Dynamic Playbook Generation for Cybersecurity Incident Response
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Solution Overview
Problem
Current cybersecurity incident response systems rely on pre-constructed playbooks that do not account for all relevant fields of an incident, limiting their effectiveness and not incorporating user customization or feedback, leading to suboptimal response strategies.
Innovation Solution
A custom machine learning system that generates playbooks based on all available data, including user feedback and past incident pairings, by localizing nearest neighbors in a feature space and integrating user customization, enabling real-time, tailored responses to new incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-constructed playbooks are used based only on incident category, then playbook selection is simple and fast, but the effectiveness and accuracy of response is limited
Solution Approach 1:
The system transitions from static pre-constructed playbooks to dynamic custom playbook generation. The playbook generation system dynamically creates customized playbooks based on incident features, nearest neighbor incidents, and user feedback, allowing the response strategy to adapt to each specific incident rather than relying on fixed templates.
Solution Approach 2:
The system incorporates user feedback loops where user customizations and interactions with generated playbooks are fed back into the machine learning model. This feedback mechanism continuously improves the accuracy of playbook generation by learning from user preferences and incident outcomes, enhancing response effectiveness over time.
2Adaptability or versatility
If all available incident data and user feedback are incorporated into playbook generation, then the accuracy and adaptability of playbooks improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system introduces a playbook generation system as an intermediary layer between incident detection and response execution. This intermediary processes incident data, queries the knowledge base, applies machine learning algorithms, and generates customized playbooks, thereby managing complexity through modular architecture while maintaining high adaptability.
Solution Approach 2:
The patent replaces manual playbook creation and selection processes with automated machine learning-based generation. The custom ML model automatically analyzes incident features, identifies nearest neighbors, and generates playbooks without manual intervention, substituting mechanical human processes with intelligent automated systems.
3Measurement precision
If custom playbooks are generated in real-time using machine learning, then response accuracy improves, but the time required for playbook preparation increases
Solution Approach 1:
The system performs preliminary actions by pre-processing incident data, maintaining a knowledge base of historical incidents and playbooks, and pre-training the machine learning model. When a new incident occurs, the system quickly queries the prepared knowledge base and applies the trained model to generate playbooks rapidly, reducing real-time computation overhead.
Solution Approach 2:
The system optimizes the balance between precision and speed by adjusting parameters such as the number of nearest neighbor incidents considered, the complexity of feature analysis, and the depth of customization. These parameter changes allow the system to adapt computation intensity based on incident severity and time constraints.
4Ease of operation
If user customization is fully integrated into the playbook generation process, then user satisfaction and response effectiveness improve, but the system becomes more complex and harder to operate
Solution Approach 1:
The system enables users to customize playbooks through intuitive interfaces where they can review generated playbooks, modify actions, and provide feedback without needing to understand the underlying machine learning processes. The system automatically processes these customizations and feeds them back into the model, allowing users to benefit from advanced AI capabilities through simple interactions.
Data Source
AI summary
A new cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the new cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the new cybersecurity incident. A set or subset of nearest neighbors of the new cybersecurity incident is localized in a feature space. The nearest neighbors of the new cybersecurity incident are other cybersecurity incidents having a distance from the new cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the new cybersecurity incident. A custom playbook is created for responding to the new cybersecurity incident having prescriptive procedures based on occurrences of prescriptive procedures previously employed in response to the nearest neighbor cybersecurity incidents.