Automated Entitlement Review Using Probabilistic Access Revocation
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Solution Overview
Problem
Existing entitlement review processes in secure computing systems are time-consuming, prone to human error, and inefficient, often relying on faulty data sets, leading to potential security risks from unnecessary user access.
Innovation Solution
Implement probabilistic sampling and 'use it or lose it' methods for automated entitlement revocation, using machine learning to assign revocation probabilities and track entitlement usage, with user challenges and feedback loops to refine decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually review entitlements, then decision accuracy can be maintained through expert knowledge, but the process becomes time-consuming and diverts human labor from more productive pursuits
Solution Approach 1:
The system enables entitlements to self-review through automated agents that continuously monitor and evaluate entitlement necessity based on system data, user behavior patterns, and security policies, eliminating the need for manual human review while maintaining high accuracy through machine learning algorithms
Solution Approach 2:
The patent replaces the mechanical human review process with an automated machine learning-based system that uses algorithms to analyze entitlement data, detect anomalies, and make revocation decisions, substituting human cognitive labor with computational processes that are faster and scalable
2Productivity
If automated machine learning is used to replace human analysts, then productivity increases and human labor is freed, but the training data is likely to suffer from the same problem of over-entitlement and the automated agent is likely to be insufficiently aggressive
Solution Approach 1:
The system implements continuous feedback loops where revocation decisions are monitored, challenged by users if inappropriate, and used to retrain and refine the machine learning models, allowing the system to learn from errors and improve decision accuracy over time while maintaining high productivity
Solution Approach 2:
The patent employs dynamic, adaptive machine learning models that continuously evolve based on new data and feedback, allowing the system to adjust its revocation aggressiveness and accuracy thresholds in real-time rather than relying on static training data, making the system both productive and reliable
3Object-affected harmful factors
If entitlements are aggressively revoked to close security holes, then security risk is reduced, but legitimate user access may be interrupted and user productivity affected
Solution Approach 1:
The system performs preliminary reviews and notifications before actual revocation, allowing users to challenge decisions and provide justification for continued access, ensuring that legitimate entitlements are protected while still aggressively revoking unnecessary ones to close security gaps
Solution Approach 2:
The patent implements preliminary protective measures by monitoring entitlement usage patterns and user challenges before revocation, creating a buffer that prevents premature or erroneous revocations while maintaining the overall aggressive stance against unnecessary entitlements, thus balancing security with operational continuity
Data Source
AI summary
A computer-implemented method and system for performing automated systematic entitlement review is disclosed. The method comprises retrieving metadata concerning the one or more entitlements and the one or more users having those entitlements; selecting a first one or more instances of an entitlement and its user for revocation, and a second one or more instances of an entitlement and its user to be retained without revocation; automatically revoking access of the entitlements in the first one or more instances; and automatically notifying the users in the first one or more instances of the automatic revocation. In one family of variants, revocations are performed via probabilistic sampling, while in another family of variants, revocation are performed based on a “use it or lose it” scheme.