Time-Based Data Access Policies Using Forecasted Request Volume
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Solution Overview
Problem
Existing data access policies struggle to dynamically determine time-based parameters and thresholds, leading to overprotection or insufficient protection due to difficulty in distinguishing normal from abnormal conditions, and often require manual updates that are not timely.
Innovation Solution
A method that groups data access requests into clusters based on activity counts, trains a forecasting model to predict future access volumes, and generates policies with time windows and thresholds, allowing or disallowing requests based on these predictions, with adjustments for prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ad hoc rules of thumb are used to define time-based data access policies, then policy implementation is simple, but the policy becomes overprotective or insufficiently protective
Solution Approach 1:
The system automatically analyzes historical access patterns and generates time-based policies without requiring manual configuration. The policy engine self-adjusts thresholds and time windows by processing actual access data, eliminating the need for administrators to hand-tune parameters while maintaining high reliability through data-driven decisions
Solution Approach 2:
The system continuously monitors access patterns and compares actual access rates against predicted thresholds. When discrepancies are detected (such as unusual access patterns or threshold violations), the system automatically adjusts policies and retrainis the forecasting model, creating a closed-loop feedback mechanism that maintains policy effectiveness without manual intervention
2Ease of operation
If static thresholds are used in data access policies, then policy enforcement is straightforward, but the policy cannot adapt to changing usage patterns
Solution Approach 1:
The system dynamically adjusts access thresholds and time windows based on forecasted access patterns. Rather than using fixed static values, the policy engine continuously updates thresholds according to predicted future access rates, allowing the policy to adapt to changing usage patterns while maintaining simple enforcement logic through automated model retraining and parameter adjustment
3Measurement precision
If manual policy updates are performed when usage patterns change, then policy accuracy can be maintained, but user access may be wrongly denied before complaints are received
Solution Approach 1:
The system proactively detects changes in usage patterns through continuous analysis of access data and automatically updates policies before problems manifest. By using forecasting models to predict future access patterns and adjusting thresholds in advance, the system prevents wrongful access denials rather than reacting to user complaints, maintaining both accuracy and timely user access
Data Source
AI summary
Data access requests are grouped into a plurality of clusters according to an activity count within each of a plurality of time periods. Based on a time separating two clusters in the plurality of clusters, a time window in which a data access policy applies is defined. Using the plurality of data access requests, a forecasting model is trained to predict a volume of future data access requests, the training resulting in a trained forecasting model. A data access policy effective during the time window and conditioned on a threshold related to the volume of future data access requests is generated. Responsive to determining that a new data request meets a criterion of the data access policy, processing of the new data request is allowed.