
Developed a secure AI-assisted sample application for the aws-samples/aurora-dsql-samples repository, enabling natural language querying of Aurora DSQL databases. The project featured an end-to-end AI agent, AWS Lambda handler, infrastructure scripts, and a gateway, all designed with a strong emphasis on security. Security hardening included least-privilege database access, robust SQL validation, input sanitization, and scoped IAM policies. The solution was validated with 37 unit tests and live deployment against a DSQL cluster. Documentation was expanded to guide production deployment and best practices. Core technologies and skills applied included Python, SQL, AI development, database management, and security best practices.
2026-04 Monthly Summary for aws-samples/aurora-dsql-samples: Delivered a secure AI-assisted sample for querying Aurora DSQL and implemented comprehensive security hardening. Business value: enabling secure, natural-language data access with auditable governance and quicker adoption.
2026-04 Monthly Summary for aws-samples/aurora-dsql-samples: Delivered a secure AI-assisted sample for querying Aurora DSQL and implemented comprehensive security hardening. Business value: enabling secure, natural-language data access with auditable governance and quicker adoption.

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