TL;DR
- Trust3 AI and Wiz [1], now a Google Cloud company, solve different layers of AI security, and most enterprises running production agents need both. In this article, you’ll find Trust3 AI vs. Wiz: AI Agent Security Compared to help you evaluate their approaches.
- Trust3 AI: a purpose-built control plane that discovers, observes, and enforces runtime access for AI agents, MCP servers, and the data they touch.
- Wiz: a CNAPP connecting code, cloud, and runtime, extended to cover AI models, agents, and AI-specific risk.
- Agents now pull from Snowflake, write to Databricks, and call MCP servers with standing access, a surface traditional cloud tools weren’t built to govern.
- Two categories have emerged: cloud platforms extending into AI (Wiz), and purpose-built agent platforms (Trust3 AI) that find shadow agents, trace every decision, and enforce access the moment an action happens.
What is Trust3 AI?
Trust3 AI is a vendor-neutral security control plane built specifically to secure AI agents, the attack surfaces they interact with, and the data behind them. Rather than treating agents as another cloud workload, Trust3 AI treats agent behavior as its own governance category, built around three core functions: discovery, observability, and runtime authorization.
Trust3 AI connects to any agentic framework, including LangChain, Copilot Studio, Cursor, CrewAI, and Amazon Bedrock. It integrates natively with data platforms such as Snowflake, Databricks, BigQuery, and Iceberg. Its architecture is built around a specific insight: agent risk starts the moment a developer spins up an agent in a tool like Cursor or connects it to an unvetted MCP server, often without visibility for the security team.
To solve the problem, Trust3 AI operates across three connected capabilities:
- Discover: Identifies every agent across an organization’s stack, including shadow AI that was never submitted for approval, and maps identity for ephemeral and delegated agents.
- Observe: Captures per-turn traces of every prompt, tool call, and response, creating an immutable audit trail and detecting drift or injection in real time.
- Secure: Enforces purpose-based access per request, resolves run-as identity, and applies just-in-time grants with auto-expiring scopes, meaning no agent holds standing access it doesn’t currently need.
According to a survey of 437 enterprise security leaders, most enterprises don’t have a complete inventory of their AI agents. enterprises commonly undercount active agents by 3 to 10 times. This is the starting point for Trust3 AI’s approach: nothing can be governed until it’s found.
What is Wiz?
Wiz is a cloud-native application protection platform (CNAPP) that connects code, cloud, and runtime into what the company calls a unified security graph. The goal is to give security teams end-to-end context across the software lifecycle..
Wiz has three core functions: Wiz Code for secure development, Wiz Cloud for preventing cloud and AI risk, and Wiz Defend for runtime protection and threat detection. Layered on top are three AI agents: Wiz Green, which fixes vulnerabilities at the source; Wiz Red, which performs automated penetration testing to discover attack paths; and Wiz Blue, which automates SecOps threat hunting and investigation.
On the AI side specifically, Wiz has extended its graph-based model to cover four areas: visibility (discovering AI models, agents, MCP servers, and services across cloud and SaaS), AI-native risk (identifying issues like sensitive data and endpoint exposure), AI posture (correlating AI risk with infrastructure, identity, and data context), and runtime and response (detecting AI runtime threats like malicious agent actions).
Wiz’s core differentiator is Attack Path Analysis: instead of surfacing every vulnerability, it maps how an attacker could realistically move through an environment, helping teams prioritize the issues that represent a genuine breach path.
Trust3 AI vs. Wiz: Side-by-Side Comparison
| Dimension | Trust3 AI | Wiz |
| Core positioning | Purpose-built control plane for AI agent security and governance | Broad cloud-native application protection platform (CNAPP) |
| Primary objects secured | AI agents, MCP servers, A2A communications, tools, APIs, and the data agents touch | Code, cloud infrastructure, workloads, identities, and increasingly AI models and agents |
| AI focus | AI-native from inception; the entire platform is built around agent behavior | AI capabilities layered onto an existing cloud security graph |
| Data security approach | Native, in-platform enforcement of purpose-based and fine-grained access control at the data source | Visibility and risk correlation for data exposure via integration and graph analysis |
| Runtime enforcement | Enforces natively at data sources; blocks violations at the point of access; runtime guardrails and kill switch at the gateway | Detects and responds to runtime threats using an eBPF sensor and log analysis |
| Agent discovery | Dedicated discovery engine for shadow agents, ephemeral agents, and delegated identity chains | Continuous discovery of AI models, agents, and MCP servers as part of the broader asset graph |
| Audit and governance | Per-turn, immutable audit trails mapped to frameworks like the EU AI Act, HIPAA, and NIST | Compliance mapping across cloud misconfigurations and broader security posture requirements |
| Deployment philosophy | Zero proxy latency for native data sources, zero standing access via just-in-time grants | Agentless architecture across cloud environments, emphasizing fast time-to-visibility |
Where Does Trust3 AI’s Approach Provide the Deepest Coverage?
How does Trust3 AI discover shadow AI agents?
Trust3 AI’s discovery engine identifies agents across Databricks, Azure AI Foundry, Copilot Studio, Microsoft 365, and other environments. The engine also discovers agents built without any formal approval process. Approval-gate models fail at the point of creation. A developer can wire an agent to an unvetted MCP server in an afternoon, with no ticket, no identity record, and no owner. Trust3 AI’s discovery layer maps identity for these ephemeral and delegated agents, so security teams can govern all existing agents, ticketed or not.
What does per-turn observability mean for AI agents?
Rather than logging agent activity at a summary level, Trust3 AI captures a trace of every prompt, tool call, and response an agent generates. This creates a replayable, immutable audit trail. If an agent’s behavior drifts or a prompt injection attempt occurs, the platform detects it in real time and preserves the evidence. This establishes a meaningfully different standard than periodic or sampled logging.
How does runtime authorization differ from role-based access?
Trust3 AI enforces purpose-based access control (PBAC) at the moment an agent attempts an action, rather than relying on static, role-based permissions. Roles say who someone is, but purpose says what a given agent gets right now. This is enforced through just-in-time grants and auto-expiring scopes, which means an agent never holds standing access it isn’t actively using. This capability closes the gap between a role that has broad permissions “just in case” and an agent that should only ever touch what its current task requires.
How does Trust3 AI secure MCP and A2A communication?
Model Context Protocol (MCP) servers and agent-to-agent (A2A) communication represent newer, less mature attack surfaces. Trust3 AI treats each MCP server as its own attack surface, citing real-world incidents such as a flaw in Asana’s MCP implementation that exposed roughly 1,000 enterprises. For A2A security, signed Agent Cards can stop forgery but don’t solve identity propagation across multi-agent chains. Trust3 AI carries user identity, declared purpose, and PBAC verdicts through every hop, even when a task passes through three or more agents.
What compliance evidence does Trust3 AI generate?
Trust3 AI pre-maps evidence packs for frameworks including the EU AI Act (high-risk compliance deadline in December 2027), HIPAA, and NIST.EU AI Act’s high-risk compliance deadline in December 2027. According to Intellyx’s 2026 validation, Trust3 AI generates audit-ready traces continuously, so customers move to production faster and spend significantly less time preparing for audits.
Where Does Wiz’s Approach Provide the Deepest Coverage?
What is Wiz’s code-to-cloud-to-runtime visibility model?
Wiz’s core architectural advantage is connecting code, cloud, and runtime into a single graph, rather than scanning each layer in isolation. This means a vulnerability discovered in production can be traced back to the specific line of code and the team that owns it. A risk identified in code can be understood in terms of its real-world cloud exposure. For organizations already using Wiz for cloud security posture management, this unified graph extends naturally to cover AI workloads as an additional asset type.
How does Wiz’s Attack Path Analysis prioritize risk?
Rather than presenting every misconfiguration or vulnerability as equally urgent, Wiz’s Attack Path Analysis models how an attacker could realistically chain together lateral movement, privilege escalation, and data access. This lets teams focus remediation efforts on a small fraction of findings that represent genuine breach paths.
How does Wiz consolidate CNAPP capabilities?
Wiz combines cloud security posture management (CSPM), cloud infrastructure entitlement management (CIEM), and vulnerability management into a single platform. For organizations managing broad multi-cloud environments, this consolidation reduces the number of point tools needed to maintain baseline cloud hygiene. This is a different problem than agent-specific governance but a necessary foundation underneath it.
How does Wiz integrate with developer and security workflows?
Wiz’s Green, Red, and Blue agents map to distinct workflow stages: fixing vulnerabilities directly in code, automating penetration testing to find exposure, and automating SecOps investigation. Wiz emphasizes that its agentless architecture allows for fast deployment, with some customers reporting meaningful visibility within the first 60 minutes of setup.
The Data Platform Story: Snowflake and Databricks
Enterprise AI agents live and die by the data platforms underneath them. Snowflake and Databricks are two of the most common environments where agent access decisions happen.
On Snowflake, Wiz’s approach centers on integration for security analytics. Wiz connects to Snowflake to extend its cloud security graph with data context, correlating Snowflake-related risk with the rest of an organization’s cloud and identity posture. Trust3 AI takes a different approach: it enforces native runtime access control directly within Snowflake, applying purpose-based access at the moment an agent queries or acts on data. Rather than analyzing Snowflake activity after the fact, Trust3 AI’s enforcement sits at the point of access itself, with zero proxy latency and zero standing access.
On Databricks, Wiz’s Security Graph provides visibility into Databricks environments and correlates that risk with the broader cloud and identity context, helping teams understand exposure across notebooks, clusters, and data assets. Trust3 AI, as a Databricks Built On Partner, instead enforces just-in-time, purpose-based access at the data layer for agents operating within Databricks. This means an agent’s access to a given dataset is scoped to its declared purpose and expires automatically, rather than relying on a standing role.
The distinction reflects the broader pattern across this comparison. Wiz strengthens the security team’s ability to see and correlate risk across a data platform, while Trust3 AI enforces access decisions natively within that platform, at runtime, for every agent action.
Who Should Choose Trust3 AI?
Trust3 AI is the stronger fit for organizations where AI agents are already operating in production, or approaching production, and where agent-specific risks are the primary concern. This includes teams that need to discover shadow agents, enforce purpose-based access, and generate continuous audit evidence ahead of regulatory deadlines.
Choose Trust3 AI if your organization needs runtime enforcement for agent actions specifically, native access control within Snowflake or Databricks, or MCP and A2A security that most general cloud tools don’t yet address in depth.
Who Should Choose Wiz?
Wiz is the stronger fit for organizations that need broad, unified visibility across code, cloud infrastructure, and runtime. Teams already standardized on Wiz for cloud security posture management gain a natural extension point for AI visibility without introducing a new platform for that layer alone.
Choose Wiz if your priority is consolidating CNAPP capabilities across a large multi-cloud estate, prioritizing risk through attack path modeling, or maintaining a single graph that connects application code to cloud exposure across the full development lifecycle.
Building a Complete AI Security Strategy
Trust3 AI and Wiz aren’t solving the same problem. Wiz provides the cloud-wide context and attack path visibility that security teams rely on to understand infrastructure risk broadly. Trust3 AI provides the agent-specific discovery, per-turn observability, and runtime enforcement needed to govern autonomous systems that act, retrieve data, and make decisions on their own.
As AI agents move from pilot to production across more of the enterprise stack, the real question is whether your organization has solved the problem that generic cloud tools can’t address: knowing what every agent is doing, in real time, and authorizing every action before it happens.
Talk to a Trust3 AI architect to see how a dedicated agent security control plane fits alongside your existing cloud security investment.
Frequently Asked Questions
Is Trust3 AI a replacement for Wiz or other CNAPP platforms?
No. Trust3 AI is purpose-built for AI agent governance and complements broader cloud security platforms rather than replacing them. Organizations commonly run Trust3 AI alongside a CNAPP to cover both cloud-wide posture and agent-specific runtime enforcement.
How long does it take to deploy Trust3 AI?
Trust3 AI is designed to integrate with existing agentic frameworks and data platforms, including Snowflake, Databricks, and BigQuery, with native connections that avoid added proxy latency. Our SaaS platform deployment only takes from 30 minutes to 1 hour.
What compliance frameworks does Trust3 AI support?
Trust3 AI pre-maps evidence packs for the EU AI Act, HIPAA, and NIST, with specific attention to the EU AI Act’s high-risk compliance deadline in December 2027. Evidence is generated continuously through per-turn audit trails rather than assembled retroactively.
Does Trust3 AI work with data platforms like Snowflake and Databricks?
Yes. Trust3 AI enforces native runtime access control within Snowflake and applies just-in-time, purpose-based access at the data layer for agents operating in Databricks, where Trust3 AI is a Built On Partner.
What’s the main risk of using only a CNAPP platform for AI agent security?
CNAPP platforms provide valuable visibility and risk correlation across cloud and AI assets, but they are generally not built to enforce purpose-based access at the moment an agent takes action. Without dedicated runtime enforcement, organizations may see agent risk without having a mechanism to authorize or block specific agent actions in real time.
