Observation Stream Engine for Dynamic Multi-Source Incident Investigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional security management systems lack the computing infrastructure and logic for user-managed investigation of security incidents, particularly in dynamic and mobile computing environments, and fail to provide a flexible and dynamic user experience in aggregating and presenting security incident data.
Innovation Solution
An observation stream engine in a security management system that supports user-configured observation stream queries, dynamically generating and presenting security incident data through an observation stream framework, enabling real-time understanding of malicious code operations and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional security management systems centrally store and statically present security incident data, then data storage and basic access are simplified, but the system lacks flexibility in aggregating and presenting security incident data, limiting dynamic user experience
Solution Approach 1:
The system implements dynamic querying capabilities where users can configure observation stream queries with parameters for selecting data sources, time ranges, and event types. The observation stream engine dynamically executes these queries against multiple security data sources (firewall logs, intrusion detection systems, endpoint security tools) and presents results in real-time, transforming static data storage into dynamic data aggregation and presentation.
Solution Approach 2:
The observation stream engine serves multiple functions within the security management system: it queries diverse data sources, filters and correlates security events, generates observation stream data with timestamps and metadata, and presents information through graphical interface elements. This multi-functional component addresses the need for flexible data aggregation without requiring separate systems for each function.
2Ease of operation
If the system provides dynamic tracking of security incidents across multiple data sources, then investigation capability is improved, but the complexity of querying and processing data increases
Solution Approach 1:
The observation stream engine acts as an intermediary layer between users and the complex underlying security data sources. Users interact with simplified observation stream queries that specify high-level parameters (data sources, time ranges, event types) rather than dealing with complex query languages or data schemas. The engine handles the complexity of executing queries across multiple sources, filtering events, and correlating data, while presenting results in a standardized observation stream format.
Solution Approach 2:
The system segments the security incident investigation process into discrete observation stream queries that can be independently configured and executed. Each query operates on specific parameters (data source selection, time range, event type filters) and returns focused observation stream data. This segmentation allows users to manage complex investigations by breaking them down into manageable query units rather than dealing with monolithic complex queries.
3Measurement precision
If the system generates observation stream data with user-defined interpretation data, then the quality of security incident analysis is improved, but the time required for data processing and interpretation increases
Solution Approach 1:
The observation stream engine performs preliminary processing of security data by pre-filtering, pre-correlating, and pre-formating data from multiple sources into a standardized observation stream format before it reaches the user. User-defined interpretation data is generated in advance through automated analysis of event patterns, threat intelligence feeds, and contextual information. This preliminary action reduces the time users would otherwise spend manually processing and interpreting raw data, while maintaining high analysis quality through automated intelligence integration.
Data Source
AI summary
Methods, systems, and computer storage media for providing observation stream data of security incidents using an observation stream engine in a security management system. An observation stream framework supports continuously generating and presenting observation stream data that facilitates developing a working hypothesis of an active security incident. The observation stream framework can also include observation stream query-types that can be selected for running queries against a plurality of security data sources. In operation, an observation stream query is accessed. The observation stream query is a user-generated observation stream query associated with an observation stream query-type. The observation stream query-type comprises parameters for querying a plurality of security data sources and dynamic tracking of a security incident. The observation stream query is executed and observation stream data is generated. The observation stream data is caused to be displayed on an observation stream interface comprising data visualizations of the observation stream data.