Visual Playbook Editor for Incident-Driven Data Path Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing playbook development tools for IT and security operations applications are labor-intensive, error-prone, and require extensive manual data path configuration, making it difficult for developers to efficiently create and test playbooks due to overwhelming data and lack of intuitive data visualization.
Innovation Solution
A data-driven approach for playbook generation that allows users to visualize and interact with actual incident data through a visual playbook editor (VPE), providing intuitive data visualizations, system-recommended actions, and suggested actions based on incident metadata, enabling developers to easily select and incorporate relevant artifacts into playbooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual playbook development is used with traditional tools, then developers can create playbooks with detailed control, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The system performs self-service by automatically generating playbook configurations, data paths, and artifact selections based on incident data analysis. The playbook editor autonomously populates fields, suggests artifacts, and configures data paths without requiring manual developer intervention for each element, thereby maintaining precision while accelerating development.
Solution Approach 2:
The system performs preliminary actions by pre-configuring playbook templates, pre-analyzing incident data to identify relevant artifacts, and pre-generating data paths before the developer needs them. This advance preparation eliminates manual configuration steps and reduces errors during the actual playbook creation process.
2Manufacturing precision
If extensive manual data path configuration is required, then developers can ensure accurate data mapping, but the complexity and time required increases significantly
Solution Approach 1:
The system replaces the mechanical manual configuration process with an automated data-driven system. Instead of manually mapping data paths through complex interface operations, the system automatically analyzes incident data, identifies relevant artifacts, and generates data paths programmatically, thereby reducing both complexity and potential errors.
Solution Approach 2:
The system introduces an intermediary layer between the developer and the complex data path configuration. This intermediary automatically processes incident data, suggests relevant artifacts, and generates data path configurations, shielding the developer from complexity while ensuring accurate data mapping through systematic analysis.
3Quantity of substance
If developers work with overwhelming amounts of incident data, then they can access comprehensive information, but it becomes difficult to visualize and select relevant artifacts
Solution Approach 1:
The system extracts only the relevant artifacts and data elements from the overwhelming incident data based on the playbook context and incident type. It selectively pulls out necessary information while filtering out irrelevant data, making artifact selection easier without losing comprehensive information when needed.
Solution Approach 2:
The system provides feedback by analyzing the playbook configuration and incident data to suggest relevant artifacts and data paths. This feedback mechanism guides developers through the data by highlighting what is most relevant based on the current context, making artifact selection intuitive despite the volume of available data.
4Reliability
If manual playbook testing is performed, then developers can verify each configuration step, but the overall development cycle time increases
Solution Approach 1:
The system enables continuous verification by automatically testing playbook configurations as they are being built rather than requiring separate testing phases. The automated system continuously validates data paths, artifact selections, and configuration integrity, maintaining reliability while eliminating idle time between configuration and verification steps.
Data Source
AI summary
Systems, methods, and computer-readable media may facilitate data-driven playbook generation. Resources may be sent to facilitate presentation of a graphical user interface (GUI) that allows configuring of function blocks with a playbook editor to build a playbook. The playbook editor may include an interface and a playbook canvas that allows addition and interrelation of function blocks to define an ordered set of operations to be performed in response to identification of an incident in an information technology (IT) environment. A selection of an interface option to add a first function block to the playbook canvas may be received. The first function block may be added to the playbook canvas of the interface. Outputs of the first function block with sample data for the outputs in a data panel of the interface may be presented in conjunction with the playbook canvas.