AI-Powered Gateway Security App
Budget: ₹37,500 – ₹75,000 INR
You’ll help me turn a concept—“rolling out a custom Gateway system solution”—into a production-ready security application. The core feature set centres on vulnerability scanning for web applications; the tool must inspect HTTP/S traffic, flag weaknesses in real time, and feed findings into an ML model that refines detection accuracy with each scan.
I’ve already chosen the stack: Python for the service layer, Kubernetes for orchestration, and Google Cloud as the deployment target. Familiarity with container-native workflows (Helm, Cloud Build, GKE) will let you move quickly. If you have experience weaving Machine Learning and Artificial Intelligence techniques into security pipelines, that will be invaluable because I’d like automated enrichment—think anomaly scoring or pattern clustering—right in the gateway flow.
Deliverables
• Clean, well-documented Python codebase implementing the gateway, scanning engine, and ML integration
• Kubernetes manifests/Helm charts that run the full stack end-to-end on GKE
• CI/CD pipeline scripts for seamless rollout in Google Cloud
• Usage guide and API documentation describing how to plug the gateway in front of any web application
• Test suite demonstrating detection accuracy and system resilience under load
I’ll be available for quick feedback loops and can supply sample traffic logs to jump-start model training. Once we hit an MVP that blocks and reports web-app vulnerabilities reliably, we’ll plan the next phase together.
I’ve already chosen the stack: Python for the service layer, Kubernetes for orchestration, and Google Cloud as the deployment target. Familiarity with container-native workflows (Helm, Cloud Build, GKE) will let you move quickly. If you have experience weaving Machine Learning and Artificial Intelligence techniques into security pipelines, that will be invaluable because I’d like automated enrichment—think anomaly scoring or pattern clustering—right in the gateway flow.
Deliverables
• Clean, well-documented Python codebase implementing the gateway, scanning engine, and ML integration
• Kubernetes manifests/Helm charts that run the full stack end-to-end on GKE
• CI/CD pipeline scripts for seamless rollout in Google Cloud
• Usage guide and API documentation describing how to plug the gateway in front of any web application
• Test suite demonstrating detection accuracy and system resilience under load
I’ll be available for quick feedback loops and can supply sample traffic logs to jump-start model training. Once we hit an MVP that blocks and reports web-app vulnerabilities reliably, we’ll plan the next phase together.