Alert Suppression Policy Control With Explainable Benefit Feedback
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Solution Overview
Problem
Existing alert suppression policies in IT environments are inflexible, lack adaptability, and fail to consider dynamic changes, leading to increased false positives and decreased productivity due to manual configuration and lack of transparency, resulting in inadequate coverage and user distrust.
Innovation Solution
A system that computes expected and observed benefits of alert suppression policies, provides explanations for policy recommendations, and automatically refines or deletes policies based on performance, ensuring adaptability and user trust through dynamic explanation and lifecycle management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of alert suppression policies is used, then ease of operation is reduced, but device complexity is also reduced
Solution Approach 1:
The system automatically generates alert suppression policies by analyzing historical alert data and determining persistent regions where alerts should be suppressed. The system self-configures policies without manual intervention, computing expected benefits and automatically refining policies based on observed performance, thereby improving ease of operation while managing complexity through automation
Solution Approach 2:
The system pre-computes alert suppression policies during offline training phases using historical data before runtime deployment. By performing preliminary analysis and policy generation in advance, the system reduces operational complexity during runtime while maintaining high ease of operation through pre-configured, data-driven policies
2Adaptability or versatility
If static alert suppression policies are used, then adaptability is reduced, but device complexity is reduced
Solution Approach 1:
The system implements dynamic alert suppression policies that adapt to changing IT environments by continuously monitoring runtime alert data. The policies are automatically refined based on observed benefits, allowing the system to adapt to new patterns and conditions while managing complexity through systematic adaptation mechanisms
Solution Approach 2:
The system computes both expected benefits (from historical data) and observed benefits (from runtime data) of alert suppression policies. By comparing expected versus observed outcomes and using this feedback to automatically refine policies, the system achieves adaptability while controlling complexity through evidence-based policy adjustment
3Measurement precision
If comprehensive policy analysis is performed, then measurement precision is improved, but loss of time is increased
Solution Approach 1:
The system performs comprehensive policy analysis and computes expected benefits during offline training phases using historical data. By completing thorough measurements and analyses in advance, the system achieves high measurement precision for policy recommendations while minimizing runtime computational overhead and time loss during operational phases
4Loss of information
If transparent policy explanations are provided, then user trust is improved, but device complexity is increased
Solution Approach 1:
The system introduces explanation mechanisms that act as intermediaries between the complex policy analysis engine and end users. By providing natural language explanations of why policies are recommended, what benefits are expected, and how policies perform, the system maintains transparency and builds user trust while managing the complexity of the underlying analysis through structured explanation generation
Data Source
AI summary
An approach is provided for controlling an alert suppression policy. Using historical training data and for a time period, an expected alert reduction for an alert suppression policy (ASP) is computed. The ASP is recommended for an information technology (IT) environment. During runtime and for the time period, an observed alert reduction for the ASP is computed. A visualization of a benefit of the ASP is generated by generating a visualization of a comparison of the observed alert reduction and the expected alert reduction. An explanation of why the ASP is recommended for the IT environment is generated by determining persistent regions for respective event time series from the historical training data and generating a visualization of the persistent regions on a time graph that includes a duration and a number of events for a given persistent region. Each event time series corresponds to a given resource for the ASP.