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In this episode of AI Powerplay, we dive into a real-world case study with a major airline using WitnessAI to secure how employees interact with generative AI systems like ChatGPT, Gemini, Grok, and others. When flight crews or employees search for aviation safety protocols, they often face two challenges: Scattered internal resources across Confluence or SharePoint. Inconsistent answers from generative AI apps that are non-deterministic and can vary by model. This creates risk when asking mission-critical questions like: “How do I extinguish a fire in-flight?” or “What should I do if a panel falls off mid-flight?” WitnessAI solves this problem with AI behavioral intelligence and prompt routing. By detecting the intention of the prompt (e.g., aircraft safety, cabin procedures, in-flight emergencies), the system can reroute the query away from public LLMs to an internal LLM trained on the airline’s safety protocols. ✅ Consistent, policy-aligned answers ✅ Customizable guardrails for aviation, compliance, and safety ✅ Multi-language support (Spanish, Japanese, etc.) ✅ Centralized visibility into employee AI interactions This approach ensures employees always get the correct, internally approved response—reducing reliance on inconsistent public AI systems while improving compliance and safety outcomes. WitnessAI acts like an AI firewall for enterprises, applying guardrails to ensure safe, secure, and reliable AI usage at scale. ⏱️ Timestamps: 00:00 – Airline AI safety challenge 01:54 – Why generative AI answers can’t be trusted for safety 02:40 – How WitnessAI reroutes unsafe prompts 03:50 – Policy engine demo inside WitnessAI console 05:47 – Live example with ChatGPT prompt routing 07:05 – Multi-language guardrails and enterprise use cases 10:13 – How to run this play in your own organization 🔔 Subscribe to WitnessAI for more real-world AI security examples. https://witness.ai