Privileged Identity Authentication via User Behavior Analytics
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Solution Overview
Problem
Current privileged identity management systems fail to effectively prevent privilege escalation attacks by not calculating a risk score based on continuous learning of user behaviors, leading to unauthorized access to sensitive endpoints.
Innovation Solution
Implement a system that calculates a risk score for privileged users based on their actions, using a User Behavior Analytics device to assess factors such as unusual locations, times, and previous actions, and employs multi-factor authentication or rerouting for approval when the risk score falls within specific thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If privileged users are granted access to endpoints without continuous risk assessment, then access speed and ease of operation are improved, but security and reliability deteriorate due to inability to detect privilege escalation attacks
Solution Approach 1:
The system performs preliminary risk assessment before granting access by calculating a risk score based on user behavior patterns, location, time, and action history. This preliminary evaluation prevents privilege escalation attacks while maintaining normal access flow for authorized users.
Solution Approach 2:
The system continuously monitors user actions during privileged sessions and provides feedback by updating risk scores in real-time. When risk thresholds are exceeded, the system responds by terminating sessions or requiring additional authentication, creating a closed-loop security mechanism.
2Reliability
If risk score calculation is performed for every access request, then security is improved through continuous behavior monitoring, but system complexity and processing time increase
Solution Approach 1:
The risk assessment system is segmented into distinct modules: behavior pattern learning component, risk score calculation component, and decision enforcement component. This segmentation allows independent optimization and maintenance of each module while reducing overall system complexity.
Solution Approach 2:
The system applies partial risk assessment by focusing computational resources on high-risk scenarios. Not all access requests require full behavioral analysis - the system uses heuristic rules for low-risk contexts and reserves comprehensive behavior monitoring for situations that trigger risk thresholds.
3Reliability
If multi-factor authentication is required for all users, then security is improved, but ease of operation and productivity deteriorate due to additional authentication steps
Solution Approach 1:
The authentication system is dynamic rather than static - it adjusts the level of authentication required based on real-time risk scores. Low-risk users experience minimal friction with standard authentication, while high-risk scenarios automatically trigger multi-factor authentication, optimizing both security and productivity.
Data Source
AI summary
Embodiments can provide a method for accessing an endpoint, including: receiving, from a privileged user, a request for accessing the endpoint; providing, by a user behavior analytics device, a risk score with respect to the privileged user, wherein the risk score is calculated based on at least one action performed by the privileged user. If the risk score is lower than a first predefined value, providing access to the endpoint for the privileged user. If the risk score is higher than a second predefined value, denying the request for accessing the endpoint. If the risk score is between the first predefined value and the second predefined value, performing at least one of: providing, by a privileged identity management server, a multi-factor authentication against the privileged user; and rerouting the request for accessing the endpoint for approval.