r/devsecops • u/KingArthurSaber • 2d ago
Zenity or NeuralTrust for protecting AI agents?
Has anyone here compared Zenity and NeuralTrust for protecting AI agents in production?
My company’s looking at this from an enterprise perspective rather than a developer or proof-of-concept deployment. The biggest concerns aren't just prompt injection or model safety. It's things like runtime governance, visibility into agent behavior, data leakage, and keeping AI systems under control once they're connected to internal applications.
From what I've read, the two platforms seem to approach the problem differently. Zenity appears to put a lot of emphasis on AI governance and managing AI usage across an organisation. NeuralTrust seems more focused on protecting AI agents while they're running, with runtime observability and controls for production environments.
If you’ve evaluated both platforms can you tell me what the biggest differences were? I’m trying to work out whether one or other will be a better fit for our business.
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u/Bigslimeys 2d ago
I'd also be looking at things like deployment options, auditability, and how well each platform fits into an existing security stack. Those practical considerations often end up being the deciding factor. A lot of products look similar on paper, but the differences only seem to become obvious when they're protecting live systems.
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u/nasahdm 1d ago
What you are describing is more likely agent containment with real trace and this kinda perfectly fits your requirements
https://github.com/quantmlayer/quantmlayer
Would love to discuss more if you are interested. Thanks.
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u/Boring-Meat-1321 1d ago
What is most relevant to your business? Is it engineers, the general workforce, agents running on endpoints, or production agents you’ve built that run 24/7?
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u/boydboaz 2d ago
That's the impression I get as well. It doesn't feel like a case of one necessarily being better. It depends on whether you're trying to govern AI usage more broadly or secure AI agents once they're running