ML Authorization Policy Recommendations With Feedback-Driven Updates
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Solution Overview
Problem
Organizations face challenges in creating dynamic and adaptive authorization policies that address ever-changing security and operational needs, as manual methods fail to keep pace with organizational evolution.
Innovation Solution
A machine learning-based system that receives input data, preprocesses it, generates authorization policy recommendations, and iteratively updates the model based on organizational feedback, using techniques like unsupervised learning, semi-supervised learning, and reinforcement learning to provide tailored security recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to define authorization rules, then organizations can maintain control over security policies, but the system cannot keep pace with ever-changing organizational needs and security threats
Solution Approach 1:
The system transitions from static manual policy definitions to dynamic machine learning-driven policy generation. The ML model continuously learns from organizational data and feedback, automatically adapting authorization rules to changing needs without manual intervention, thereby resolving the contradiction between adaptability and time loss.
Solution Approach 2:
The system implements feedback loops where organizational outcomes and security events are continuously fed back to the ML model. This feedback mechanism enables the model to learn from actual organizational behavior and security incidents, automatically updating policies to match evolving needs while eliminating the time delay associated with manual policy updates.
2Adaptability or versatility
If machine learning models are used to generate authorization policy recommendations, then adaptability to changing needs is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between raw organizational data and authorization policies. The ML model acts as this intermediary, translating complex organizational behaviors and security requirements into simplified, actionable policy recommendations. This intermediary approach manages system complexity by encapsulating the complexity within the model while presenting simplified outputs to users.
Solution Approach 2:
The system implements self-service capabilities where the ML model autonomously generates and updates authorization policies based on learned patterns from organizational data. This self-service approach reduces the need for complex manual configuration and management interfaces, thereby managing system complexity while maintaining high adaptability.
3Productivity
If authorization policies are automatically enforced, then operational efficiency is improved, but flexibility to adjust policies decreases
Solution Approach 1:
The system employs dynamic policy enforcement where automatically generated policies can be adjusted in real-time based on new organizational data and feedback. The ML model continuously monitors outcomes and modifies policies dynamically, maintaining both automated efficiency and the flexibility to adapt to changing conditions without manual intervention.
Data Source
AI summary
Disclosed embodiments relate to developing machine learning authorization policy recommendations. Techniques may include receiving input data for an organization; pre-processing the input data, wherein the pre-processing includes a feedback loop to update the input data by providing feedback to the organization; generating, using a machine learning model, at least one authorization policy recommendation based on the input data, wherein the machine learning model is trained using at least one of: an organizational attribute, an organizational action, an organization policy, or domain information; providing the at least one authorization policy recommendation to the organization; identifying a status of the at least one authorization policy recommendation, wherein the status comprises at least one organizational feedback; and iteratively updating the machine learning model based on the identified status.