Generative Machine Learning Security Responses for Emerging Threats
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Solution Overview
Problem
Extended Detection and Response (XDR) systems face challenges in automating and expediting the response and remediation of security deficiencies, particularly in dynamically adapting to new threats and vulnerabilities without requiring extensive retraining.
Innovation Solution
Utilizing a generative machine learning model to process unstructured text data, generate mitigating responses for security deficiencies, and validate outputs against predefined constraints, enabling rapid adaptation to emerging threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional security response systems are used, then they can detect security deficiencies, but they cannot rapidly adapt to new threats without extensive retraining
Solution Approach 1:
The patent introduces a generative machine learning model as an intermediary between security deficiency detection and response formulation. This model processes unstructured text data from multiple sources (security advisories, vulnerability databases, threat intelligence reports) to automatically generate mitigating responses, eliminating the need for extensive retraining when new threats emerge.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing unstructured text data from various security sources in a structured format. This preparation work is done in advance, allowing the generative model to quickly retrieve and adapt relevant information when new security deficiencies are detected, without requiring time-consuming retraining.
2Productivity
If generative machine learning models are used to generate mitigating responses, then response speed improves, but output validation and constraint satisfaction become complex
Solution Approach 1:
The patent implements a feedback mechanism where the generated mitigating responses are validated against predefined constraints and criteria. The validation process provides feedback to the generative model, allowing for iterative refinement of outputs to ensure they meet security standards and operational requirements while maintaining rapid response generation.
Solution Approach 2:
The system dynamically adjusts the validation process based on the type of security deficiency and the confidence level of generated responses. For high-confidence responses with clear constraint satisfaction, validation is streamlined. For uncertain cases, more comprehensive validation and human review are triggered, optimizing the balance between speed and accuracy.
Data Source
AI summary
An example method includes receiving an identifier associated with a security deficiency, wherein the security deficiency is associated with a computer system; determining, based on the identifier, text data associated with the identifier; determining a text prompt, wherein the text prompt comprises an instruction segment and the text data, and wherein the instruction segment identifies a mitigating response detection task and an output constraint; providing the text prompt to a generative machine learning model; receiving, from the generative machine learning model, a set of outputs including a first output identifying a first mitigating response and a second output identifying a second mitigating response; determining that the first output satisfies the output constraint; determining that the second output fails to satisfy the output constraint; determining, based on the first output, a final output; and providing the final output using an output interface.