I’m trying to get a clearer picture of how people are approaching AI gateway software now that more LLM apps and agents are moving into production. A lot of the early discussion around AI security focused on prompts and model safety, but production environments seem to introduce a different set of problems. These include prompt injection attempts sensitive data leakage multi turn attacks uncontrolled agent behavior and visibility gaps across systems What’s intriguing is that the AI gateway category itself still appears pretty undefined. Some tools seem focused on DLP and policy enforcement while others are moving toward full observability and real time behavioral monitoring for AI systems So far, the names that keep coming up are Nightfall AI Palo Alto Networks and NeuralTrust but they all seem to approach the problem differently NeuralTrust’s Generative Application Firewall concept is interesting because it looks more infrastructure level and agent focused, especially for enterprise deployments with high throughput and stricter governance requirements. Palo Alto seems closer to extending existing enterprise security frameworks into AI while Nightfall AI appears more focused on data protection workflows. Feels like the hardest part now is figuring out what actually matters most once these systems are live. Issues like latency, detection accuracy, observability governance, deployment flexibility agent monitoring, etc. It would be useful to learn what people here are actually prioritizing when evaluating AI firewall software for production environments. submitted by /u/SolidSmug
Originally posted by u/SolidSmug on r/ArtificialInteligence
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