For this demo implementation, due to time constraints, I kept everything in a single configuration file IPs, ports, tokens, usernames, and passwords. It was purely for a controlled, non-production setup.
In a real-world environment, I would use AWS Secrets Manager to store all sensitive data. Instead of hardcoding credentials, the config would reference secret ARNs (e.g., Jenkins_Token_Arn = "arn:aws:secretsmanager:..."), and the AI agent would retrieve the secret dynamically at runtime using IAM-based access.
Hardcoding is acceptable for a quick demo, but for production-grade DevSecOps workflows, centralized secret management with proper IAM controls is non-negotiable.
That answers the question perfectly. I built a Zero-knowledge secrets management approach for ai agents. The ai can make authenticed requests with your secrets without seeing their values
How do you handle secrets when using this workflow?
For this demo implementation, due to time constraints, I kept everything in a single configuration file IPs, ports, tokens, usernames, and passwords. It was purely for a controlled, non-production setup.
In a real-world environment, I would use AWS Secrets Manager to store all sensitive data. Instead of hardcoding credentials, the config would reference secret ARNs (e.g., Jenkins_Token_Arn = "arn:aws:secretsmanager:..."), and the AI agent would retrieve the secret dynamically at runtime using IAM-based access.
Hardcoding is acceptable for a quick demo, but for production-grade DevSecOps workflows, centralized secret management with proper IAM controls is non-negotiable.
That answers the question perfectly. I built a Zero-knowledge secrets management approach for ai agents. The ai can make authenticed requests with your secrets without seeing their values
Could you please share all the details at simplynadaf@gmail.com ?