Agentic Cloud Security Control Selection for Expert-Free Architecture
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Solution Overview
Problem
Developing robust cloud architectures is complex and resource-intensive, often requiring subject matter experts, and results in systems that are vulnerable to security exploits without adequate expertise, making it difficult for smaller entities to leverage cloud computing advantages.
Innovation Solution
Utilizing agentically-orchestrated machine-learned models, such as Large Foundational Models, to emulate roles like cloud architects and security engineers to select and configure security controls for cloud architectures based on user requirements, reducing the need for expert involvement and improving security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subject matter experts are used to develop cloud architectures, then security and reliability are improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent creates virtual copies of subject matter experts through AI agents that emulate the knowledge and decision-making processes of human cloud architects and security engineers. These digital twins can analyze cloud architectures, identify security vulnerabilities, and provide recommendations without requiring actual human experts to be present, thereby maintaining high security standards while reducing the complexity and resource requirements of expert involvement.
Solution Approach 2:
The patent introduces AI agents as intermediary components between the cloud architecture and security analysis processes. These agents serve as mediators that automatically perform security assessments, validate configurations, and enforce security policies, eliminating the need for direct human expert intervention while maintaining reliable security outcomes.
2Reliability
If subject matter experts are involved in cloud architecture development, then security is improved, but productivity decreases due to resource intensity
Solution Approach 1:
The patent implements self-service capabilities where AI agents automatically perform security analysis, validation, and configuration tasks that traditionally required human expert intervention. The system can autonomously assess cloud architectures, identify vulnerabilities, and apply security controls without human involvement, dramatically improving productivity while maintaining security standards through automated expert-level analysis.
Solution Approach 2:
The patent employs advanced AI models with enhanced processing capabilities that accelerate the security analysis process. These powerful computational models can rapidly evaluate complex cloud architectures, perform deep security assessments, and generate recommendations much faster than human experts, thereby improving productivity without compromising security thoroughness.
3Reliability
If cloud architectures are made more secure through expert design, then reliability is improved, but ease of manufacture deteriorates
Solution Approach 1:
The patent introduces AI agents as intermediaries that automatically handle the complex task of implementing security controls in cloud architectures. These agents translate security requirements into specific configuration actions, automatically apply security policies, and validate implementations, making secure cloud architecture deployment accessible to organizations without expert personnel while maintaining high security standards.
Solution Approach 2:
The patent creates reusable security templates and configurations generated by AI agents that can be copied and applied across multiple cloud environments. These pre-validate d security patterns encapsulate expert knowledge and can be rapidly deployed without requiring organizations to manually design secure architectures from scratch, thereby improving ease of implementation while maintaining reliability.
Data Source
AI summary
Cloud architecture information descriptive of a proposed cloud architecture is obtained, including user response information including information indicative of cloud architecture requirements for the proposed cloud architecture to fulfill and component selection information indicative of cloud components that meet cloud architecture requirements. The cloud architecture information is processed with agentic orchestration models to obtain a role output including security control information indicative of security controls selected for the proposed cloud architecture. Each of the agentic orchestration models includes a machine-learned model prompted to fulfill a corresponding cloud architecting role. The role output is associated with a cloud security role. Based on the security control information, information indicative of the security controls selected for the proposed cloud architecture is provided to a user device.