SIEM Alert Pipeline Recovery Through Offline Task Reconstruction
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Solution Overview
Problem
SIEM systems face inefficiencies due to rudimentary case creation processes with little resiliency, leading to data loss and missed threats when alert failures occur, relying heavily on the quality of alerts and requiring re-running processes to reproduce errors.
Innovation Solution
A method to handle failures in a SIEM system pipeline by deserializing error logs, reconstituting tasks in an offline environment, identifying causes, and re-executing the pipeline with modified or replacement tasks to process alerts, enhancing data processing and security event prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rudimentary case creation process is used in SIEM systems, then the system structure remains simple, but the system lacks resiliency and cannot recover from failures
Solution Approach 1:
The patent implements preliminary error logging and task state serialization before pipeline execution, enabling the system to capture failure states and reconstruct failed tasks. Error logs are maintained in advance with detailed task information, allowing the system to recover from failures without complete re-execution, thus improving resiliency while managing complexity through structured pre-prepared data.
Solution Approach 2:
The patent creates copies of task states and error information in structured logs, allowing the system to reconstruct failed tasks without re-running the entire pipeline. Task contexts are serialized and stored, enabling exact reproduction of failed task states for analysis and recovery, improving reliability through data duplication rather than complex recovery logic.
2Loss of information
If the pipeline re-runs the entire process when an error occurs, then data loss is avoided, but significant time is wasted and inefficiencies occur
Solution Approach 1:
The patent segments the pipeline into individual tasks with distinct entry and exit points, allowing error identification at specific task boundaries. When an error occurs, only the failed task and subsequent tasks are re-executed, while successful tasks are skipped by loading their results from logs. This segmentation enables partial re-execution, preventing data loss while minimizing time waste.
Solution Approach 2:
The patent discards successfully completed tasks by loading their results from logs rather than re-executing them, while recovering and re-executing only the failed task and subsequent tasks. Task contexts are recovered from serialized logs, enabling selective re-execution that prevents information loss while significantly reducing the time penalty compared to full pipeline re-runs.
3Reliability
If alert quality is not validated, then the processing speed is maintained, but threats may be missed or false positives occur
Solution Approach 1:
The patent implements preliminary alert validation checks at the beginning of pipeline tasks, verifying alert quality and format before main processing. This preliminary action catches invalid alerts early, preventing downstream processing waste and ensuring only valid alerts proceed through the pipeline, maintaining both accuracy and efficiency through staged validation.
Solution Approach 2:
The patent replaces manual threat analysis with automated validation rules and error logging mechanisms that systematically check alert quality. This substitution of mechanical validation processes ensures consistent threat detection accuracy without significantly impacting processing speed, as automated checks are faster and more reliable than manual review.
4Productivity
If errors are skipped without logging, then the pipeline continues execution, but the cause of errors cannot be identified and fixed
Solution Approach 1:
The patent implements comprehensive error logging that captures detailed task contexts, error types, and failure states. This feedback mechanism provides actionable information about pipeline failures, enabling systematic error identification and fixing. The structured error logs serve as feedback that guides debugging and improvement while allowing the pipeline to continue execution with skipped errors properly documented.
Data Source
AI summary
A method of handling a failure in a pipeline between a source of alerts and a Security Incident & Event Management (SIEM) platform involves: deserializing data from at least one error state object in an error log; transferring the deserialized data to an offline environment; reconstituting at least some tasks of the pipeline, including the failed task, using contents of the error log and contents from a task log; re-running the at least some tasks of the pipeline; identifying a cause of the failed task; based upon the cause of the failed task, transferring at least one of a modified task or replacement task for use in the pipeline; instantiating a new version of the pipeline incorporating the at least one of the modified task or the replacement task; and re-executing the new version of the pipeline to process alerts and provide the processed alerts to the SIEM platform.