HEAD-TO-HEAD
Zenity vs. Obsidian
Zenity watches the agent. The breach plays out in Salesforce, Workday, and your third-party apps.
Agent telemetry can show that a tool call happened. But the real risk lives downstream: what data was touched, what permissions were consumed, what the service account could access, and what activity appeared inside the SaaS app.
Inventory should do more than list agents. It should show which ones combine broad SaaS reach, sensitive-data access, service-account permissions, IdP gaps, and risky activity. That way, teams know what to investigate, restrict, or remediate first.
Runtime control shouldn’t rely only on prompts, behavior, and tool calls. It should also understand SaaS state, identity, permissions, IdP federation, data sensitivity labels, and app activity. That way teams can block or flag the actions that create real business risk.
Zenity is an AI agent security platform focused on agent inventory, build-time governance, prompt and jailbreak analysis, and inline runtime policy, with particular depth across Microsoft Copilot Studio and Power Platform.
Zenity’s approach is built around the agent itself: what agents exist, how they’re built, what prompts they use, what tools they call, and how they behave at runtime. That gives security teams visibility into agent-layer risk and a way to apply policies close to agent execution.
The gap is context. Every AI agent operates on top of SaaS applications, identity systems, service accounts, OAuth grants, permissions, and audit trails. The agent layer can show that a tool call happened, but it may not show what the receiving app allowed, what the service account could access, whether the app was federated, or what downstream activity occurred after the call.
Both companies are in AI agent security. The difference is the data each platform reads. Zenity goes deep on the agent layer. Obsidian connects agent activity to the SaaS and identity context that determines whether the activity is actually risky.
Obsidian's Knowledge Graph ties identity, permissions, token grants, integrations, and activity together across every connected application. When a third-party vendor is compromised, Obsidian doesn't wait for the disclosure. Network effects mean that signal is already flowing across every environment we protect.
The result is faster investigations, cleaner blast radius attribution, and remediation decisions backed by what actually happened, not what could have.
Both platforms secure AI agents. The difference is the layer each platform prioritizes and the data each one reads.
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Copilot Studio and Zenity help govern the agent build layer: prompts, behavior, and tool calls. But once Copilot agents are live, they act inside SaaS apps like Salesforce, ServiceNow, Workday, and M365 through OAuth tokens and service accounts.
That’s where Zenity can struggle. Without native SaaS connectors and SaaS logs, it’s hard to show what the agent actually did inside the receiving app, what permissions or app configurations enabled it, and which verified identity or service account was behind the action. Obsidian reads that SaaS and identity context natively, so you can understand the agent’s real downstream impact across apps.
Both enforce at runtime. The difference is what each policy reads when it fires. Agent-side runtime can control what an agent does at the tool-call level. Obsidian extends runtime decisioning with SaaS state, identity, and permissions, so policies can be more specific over time: for example, don't touch a file in OneDrive labeled sensitive, when the agent was built by a specific user, calling on behalf of a specific identity. That granularity comes from reading the receiving app's state and identity context, not just the agent's tool calls.
Inventory is only the starting point. Obsidian shows who built the agent, who ran it, and what the service account inside the SaaS app is permissioned to do. That gives defenders the context to understand which agents create real business risk and align configuration to that risk.
You can, but there's meaningful overlap at the agent-platform layer. In shared AI platforms like Copilot Studio, both products may rely on similar agent-platform telemetry. The cleaner answer is to pick the platform that also reads the SaaS and identity context the other doesn't.
Posture detection tells you an agent looks risky. By the time a posture finding lands, the agent has often already executed. Runtime enforcement fires at the tool call, before the action completes. Both platforms have moved to runtime for this reason. The remaining question is what each runtime can read: agent inputs and outputs alone, or also the SaaS state and identity context that decide whether the action is actually risky.
99.99% uptime over the last 12 months. Regional hosting across the US, Europe, Saudi Arabia, and Australia. Granular RBAC scoped per app. Production-safe connectors with bulk-API support. Obsidian connects to your most critical SaaS apps and collects activity data without disrupting them. Learn more about our certifications and attestations.
These come from real customer environments, including customers who've evaluated Zenity and Obsidian.
See what gives Obsidian the edge over others
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