<\/span>4. The Remediation Plan<\/span><\/h3>\nWe aren’t just patching this. We are re-engineering the entire AI stack. Here is the step-by-step plan for the next two weeks:<\/p>\n
\n- Immediate Lockdown<\/strong>: All AI services are currently disabled. They will remain disabled until they pass a security audit.<\/li>\n
- Database Hardening<\/strong>: The database user used by the AI service has been stripped of all permissions except for
SELECT<\/code> on three specific, non-sensitive tables. All DELETE<\/code> and UPDATE<\/code> operations are now handled by a separate, non-AI-accessible microservice.<\/li>\n- Dependency Pinning<\/strong>: We are moving to a single, unified Docker base image for all AI workloads.\n
\n- Base:
nvidia\/cuda:12.2.0-base-ubuntu22.04<\/code><\/li>\n- Python: 3.11.2<\/li>\n
- PyTorch: 2.1.0+cu121<\/li>\n
- Transformers: 4.34.0<\/li>\n<\/ul>\n<\/li>\n
- Implementation of the “Gatekeeper”<\/strong>: A new service,
ai-validator<\/code>, is being written in Rust (for speed and safety). It will sit between the LLM and the rest of our infrastructure. It will perform the four stages of validation mentioned in Section 3.<\/li>\n- Observability Overhaul<\/strong>: We are adding custom Prometheus exporters for GPU metrics and token usage. We will have dashboards that show exactly how much each “artificial intelligence” feature is costing us in real-time.<\/li>\n
- Circuit Breakers<\/strong>: We are implementing the
resilience4j<\/code> pattern (or the Python equivalent) to ensure that if the AI service starts failing or slowing down, it fails fast and doesn’t take the rest of the system with it.<\/li>\n<\/ol>\n