Behavior-Based Security Policy Escalation for Adaptive Authentication
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Solution Overview
Problem
Existing authentication systems cause friction and distraction for employees due to frequent authentications and reauthentications, and existing security policies do not effectively adapt to changes in user behavior within an organization.
Innovation Solution
A monitor is used to detect behavioral anomalies by comparing device behavior to predefined clusters of user behaviors within an organization, adjusting security policies based on the distance from these clusters, and adapting authentication processes without requiring software installation on devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication systems trigger reauthentications to confirm device identity, then security is improved, but user experience deteriorates due to friction and distraction
Solution Approach 1:
The system dynamically adjusts authentication requirements based on real-time behavioral analysis. When user behavior deviates from established patterns, the system automatically triggers reauthentication. When behavior is consistent with norms, the system maintains seamless access. This dynamic adaptation resolves the contradiction by making security measures context-dependent rather than static.
Solution Approach 2:
The system changes the parameter of authentication frequency based on behavioral parameters. By monitoring behavioral metrics and comparing them against thresholds, the system adjusts whether reauthentication is required. This parameter-based control allows the system to optimize both security and user experience by applying authentication only when necessary.
2Reliability
If authentication systems require frequent reauthentications, then security is improved, but productivity deteriorates due to work interruptions
Solution Approach 1:
The system implements continuous feedback loops by monitoring user behavior and automatically adjusting authentication requirements. Behavioral data is collected, analyzed, and fed back into the authentication decision-making process. This feedback mechanism ensures that reauthentication is triggered only when behavioral anomalies suggest potential security risks, thereby maintaining productivity while ensuring security.
Solution Approach 2:
The system performs preliminary behavioral analysis before triggering reauthentication. By establishing baseline behavioral patterns and continuously comparing current behavior against these baselines, the system proactively identifies anomalies that warrant security intervention. This preliminary action approach prevents unnecessary interruptions while maintaining security vigilance.
3Device complexity
If security policies are statically defined, then system complexity is reduced, but adaptability deteriorates in response to changing user behavior
Solution Approach 1:
The system provides self-service security policy adjustment by automatically adapting policies based on observed user behavior. Rather than requiring manual policy updates or complex administrative configuration, the system autonomously learns behavioral patterns and adjusts authentication requirements accordingly. This self-service approach maintains low system complexity while achieving high adaptability.
Data Source
AI summary
This disclosure describes techniques for escalating a security policy based on anomalous behavior. An example method includes identifying first behaviors associated with a first user and identifying a cluster comprising the first behaviors and second behaviors associated with at least one second user. The first user and the at least one second user are within a predetermined group within an organization. The example method further includes determining that a third behavior of a device associated with the first user is greater than a threshold distance from the cluster and outputting an alert.