Zenity is one of the best-known vendors in agentic AI security, and its public positioning centers on securing the agents enterprises build and buy — security posture management, threat detection, and response across agent platforms such as Microsoft Copilot Studio, Salesforce Agentforce, and low-code/no-code environments, per its public materials. It is an enterprise, buyer-facing platform whose public history and positioning began in low-code/no-code application security before expanding to agentic AI.
Teams searching for alternatives are usually not doubting the category — they are trying to match a tool to a problem that differs from the one Zenity centers: custom-built agents rather than platform-built ones, runtime enforcement rather than posture visibility, or a different operating and procurement model (open source, self-hosting, EU residency, usage-based entry). This post lays out the evaluation lenses and a factual survey of options. Disclosure up front: Praesidia is our product and appears below; we hold its entry to the same public-claims-only standard as every other vendor, and as of July 2026 you should verify all capabilities against each vendor's current documentation.
What Zenity focuses on, in its own terms
From Zenity's public positioning: visibility into the agents an organization has built and deployed across enterprise agent platforms; posture management — identifying risky configurations, excessive permissions, and policy violations in those agents; and detection and response for agent-related threats. The center of gravity is the enterprise agent estate — especially agents created in SaaS and low-code platforms by business users — viewed through a security-team lens.
That focus is genuinely valuable, and it also defines the boundary that sends buyers looking at the wider category: posture tooling tells you which agents are risky; it is a different layer from the infrastructure that enforces what any agent can do at runtime. The distinction between those two layers is drawn precisely in runtime security vs AI security posture management.
The evaluation lenses
Before comparing vendors, decide which of these is your primary problem:
- Where your agents come from. Built by business users inside Copilot Studio/Agentforce-style platforms, or built by engineers on frameworks and APIs? Posture products differ sharply in coverage between the two.
- Visibility vs enforcement. Do you need to find and assess agents (posture, inventory, detection), or to govern them in-band (identity, per-action authorization, guardrails, budgets, kill switches)?
- Content-layer depth. Is prompt-injection and data-leakage filtering your core need, or one layer among several?
- Operating model. SaaS-only or self-hostable; open or closed source; data residency options; entry price and procurement weight.
The alternatives
Praesidia (disclosed: our product)
Praesidia is an AI agent governance control plane: every agent, application, and MCP server gets its own identity and credentials; policies, bidirectional guardrails, budgets, and rate limits are enforced in-band at runtime; and every action lands in an append-only, hash-chained audit log. It is enforcement-first rather than posture-first — the runtime security layer — with compliance surfaces (EU AI Act classification, evidence collection) built on top. Its distinguishing operating-model choices: an open-source core under the Apache license, EU data residency, and a $0 free tier. The honest boundary: Praesidia governs the agents you route through it; it is not a scanner that discovers agents living inside third-party SaaS agent builders.
Lakera
Lakera's public positioning is AI-native content defense: prompt-injection detection and content moderation delivered via API, informed by its widely known Gandalf security game and threat research. It is a strong candidate when the content layer is your specific problem; broader estate governance (identity, budgets, audit) is outside its stated scope. We cover that category in depth in Lakera alternatives for LLM guardrails.
Prompt Security (SentinelOne)
Prompt Security, whose acquisition by SentinelOne was publicly announced in 2025, positions around GenAI security for the enterprise: protecting employee AI usage (browser-level controls), applications, and — per its public materials — MCP and agent traffic, now within SentinelOne's platform context. A natural evaluation when endpoint/employee AI usage and application protection are the same conversation. See Prompt Security alternatives for the fuller comparison.
Microsoft-native tooling
For estates deep in Microsoft's ecosystem, Microsoft's own stack — Entra Agent ID for agent identity, Purview for data governance, and the security controls in Copilot Studio and Azure AI Foundry, per Microsoft's public documentation — covers meaningful ground for agents inside that estate. The trade-off is reach: coverage is strongest within Microsoft's platforms, and cross-vendor agent estates still need an independent layer — the argument developed in Microsoft agent governance and independent control planes.
Open-source guardrail components
Projects such as NVIDIA's NeMo Guardrails and Meta's Llama Guard family (both publicly documented) provide programmable content-safety layers teams can self-host and embed. They are components rather than platforms — you assemble identity, audit, policy, and operations around them — which suits teams with platform-engineering capacity and unusual requirements. The realistic cost of that assembly is the subject of build vs buy for agent governance.
Choosing between them
A compressed decision path: if your risk lives in business-user-built agents inside SaaS platforms, shortlist posture-focused platforms like Zenity and compare Microsoft-native controls for the Microsoft slice. If your risk lives in engineer-built agents acting through APIs, tools, and MCP servers, shortlist governance control planes (Praesidia's category) and guardrail specialists, and decide whether posture tooling joins later. If you need both — common in large enterprises — the layers compose: posture tools find and assess; control planes enforce. Whatever the shortlist, run a proof of concept against your own failure modes with the RFP checklist as the question bank.
Common questions
What is Zenity known for? Per its public positioning: agentic AI security posture management, observability, and detection/response for agents built and deployed across enterprise platforms — with particular strength in low-code/no-code and SaaS agent builders like Microsoft Copilot Studio.
Why do teams look for Zenity alternatives? Most commonly a problem-shape mismatch rather than dissatisfaction: custom-built agent estates needing runtime enforcement rather than posture visibility, content-layer depth as the core need, or operating-model requirements (open source, self-hosting, EU residency, low-friction entry) that point to a different category of vendor.
Is Praesidia a direct Zenity replacement? Not one-for-one. The overlap is agent security; the centers differ — Zenity's public focus is discovering and assessing the agent estate (posture), while Praesidia enforces governance in-band for agents routed through it (runtime). Some organizations legitimately want both layers. We are the vendor of one of them, so verify both against your own use case.
How should we evaluate vendors in this category? Proof of concept over demo: connect one or two real agents, then test that an unapproved tool call is blocked, a seeded secret is caught in-band, a budget hard-stops a loop, and a revoked credential dies immediately. Category labels blur exactly where these tests discriminate.