About This Architecture

AI-powered secure coding platform with multi-tier architecture combining prompt analysis, local LLM inference, and real-time safety checks. User requests flow through WAF and CDN to a web UI, then through Prompt Analyzer, Sensitivity Analysis Module, and Risk Scoring Engine before reaching the AI Code Generation Model with Ollama. Generated code passes through Code Safety Checker Module, Risk Report Generator, and Logging and Audit System, with all patterns stored in Vector Database and Audit Object Storage for compliance and feedback loops. This architecture demonstrates defense-in-depth for AI systems by isolating safety validation, enforcing authentication at the safety tier, and maintaining immutable audit trails. Fork and customize this diagram on Diagrams.so to adapt security controls, swap LLM providers, or extend the audit pipeline for your organization's governance requirements.

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How do you architect a secure AI code generation platform that validates prompts, checks generated code for safety, and maintains audit trails?

This diagram shows a defense-in-depth approach: user prompts flow through Prompt Analyzer, Sensitivity Analysis Module, and Risk Scoring Engine before reaching the local LLM. Generated code is validated by Code Safety Checker Module, with all decisions logged and patterns stored in Vector Database for continuous improvement and compliance auditing.