Cloud Vulnerability Scanning With Resource-Aware Environment Deployment
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Solution Overview
Problem
Current cloud computing environment scanning solutions are resource-intensive, costly, and fail to provide comprehensive vulnerability scanning for multi-tenant systems and various cloud system structures, particularly for containers and serverless applications, with agent-dependent processes being complex and snapshot-based scanning lacking integration and data protection.
Innovation Solution
A system and method for optimizing resource deployment in cloud environments by detecting entities, generating a security database representation, associating entities with software-based functions, determining resource utilization, and deploying a second cloud environment to minimize resource usage, utilizing static analysis and sensor deployment for efficient vulnerability detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If snapshot-based scanning is used for agentless vulnerability analysis, then deployment complexity is reduced, but data protection requirements are compromised in multi-tenant systems
Solution Approach 1:
The patent introduces a security gateway as an intermediary component that mediates between the snapshot-based scanning mechanism and the multi-tenant cloud environment. The gateway enforces security policies, controls data access, and ensures proper authorization before allowing snapshot operations, thus maintaining data protection while enabling agentless scanning in multi-tenant systems.
Solution Approach 2:
The patent implements dynamic security policy enforcement where access control rules and security parameters are adjusted in real-time based on the specific multi-tenant context, user credentials, and sensitivity of the data being scanned. This dynamic adaptation allows the system to maintain security appropriateness while enabling flexible snapshot-based scanning operations.
2Adaptability or versatility
If multiple separate scanning solutions are deployed for different cloud workload types, then comprehensive vulnerability coverage is achieved, but system complexity increases
Solution Approach 1:
The patent creates a universal scanning platform that can handle multiple cloud workload types (virtual machines, containers, serverless functions) through a single integrated system. The platform uses workload-type detection and adaptive scanning mechanisms to automatically adjust its behavior based on the target workload, eliminating the need for separate specialized scanning solutions while maintaining comprehensive vulnerability coverage.
Solution Approach 2:
The patent segments the scanning functionality into modular components that can be dynamically selected and executed based on workload type. The system divides the scanning process into distinct phases (discovery, analysis, reporting) and uses workload-specific adapters for different cloud environments, allowing comprehensive coverage through organized modularity rather than monolithic complexity.
3Reliability
If comprehensive vulnerability scanning is performed across all cloud entities, then security detection capability is improved, but resource consumption increases
Solution Approach 1:
The patent implements risk-based prioritization that performs comprehensive scanning on high-risk entities while using optimized or sampled scanning approaches for lower-risk entities. The system calculates risk scores based on entity characteristics, exposure, and vulnerability likelihood, then adjusts scanning depth and frequency accordingly, achieving effective security detection with reduced overall resource consumption.
Solution Approach 2:
The patent dynamically changes scanning parameters (such as scan depth, frequency, and intensity) based on entity risk profiles, historical vulnerability data, and current security context. High-priority entities receive intensive scanning with detailed parameter analysis, while low-priority entities receive lighter scanning, optimizing the balance between detection capability and resource usage through parameter adaptation.
Data Source
AI summary
A method for optimizing resource deployment in a cloud computing environment based on a cybersecurity inspection is presented. The method includes: detecting a plurality of entities deployed in a cloud computing environment; generating a representation of the cloud computing environment in a security database; associating each entity of the plurality of entities with a software-based function in the cloud computing environment; determining a resource utilization based on the software-based function and the generated representation; generating an instruction to deploy a second cloud computing environment based on the software-based function and further based on minimizing the determined resource utilization; and deploying the second cloud computing environment.