Prescriptive Rule Engine for Incident Alert Reduction
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Solution Overview
Problem
Enterprise networks face inefficiencies in incident alert systems due to high rates of false positive alerts, which consume resources and time, and lack a dynamic method to identify and eliminate unnecessary alerts across interconnected domains.
Innovation Solution
A system and method utilizing a prescriptive rule engine that generates a prescriptive avoidance rule set by extracting rules from historical incident alert data, refined through reinforcement learning, to automatically eliminate dead-end tickets and reduce unnecessary alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional empirical rule-based algorithms are used to identify false positive incident alerts, then the system can eliminate some unnecessary alerts, but the system cannot dynamically extract all possible rules and cannot identify cross-domain false positives
Solution Approach 1:
The system enables itself to automatically learn and generate correlation rules from historical incident alert data without requiring manual configuration by administrators. The machine learning model autonomously identifies patterns and relationships across domains, continuously improving its ability to detect false positives including cross-domain scenarios that conventional empirical rules cannot capture.
Solution Approach 2:
The system transforms the static, fixed set of empirical rules into dynamic, adaptive parameters through machine learning. The correlation rules are no longer hardcoded but are generated and refined based on historical data patterns, allowing the system to adapt to new types of false positives and cross-domain relationships that emerge over time.
2Ease of manufacture
If a fixed set of empirical rules is used in incident alert systems, then the system is simple to implement, but new causes of false positives remain unnoticed and require time-consuming manual tracking
Solution Approach 1:
The system automatically learns new false positive patterns from historical data without requiring manual updates to the rule set. The machine learning model continuously discovers new correlation rules and patterns, eliminating the need for administrators to manually track and add new false positive causes while maintaining system simplicity.
Solution Approach 2:
The system performs preliminary analysis of historical incident alert data to pre-learn correlation rules and patterns before they are needed for actual false positive detection. This advance learning enables the system to quickly identify new false positive causes without requiring time-consuming manual intervention when they occur in production.
3Adaptability or versatility
If manual configuration of correlation rules is required for each enterprise environment, then the system can be customized to specific needs, but the complexity and time required to configure and maintain rules increases significantly
Solution Approach 1:
The system automatically adapts to specific enterprise environments by learning from their historical incident alert data. Instead of requiring manual configuration of correlation rules for each environment, the machine learning model autonomously discovers environment-specific patterns and relationships, providing customization without the complexity of manual rule management.
Solution Approach 2:
The system transitions from static, manually-configured rules to dynamic, data-driven correlation rules that automatically adapt to each enterprise environment's unique characteristics. The correlation rules evolve over time based on historical data patterns, enabling customization to specific environments while eliminating the complexity of manual configuration and maintenance.
4Productivity
If incident alert system personnel manually review and resolve all submitted alerts, then all alerts can be processed, but resources are wasted on false positives and the process is time-consuming
Solution Approach 1:
The system extracts and removes false positive alerts from the stream of submitted incident alerts before they reach personnel for manual review. By using machine learning to identify and filter out false positives including cross-domain scenarios, the system separates harmful false alerts from legitimate incidents, allowing personnel to focus only on resolvable issues and improving both throughput and resource efficiency.
Solution Approach 2:
The system performs preliminary filtering of false positive alerts using learned correlation rules before alerts reach personnel for manual review. This advance identification and elimination of false positives prevents personnel from wasting time on unresolved issues, improving productivity while reducing resource waste on false alarm processing.
Data Source
AI summary
A system and method for reducing incident alerts for an enterprise environment are described. In one embodiment, a method of reducing incident alerts for an enterprise environment includes receiving a plurality of historical incident alerts associated with previous incidents associated with nodes within an enterprise environment. The method includes extracting from a first subset of the historical incident alerts a plurality of rules to generate a rule knowledge base and analyzing a second subset of the historical incident alerts against the plurality of rules to identify candidate incidents alerts as potential dead-end tickets. The method also includes providing feedback on the candidate incident alerts to confirm or deny that the alert is a dead-end ticket. Based on the feedback, a prescriptive avoidance rule set is generated to identify an incident alert as a dead-end ticket and eliminate the dead-end tickets from submitted incident alerts.