Normalized claim
Productivity: 70% increase
GWM reported nearly a 70% increase in development efficiency.
GWM Modernizes Security Compliance Development with Amazon Q Developer on AWS China (Ningxia)
Use case typeCode assistantUpdated Jun 13, 2026
GWM modernized its security compliance system for multi-cloud governance across 14 global cloud environments by migrating the system to AWS China (Ningxia). The security team used Amazon Q Developer to refactor the development workflow, generate code in multiple languages, and support a security left-shift approach for earlier issue detection. The new architecture includes scanner data collection, analyzer risk detection, and tenant-isolated reporting, supported by Amazon Kinesis, Amazon Aurora MySQL, Amazon OpenSearch Service, Amazon EKS, and AWS Graviton4.
- Organization
- GWM
- Industry
- Automotive
- Location
- China
- Published
- May 2026
Reported outcomes
+100%
quantified impactOther quantified impact
+70%productivity+25%quantified impact+23%quantified impact−20%cost
Strategic outcomes
Risk & complianceEnabled real-time security governanceNew product / capabilityBuilt tenant-isolated reporting architectureSpeed & agilityImproved development efficiencyCost efficiencyReduced resource costs
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 25% increase
Test coverage increased by 25%.
Normalized claim
Quantified impact: 23% increase
Query performance improved by 23% with a 100% query success rate.
Normalized claim
Quantified impact: 100% increase
Query performance improved by 23% with a 100% query success rate.
Normalized claim
Cost: 20% decrease
Resource costs were reduced by 20%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- GWM
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1AI-assisted software development
- 2Security operations
- 3Compliance automation
- GWM needed to modernize a security compliance system across 14 global cloud environments.
- The development team size had been reduced by 50%, creating pressure to improve delivery efficiency.
- The team needed better code quality, test coverage, and security compliance while also reducing performance bottlenecks and resource costs.
- GWM migrated the security compliance system to the AWS China (Ningxia) Region.
- Developers used Amazon Q Developer to generate production-oriented code from natural-language security policy requirements.
- The system was built as a three-layer governance architecture covering data collection, intelligent analysis, and multi-tenant reporting.
- Amazon Kinesis and Apache Flink were used for stream processing; Amazon Aurora MySQL stored processed data; Amazon OpenSearch Service supported real-time search and analysis; Amazon EKS and AWS Graviton4 supported deployment and cost optimization.
- GWM reported nearly a 70% increase in development efficiency.
- Test coverage increased by 25%.
- Query performance improved by 23% with a 100% query success rate.
- Resource costs were reduced by 20%.
- The system enabled real-time security governance and tenant-isolated reporting across global cloud environments.
Architecture
A three-layer security compliance platform: scanner data collection from multi-cloud workloads and IDC hosts; analyzer risk detection using rules and processed data in Amazon Aurora MySQL; tenant-isolated reporting via a web portal. The solution used Amazon Kinesis and Apache Flink for stream processing, Amazon OpenSearch Service for real-time search and analysis, Amazon EKS for containerized deployment, and AWS Graviton4 instances for performance and cost optimization.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 27, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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