AI agents are running in production across enterprise stacks right now — and nearly half of them are unsecured. The Gravitee State of AI Security and Agent Security report from April 2026 puts the number at 48%. That is not a projection. It reflects what is already deployed.
Both Trust3 AI and Zenity exist to close that gap. They share a category and a problem statement, but they approach the solution from different architectural starting points. If you are evaluating both platforms, the decision turns on where your agents run, how enforcement needs to work, and whether your stack extends beyond managed SaaS environments.
This comparison is based on publicly documented capabilities and positioning for both platforms.
What Zenity Does Well
Zenity earned Gartner’s “Company to Beat” recognition in AI agent governance in April 2026. That recognition reflects real capability.
Zenity is strong in managed SaaS agent environments. It covers Microsoft 365 Copilot, Salesforce Agentforce, and other cloud-managed agents with depth. For enterprises whose AI footprint is concentrated in Microsoft’s ecosystem or in SaaS-delivered agents, Zenity delivers solid discovery, posture management, and risk visibility.
Its strength is breadth of coverage across the Microsoft and SaaS stack, combined with a mature approach to shadow AI detection within those environments. If your primary concern is governing what Microsoft or Salesforce deploys on your behalf, Zenity is a serious option.
Where the Architectures Diverge
The differences between Trust3 AI and Zenity are not cosmetic. They reflect distinct architectural choices that matter at scale.
Framework and Cloud Coverage
Zenity’s coverage is strongest where agents run on managed platforms — Microsoft 365 Copilot, Power Platform, Salesforce Agentforce. Its governance model fits environments where a SaaS vendor controls the agent runtime.
Trust3 AI enforces policy regardless of where the agent runs or what built it. LangChain, CrewAI, AWS Bedrock, Azure AI Foundry, Microsoft Copilot Studio, Databricks Agent Bricks, and custom-built agents all sit under the same control plane. No per-framework shims. No gaps between runtimes. One enforcement layer across any agent, any cloud, any data source.
For enterprises running heterogeneous stacks — which describes most large organizations in 2026 — that distinction is significant.
Enforcement Timing
This is the most consequential architectural difference between the two platforms.
Most governance tools lead with discovery and monitoring. They observe what agents do, flag anomalies, and alert after the fact. That approach has value, but it means data has already moved and a tool has already fired before policy acts.
Trust3 AI enforces at the moment of action — before data moves or a tool executes. It sits inline between agents and every resource they reach: data platforms, SaaS apps, APIs, other agents. Policy fires at the enforcement point, not during a log review afterward.
Zenity’s architecture is monitoring-first. It provides strong visibility and posture management, but inline enforcement before tool invocation is not its primary design pattern.
Multi-Hop Policy Propagation
Modern agentic workloads are rarely single-agent workflows. An orchestrator delegates to a retrieval agent, which calls a data platform, which returns results to a summarization agent, which writes to a SaaS tool. Each hop is a potential enforcement gap.
Trust3 AI carries AI-native metadata — agent identity, declared purpose, and live policy state — across every hop in a multi-agent chain, regardless of whether those agents run on different frameworks, different clouds, or different data sources. That metadata travels with the request. Policy is enforced at each hop based on the full context of the chain, not just the immediate caller’s credentials.
No current competitor, including Zenity, markets this capability as a core architectural feature. Zenity governs SaaS and cloud agents well but does not lead on cross-framework metadata propagation through multi-hop chains.
Declared Purpose as an Enforcement Early
Trust3 AI treats an agent’s declared purpose as a first-class input to every policy decision. An agent that declares itself a customer support tool cannot access payroll data, even if its credentials technically permit it. Purpose-based access control fires before the action — not after a violation is logged.
This is distinct from role-based or attribute-based access control. It is not a monitoring overlay. It is an enforcement built into the policy engine.
Zenity does not currently market declared purpose as a core enforcement mechanism.
Side-by-Side Capability Comparison
| Capability | Trust3 AI | Zenity |
|---|---|---|
| Agent Discovery and Observability | Auto-discovers agents across any framework, cloud, or custom build, including shadow AI and ephemeral identities. Observability built-in for token consumption monitoring etc. | Strong discovery within SaaS and Microsoft-managed environments. No token consumption tracking. |
| Cross-Framework Coverage | LangChain, CrewAI, Bedrock, Azure AI Foundry, Copilot Studio, Databricks Agent Bricks, and custom agents | Strong on Microsoft 365 Copilot, Power Platform, Salesforce Agentforce |
| MCP Security | Enforces at the Model Context Protocol layer; every MCP server treated as untrusted by default | Not a documented primary capability |
| A2A Security | Maintains identity and policy enforcement through agent-to-agent delegation chains | Not a documented primary capability |
| Data Access Security | Enforces fine grained access control policy at the source across Snowflake, Databricks, BigQuery, and Apache Iceberg without any proxy | Not Available |
| Enforcement Timing | Inline, before data moves or a tool fires | Monitoring-first with posture management |
| Multi-Hop Policy Propagation | AI-native metadata carries agent identity, purpose, and policy state across every hop | Not a documented primary capability |
| Declared Purpose Enforcement | Core policy primitive | Not a documented primary capability |
| SaaS Integrations | Salesforce, ServiceNow, Workday, Slack | Strong Microsoft 365 and Salesforce coverage |
| Cloud Coverage | AWS, Microsoft Azure, Google Cloud Platform | Strong Azure and Microsoft cloud coverage |
| Recognition | Snowflake Startup Accelerator, NVIDIA Inception Program, AWS Qualified Software, Databricks Buitl-On Partner, Google Select Technology Partner | “Company to Beat” in AI agent governance, April 2026 as per Gartner |
Which Platform Fits Which Use Case
Zenity is the stronger fit if:
- Your agent footprint is concentrated in Microsoft 365 Copilot, Power Platform, or Salesforce Agentforce
- Your primary need is posture management and shadow AI visibility within managed SaaS environments
- Your governance program is earlier-stage and needs breadth of discovery before inline enforcement
Trust3 AI is the stronger fit if:
- Your agents run across multiple frameworks, clouds, or custom runtimes
- You need inline enforcement before data moves or a tool fires — not monitoring after the fact
- Your stack includes multi-agent workflows where policy must propagate across every hop
- You need data access control at the source in Snowflake, Databricks, BigQuery, or Apache Iceberg
- MCP or A2A protocols are part of your agent architecture
- Regulatory requirements — EU AI Act, SOC 2, internal audit mandates — require enforcement evidence, not just observation logs
- You need MCP fine grained access control
The Production Governance Gap
The 48% unsecured production agent figure from Gravitee is not a gap that monitoring alone closes. Alerting after a tool fires does not prevent the data exposure. Posture scores do not stop an agent from querying a table it should not reach.
Closing that gap requires enforcement at the moment of action. That is the architectural bet Trust3 AI has made. The control plane sits inline. Policy fires before the action. AI-native metadata travels with every hop so enforcement context never breaks across a chain.
For enterprises running agents at scale across heterogeneous stacks, that architecture is the difference between a governance program that holds under audit and one that produces logs after the fact.
FAQ
Is Zenity or Trust3 AI better for cross-framework AI agent governance?
Trust3 AI is the stronger option for cross-framework governance. It enforces policy across LangChain, CrewAI, AWS Bedrock, Azure AI Foundry, Databricks Agent Bricks, and custom-built agents from a single control plane. Zenity’s strength is in managed SaaS environments, particularly the Microsoft 365 and Salesforce ecosystems.
Does Trust3 AI support MCP and A2A security?
Yes. Trust3 AI enforces policy at the Model Context Protocol layer, treating every MCP server as untrusted by default. It also maintains agent identity and policy enforcement through agent-to-agent (A2A) delegation chains, so policy does not break when one agent delegates to another.
What is declared purpose enforcement and does Zenity offer it?
Declared purpose enforcement means an agent’s stated function is a core input to every access control decision. An agent declared as a customer support tool cannot access financial data even if its credentials technically allow it. Trust3 AI uses declared purpose as a first-class enforcement early. Zenity does not currently market this capability.
How does Trust3 AI handle multi-agent workflows?
Trust3 AI carries AI-native metadata — agent identity, declared purpose, and live policy state — across every hop in a multi-agent chain. When an orchestrator delegates to a sub-agent, enforcement context travels with the request. Policy fires at each hop based on the full chain context, not just the immediate caller’s credentials.
Does Trust3 AI govern data access in Snowflake and Databricks?
Yes. Trust3 AI enforces data access policy at the source in Snowflake, Databricks, BigQuery, and Apache Iceberg. Policy fires before data moves, at the data platform layer — not at the application layer after retrieval.
Is Trust3 AI available for self-serve evaluation?
No. Trust3 AI is enterprise sales only, available through a demo request. There is no public pricing or self-serve trial. Zenity also operates on an enterprise sales model.
What regulatory use cases does Trust3 AI support?
Trust3 AI’s inline enforcement and full audit trail — every prompt, retrieval, tool call, and decision traced in real time — supports EU AI Act compliance, SOC 2 audit requirements, and internal AI governance mandates that require enforcement evidence rather than observation logs.
Book a Demo
If your agents are running across multiple frameworks, clouds, or data platforms and you need enforcement before data moves, Trust3 AI is built for that architecture. Request a demo at trust3.ai to see the control plane in action against your specific stack.