Wiz sees the cloud.
Trust3 AI sees the agent.
Both platforms touch AI security. They solve different layers of the problem, and most enterprises running agents in production need both. Wiz is built to give security teams a unified graph across code, cloud, and runtime. Trust3 AI is built to enforce access the moment an agent acts, natively inside the data platforms those agents reach.
Which platform fits your stack
Wiz
- Your priority is consolidating CNAPP capabilities across a large multi-cloud estate
- You need attack path modeling to prioritize which findings represent a real breach path
- You want one graph connecting application code to cloud exposure across the full development lifecycle
- Your team is already standardized on Wiz for cloud security posture management
Trust3 AI
- Agents are built on more than one framework, or your teams build their own
- Agents run across more than one cloud
- Policy has to be enforced before an action, not reported after it
- Multi-agent workflows hand work between agents and the originating identity has to survive the chain
- Agents query Snowflake, Databricks, BigQuery, or Iceberg and access has to be controlled at the source
- MCP servers and tool calls need fine-grained access control
- Auditors want enforcement evidence, not observation logs
Where Trust3 AI goes further than Wiz
Wiz consolidates CSPM, CIEM, and vulnerability management into one graph, with Attack Path Analysis that prioritizes the fraction of findings representing a real breach path. Here is where the architectures diverge for agents specifically.
Enforcement, not just a risk graph
Wiz answers what the environment around the agent looks like, correlating AI risk across a code-to-cloud-to-runtime graph. Trust3 AI answers what this specific agent is allowed to do right now, enforcing natively inside the data platform at the moment of the request. Visibility tells you what happened; Trust3 AI decides what happens next.
Native runtime enforcement inside Snowflake and Databricks
On Snowflake, Wiz extends its cloud security graph with data context, correlating risk with the rest of an organization’s cloud and identity posture. Trust3 AI enforces native runtime access control directly within Snowflake, applying purpose-based access at the moment an agent queries data, with zero proxy latency. On Databricks, Trust3 AI is a Databricks Built On Partner, enforcing just-in-time, purpose-based access so an agent’s access to a dataset expires automatically.
Enforcement before the action, not detection after
Wiz detects and responds to runtime threats using an eBPF sensor and log analysis — strong for catching and investigating what already happened. Trust3 AI enforces at the moment of action, before data moves or a tool executes. Policy fires first, then the action happens or it does not.
Identity survives multi-hop delegation
By the third hop in an agent chain, the originating user’s identity is usually gone and a service account is what the data layer sees. Trust3 AI carries agent identity, declared purpose, and live policy state across every hop; scope can only narrow, never expand, and the full chain is recorded as one correlated audit record.
Declared purpose as a first-class enforcement primitive
An agent declared as a customer support tool cannot reach payroll data even when its credentials would permit it. Grants are just-in-time and expire with the task — not currently something Wiz markets as a core enforcement mechanism.
Side-by-side capability comparison
| Capability | Trust3 AI | Wiz |
|---|---|---|
| Agent discovery and observability | Auto-discovers every agent across any framework, cloud, or custom build, including shadow AI and ephemeral identities. End-to-end traces of every agent step, input to output. | Continuous discovery of AI models, agents, and MCP servers as part of the broader cloud asset graph. |
| Cross-framework coverage | LangChain, Amazon Bedrock, Azure AI Foundry, Copilot Studio, Databricks, and custom agents, plus 50+ platforms | AI visibility extends from Wiz’s existing CNAPP graph; strongest for AI workloads already inside a Wiz-covered cloud estate |
| MCP security | Enforces at the Model Context Protocol layer. Every MCP server is untrusted by default. | Discovers MCP servers as an asset type in the graph; not a documented protocol-layer enforcement capability |
| A2A security | Identity and policy enforcement maintained through agent-to-agent delegation chains | Not a documented primary capability |
| Data access enforcement | Row, column, and tag-based policy enforced natively at the source across Snowflake, Databricks, BigQuery, and Apache Iceberg, with no proxy hop | Visibility and risk correlation for data exposure via graph analysis and integration, not native runtime enforcement |
| Enforcement timing | Inline, before data moves or a tool fires | Detects and responds to runtime threats via an eBPF sensor and log analysis |
| Multi-hop policy propagation | AI-native metadata carries agent identity, declared purpose, and policy state across every hop | Not a documented primary capability |
| Declared purpose enforcement | Core policy primitive, evaluated per request | Not a documented primary capability |
| Cloud coverage | AWS, Microsoft Azure, Google Cloud | AWS, Microsoft Azure, Google Cloud — core CNAPP strength across large multi-cloud estates |
What this looks like in production
The production governance gap
48% of AI agents in production are running unsecured, according to Gravitee’s The State of AI Agent Security 2026, a survey of 750 senior technology leaders published June 2026.
Monitoring alone does not close that gap. Seeing an unauthorized data access after it happened is evidence, not control. The control plane has to sit inline, and the policy has to fire before the action.
Frequently asked questions
Which platform is better for cross-framework agent governance?
Trust3 AI, when agents run on more than one framework or cloud. Enforcement does not depend on a single vendor controlling the agent runtime. Wiz is the stronger fit when the priority is unified cloud-to-code visibility rather than agent-specific runtime enforcement.
Does Trust3 AI support MCP and A2A security?
Yes to both. Every MCP server is treated as untrusted by default, with server verification, credential isolation through short-lived task-scoped tokens, and identity propagation through the MCP call to the data source. For A2A, identity and declared purpose propagate through every hop, scope can only narrow, and the full chain is recorded as one audit record tied to the originating user.
What is declared purpose enforcement?
Access is granted against what an agent was declared to do, not against a role it inherited. A support agent scoped to ticket data cannot reach payroll data even if its credentials would allow it. Grants expire when the task ends.
How does Trust3 AI handle multi-agent workflows?
Agent identity, declared purpose, and live policy state travel with every request across every hop. Policy is evaluated at each hop with the full chain in context.
We already govern access in Snowflake and Databricks. Why add Trust3 AI?
Those platforms secure the payload. By the time an agent chain reaches the platform, the originating user identity is usually gone and a service account is what the data layer sees. Policy cannot fire against a principal that was lost three hops ago. Trust3 AI compiles policy into native enforcement in each platform and carries the real identity to it.
Is either platform self-serve?
Both are enterprise sales. Neither publishes pricing or offers a self-serve trial.
Does Trust3 AI help with regulatory compliance?
Trust3 AI ships pre-built compliance packs covering GDPR, HIPAA, SOX, NIST AI RMF, EU AI Act, CCPA, and PCI-DSS, with one-click evidence generation that updates as standards evolve. The evidence produced is enforcement evidence, not observation logs.
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