Data-layer enforcement,
or boundary inspection.
Both platforms address AI agent security. They enforce at different layers, and that decides which one fits your mandate. Trust3 AI enforces inside the data platform, evaluated at the point of access with no proxy hop. Singulr AI enforces at the AI-interaction boundary, inspecting prompts, uploads, and responses on the way to AI tools.
Which platform fits your stack
Singulr AI
- Your mandate is governing how employees use AI on endpoints and in browsers
- You need CI/CD red-teaming mapped to OWASP LLM Top 10, NIST, and MITRE ATLAS
Trust3 AI
- Your mandate is governing production agents against regulated data
- Agents reach Snowflake, Databricks, BigQuery, or Iceberg and identity must survive the hop
- You need to see every agent, tool, and data source a compromised agent could reach
Where Trust3 AI goes further than Singulr AI
Singulr AI is strong on things Trust3 AI does not do by design — continuous red-teaming in CI/CD, endpoint and browser coverage, and broad discovery across homegrown apps and SaaS copilots. Here is where Trust3 AI goes further.
Data-layer enforcement, not boundary inspection
Trust3 AI enforces natively inside the data platform: row, column, and tag access control across Snowflake, Databricks, BigQuery, Iceberg, and 50+ native data sources. Boundary inspection can catch oversharing in a prompt. Only data-layer authorization can stop an agent from reaching data it was never authorized to reach in the first place.
Identity survives the agent-to-agent handoff
Trust3 AI carries authorization context through every agent-to-agent hop. Identity, declared purpose, and live policy state travel with the request. Scope can only narrow through the chain, never expand, and the whole chain is recorded as one correlated audit record tied to the person who started it.
Blast radius mapped before an incident, not after
See what one compromised agent can reach — every other agent, tool, and data source — mapped in advance, not reconstructed after the fact.
A live, explainable agent trust score
A score from 0 to 10, continuously recalculated and explainable across four weighted dimensions, rather than a static risk label.
Native MCP and A2A protocol security
MCP and agent-to-agent security are native to the Unified Trust Layer, not a bolt-on inspection layer.
One-click compliance evidence, not just records
Evidence packs across GDPR, HIPAA, SOX, NIST AI RMF, EU AI Act, CCPA, and PCI-DSS, generated in one click and kept current as standards evolve.
Where the two platforms run close
What both do
- Agent discovery
- Risk scoring
- Compliance evidence production
- Integration with an existing security stack
If those four capabilities are the whole requirement, either platform will serve. The real differences show up in where enforcement happens and what each platform was scoped to do.
How the two platforms compare
| Dimension | Trust3 AI | Singulr AI |
|---|---|---|
| Data-layer enforcement | Enforces natively inside the data platform: row, column, and tag access control across Snowflake, Databricks, BigQuery, Iceberg, and 50+ native data sources | Not a documented primary capability |
| Enforcement point | At the point of data access, with no proxy hop | At the AI-interaction boundary, inspecting prompts, uploads, and responses |
| Identity through delegation | Carries authorization context through every agent-to-agent hop | Not a documented primary capability |
| Cross-asset blast radius | Identity-to-data reachability across agents, MCP servers, tools, and sources | Not a documented primary capability |
| MCP and A2A protocol security | Native to the Unified Trust Layer | Not a documented primary capability |
| Agent trust scoring | A live score from 0 to 10, continuously recalculated and explainable across four weighted dimensions | Singulr Pulse risk scoring, prioritized by severity |
| Red-teaming in CI/CD | Not a primary capability | Continuous, mapped to OWASP LLM Top 10, NIST, and MITRE ATLAS |
| Endpoint and browser coverage | Out of scope by design | Available, spanning desktop AI apps and browser extensions |
| Compliance evidence | One-click evidence packs across GDPR, HIPAA, SOX, NIST AI RMF, EU AI Act, CCPA, and PCI-DSS | Tamper-evident records, SIEM and EDR feeds, board-level reporting |
What this looks like in production
The bottom line
Four questions to ask before you choose:
1. When your agents reach Snowflake or Databricks, whose identity is on the query — the person who started the chain, or a service account?
2. Can you scope an agent’s access to a single declared purpose and have it expire when the task ends, or does it hold standing credentials?
3. When one agent is compromised, can you see every other agent, tool, and data source it can reach?
4. Is your primary mandate governing production agents against regulated data, or governing how employees use AI on their endpoints?
Answers one through three point to data-layer enforcement. Answer four is the one that decides it. If your mandate is governing production agents against regulated data, enforcement has to happen where the data lives. Trust3 AI is built by the creators of Apache Ranger, on the production policy-enforcement lineage that came out of it.
Frequently asked questions
Which platform is better for cross-framework agent governance?
Trust3 AI, when your mandate is governing production agents against regulated data and identity has to survive multi-hop delegation. Singulr AI is the stronger fit when the mandate is governing how employees use AI on endpoints and in browsers, with CI/CD red-teaming built in.
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.
See it against your stack.
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