Signal Aggregation Labeling for Identity Risk Ground Truth
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Solution Overview
Problem
Identity management systems face challenges in accurately evaluating the efficacy of risk assessment products due to limited access to ground truth knowledge, which is crucial for assessing and improving user risk classification.
Innovation Solution
The system collects data signals from multiple sources, stores them in a database, and assigns labels to users as malicious or benign, using these labels as ground truth to evaluate risk assessment products, calculating confidence levels and improving their accuracy through signal aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses heuristics or models to assess user risk, then the system can perform risk classification, but the accuracy and efficacy of the heuristics or models cannot be reliably evaluated without ground truth knowledge
Solution Approach 1:
The system performs preliminary actions by collecting data signals from multiple sources before risk assessment to establish ground truth labels. This preliminary data collection and labeling enables subsequent accurate evaluation of risk assessment models by providing the necessary ground truth information that was previously missing.
Solution Approach 2:
The system implements feedback mechanisms by comparing risk assessment outputs against ground truth labels derived from aggregated data signals. This feedback loop enables continuous improvement of risk assessment accuracy by identifying and correcting misclassifications based on verified ground truth information.
2Reliability
If the system collects and aggregates data signals from multiple sources, then ground truth can be established for evaluation, but the device complexity increases
Solution Approach 1:
The system merges multiple data signals from diverse sources (native sources, internal sources, external sources, remediation procedures) into a unified ground truth framework. By combining these signals through aggregation and synthesis, the system achieves reliable ground truth establishment while managing complexity through integration rather than separate processing streams.
Solution Approach 2:
The ground truth establishment mechanism serves multiple functions: it provides evaluation benchmarks for risk assessment models, enables accuracy measurement, supports model improvement, and facilitates auditing. This multi-functionality justifies the added complexity by delivering value across multiple operational domains.
Data Source
AI summary
An identity management system may perform ground truth establishment and labeling techniques using signal aggregation. The identity management system may obtain, from multiple data sources, multiple data signals associated with a user of a set of multiple users of the identity management system. The identity management system may store the multiple data signals in a database. In some examples, the identity management system may aggregate the multiple data signals in the database. The identity management system may assign a label to the user based on the database. The label may indicate whether the user is malicious or benign. The identity management system may calculate a confidence level for a risk assessment product based on a comparison between the label and one or more outputs of the risk assessment product. The confidence level may indicate a confidence of the risk assessment product to classify the user as malicious or benign.