Endpoint sensor → customer infrastructure → customer dashboard. Three components, one architectural rule: customer data never touches AiEGIS Ltd.
Employee Laptop sends encrypted log stream to Customer's Cloud / VPC. Endpoint runs network proxy (detects AI API calls, captures prompt+response+tool calls). Browser extension catches Chrome/Firefox chatbot UI usage. Customer Cloud runs AiEGIS Dashboard (Docker) with search, real-time alerts, audit log export, policy engine. AiEGIS Ltd cloud sees only license validity + software updates. NOT prompts, responses, customer data.
Captures: outbound network requests to AI vendor APIs.
Vendor coverage roadmap (v1 target, Q3 2026 alpha for design-partner pilots): OpenAI, Anthropic, Google, Microsoft Copilot, Perplexity, Cursor, Cohere.
Per-call data: prompt, response, model, tool calls, latency, status, employee ID.
Does NOT: capture personal user content, decrypt non-AI traffic, run as root, modify packets.
Footprint (design target): <2% CPU, <50MB RAM, <100kbps bandwidth. v1 measurements published in pilot agreements.
Platforms: macOS Endpoint Security framework + Network Extension; Windows Filtering Platform driver; Linux eBPF (no kernel module).
Captures: prompts + responses inside chat interfaces of chat.openai.com, claude.ai, copilot.microsoft.com, gemini.google.com.
Why separate: web AI uses TLS to vendor cloud — endpoint sensor sees TLS handshake but not plaintext. Browser extension sees plaintext at DOM level.
Privacy-first: only runs on configured AI domains. Browsing other sites never inspected.
Deployed where customer chooses:
Stack: PostgreSQL + Python backend + React UI. Open-source components, audit-friendly.
Features:
Data ownership: every byte in customer infra. AiEGIS Ltd has no copy, no access, no key.
Minimum required to maintain the service:
That's it. We sell software. Customer owns data.
| Article | What it requires | What AiEGIS provides |
|---|---|---|
| 9 | Risk management | Per-AI-call risk classification |
| 10 | Data governance | What data went where |
| 11 | Technical documentation | System architecture exposed |
| 12 | Record-keeping | Per-employee per-call log |
| 13 | Transparency | Disclosure-ready audit reports |
| 14 | Human oversight | Real-time policy enforcement |
| 15 | Robustness | Anomaly detection on AI behavior |
| 50 | AI disclosure | Detection of synthetic / model-generated outputs |
| 72 | Post-market monitoring | Continuous behavioral baseline |
Penalties for non-compliance: up to 7% of global revenue. Article 50 transparency enforcement: August 2 2026. Articles 9-15, 72 (high-risk): December 2 2027.
We're transparent about scope. v1 covers approximately 80% of typical enterprise AI usage. The remaining 20% is on the v2 roadmap (3-6 months post-pilot):
If your CISO use-case requires any of these for v1, talk to us — we can scope a custom integration in design-partner agreements.