's behavior. 

 

The lower the category (location, behavior or device) score is the lower the confidence is on that category. The system gains more trust by the continuous learning process on each of those categories over multiple access requests. This will eventually lead to higher confidence in each of those categories and hence the overall user confidence. 

 

How can these category scores add more value?

In addition to providing visibility into what contributed to the user's confidence level, these category scores can be used to determine the effectiveness of your security policies fully driven by identity context. For example, if admins see the device confidence is lower across a user set (ex: users within OU=Salesforce) leading to lower assurance across that user set (salesforce) the admin can try improving the device confidence and hence overall user confidence. One way to improve device confidence is to enable users with a managed device (through EMM/UEM).

 

Another great example could be how you can map your user or group level confidence (or risk) with better granularity to an IT application (as an RSA Archer IT asset) and make informed identity context driven risk management decisions. Possibilities are infinite with this enhanced visibility into RSA SID Access Risk Engine!

 

Hope the examples above help you in mapping some of the user level or group level identity risk factors to your organizational policies. As we learn more we plan to add more visibility and better way to control the risk engine so that you can take some meaningful actions impacting your identity risk posture.

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