Cloud Misconfiguration Remediation Using RAG Prompt Chains
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Solution Overview
Problem
Existing cloud security posture management (CSPM) systems often lack automated generation of effective remediation actions for detected misconfigurations, with only 10-15% of policies including such actions, leading to inefficiencies in addressing security vulnerabilities.
Innovation Solution
A remediation application utilizing a foundation model with retrieval augmented generation (RAG) to generate remediation actions by constructing input sequences based on CSPM policies, metadata, and remediation documentation, enabling automated generation of precise remediation commands and their execution or user consideration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated remediation generation is implemented using foundation models, then productivity and effectiveness of security posture management is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces an intermediary system comprising a conversation agent, prompt template, and input sequence chain that mediates between the foundation model and the CSPM policies. This intermediary layer simplifies the interaction by structuring inputs and managing the generation process, thereby reducing the effective complexity faced by users while maintaining high productivity.
Solution Approach 2:
The system segments the remediation generation process into distinct components: policy analysis, prompt construction, model inference, and output generation. This segmentation allows each component to be optimized independently and facilitates easier implementation and maintenance, reducing overall system complexity while improving productivity.
2Measurement precision
If foundation models with RAG are used to generate remediation actions, then measurement precision and accuracy of remediation commands improve, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing CSPM policies into structured formats and pre-con constructing prompt templates before actual remediation generation. The RAG mechanism also pre-retrieves relevant information from the knowledge base, reducing the computational burden during actual generation and minimizing time loss while maintaining high accuracy.
Solution Approach 2:
The input sequence chain employs periodic action by breaking down the remediation generation into discrete, manageable steps with intermediate outputs. This allows for efficient processing cycles where the foundation model can be queried incrementally rather than requiring a single lengthy computation, reducing overall generation time while preserving accuracy.
Data Source
AI summary
A cloud misconfiguration remediation application (“remediation application”) has been created that generates a remediation action for a resource misconfiguration detected with a CSPM policy. The remediation application includes a conversation agent that interacts with the foundation model according to a chain of prompts/input sequences. The conversation agent constructs the chain of prompts based on a template, the CSPM policy, metadata about the CSPM policy and the misconfigured cloud resource, and responses from the foundation model. The foundation model is implemented with retrieval augmented generation (RAG) that uses an embedding database built with remediation documentation of the CSP. Prompts from the conversation agent are augmented based on the implemented RAG. The remediation application aggregates the responses into a remediation action that can either be automatically performed or presented for consideration by a user.