The questions
we get asked the most.
Direct answers, no hedging, no marketing fluff. If you have a question that isn't here, your advisor will answer it on the call.
Trust3 AI is the agent control plane for the enterprise. We discover every AI agent in your stack, observe every decision they make, and secure every action they take, across any framework, any cloud. Governance that runs at the speed of your agents, not the speed of your last compliance review.
Three things are happening in parallel. First, every enterprise is shipping agents (autonomous systems that touch data, make decisions, and act on behalf of users). Second, regulators are catching up fast: the EU AI Act, NIST AI RMF, and a dozen state-level laws have moved from drafts to deadlines. Third, traditional governance tools, built for humans signing into apps, were never designed for this. Trust3 AI closes the gap before it widens further.
- Unified data & AI governance: one platform, one trust layer.
- Trust Agents: autonomous, always-on governance teammates.
- Purpose-Based Access Control (PBAC): access tied to intent, not just role.
- End-to-end observability: real-time lineage of data, decisions, and outcomes.
- Vendor-agnostic by design: Snowflake, Databricks, Iceberg, and 50+ more.
Trust3 AI is built for the people responsible for moving AI from idea to production responsibly:
- Heads of Data Governance / Office of the CDO: balance democratization with control, ensure auditability of AI decisions, accelerate without compromising compliance.
- VPs of AI / AI Platform / CTO: ship production-grade agents fast, eliminate silos, embed governance from day one.
- Security & Compliance leaders: explain AI behavior, enforce consistent policies, prove trustworthiness to regulators.
- Data & AI platform teams: run secure, scalable architectures across hybrid and multi-cloud environments with full observability.
The AI Governance Agent is an always-on teammate that automatically discovers AI agents across platforms and frameworks (Snowflake, SAP Joule, Agent Bricks, custom builds, and more) and builds a centralized inventory.
For each discovered agent it captures the fingerprint: name, purpose, underlying LLMs, connected tools, prompts, and the data sources the agent touches. It connects with the Data Governance Agent to understand classification and sensitivity, then runs continuous risk assessment to flag agents accessing sensitive data, using unapproved models, or drifting outside policy.
Once assessed, it enforces guardrails (fine-grained access control, restrictions on what data can flow into prompts or out to external LLMs) and keeps an end-to-end audit trail across the full agent lifecycle.
The Data Governance Agent runs continuously across your data estate and handles four loops automatically:
- Discovery: scans clouds and platforms to identify datasets, tables, files, APIs, and streams.
- Classification: detects sensitive data (PII, financial, health, confidential) and applies tags consistently.
- Policy enforcement: translates governance rules into enforceable access controls, masking, and restrictions.
- Audit: monitors who accessed what and when, surfaces anomalies, and produces compliance-ready trails.
It works across AWS, Azure, GCP, Snowflake, Databricks, BigQuery, S3, and AI workloads on Anthropic, Gemini, and OpenAI.
Trust3 AI is the first platform that unifies data and AI governance into a single trust layer, with four key differentiators:
- Streamlined governance: one platform across your entire multi-cloud data + AI ecosystem.
- Proactive AI visibility: every agent is discovered and risk-assessed across Agent Bricks, ServiceNow, Snowflake, Salesforce Agentforce, and beyond.
- Proactive AI security: Trust Agents establish accountability and fine-grained access for both data and AI.
- 10× faster delivery: embedded guardrails turn governance from a blocker into an accelerator.
- Real-time monitoring: end-to-end observability gives instant visibility into every agent interaction.
Trust Agents are autonomous governance teammates that run continuously across your data and AI stack. They handle agent discovery, natural-language policy creation, real-time guardrails, and fine-grained controls, automatically. The result: confident, compliant agent deployments without dragging your security team through manual reviews every release.
The Trust Agents (both Data and AI Governance) operate as always-on teammates across four functions:
- Data discovery and classification with consistent tags
- Policy enforcement and access control across platforms
- Auditing of data access and AI prompts
- Reporting and compliance workflows
Trust3 AI is vendor-agnostic by design. It natively integrates with 50+ data sources and platforms (including Snowflake, Databricks, Apache Iceberg, S3, BigQuery, AWS, Azure, GCP) and modern agentic stacks: Agent Bricks, Salesforce Agentforce, ServiceNow, SAP Joule, Google's Agent Development Kit, and custom builds in LangChain, LlamaIndex, and beyond.
MCP made tool integration easy and made every connector a potential breach. Real incidents in 2025-26 alone: Asana's tenant-isolation flaw exposed up to 1,000 enterprises; WordPress MCP plugins put 100,000+ sites at risk; researchers demonstrated full Azure tenant takeover via a single poisoned MCP server. Trust3 AI covers the protocol layer end to end:
- Discovery: continuous scanning of every MCP server in your environment, including the rogue ones, with risk scoring against policy.
- Content firewall: inline inspection of every MCP response to detect prompt injection, hidden directives, and resource content poisoning before they reach the model.
- Scope minimization: purpose-bound scope reduction at the gateway and short-lived tokens via RFC 8693 token exchange. No more
files:*oradmin:*tokens. - Tool allowlists + audit: per-purpose, per-agent allowlists with break-glass approvals and tamper-evident audit logs mapped to NIST AI RMF and EU AI Act controls.
A2A v1.0 added Signed Agent Cards in early 2026, which solved forgery. It didn't solve identity propagation across delegation chains, sensitive payload leakage between agents, or evidence-grade audit when an action three agents deep goes sideways. Trust3 AI covers all three. We carry user identity, declared purpose, and PBAC verdicts through every hop in an A2A chain. We redact PII, PCI, and PHI before it leaves the originating agent and re-bind only at the authorized recipient. And we surface MCP, A2A, and direct LLM calls in one continuous trace, so you can see the full path from human prompt to source row, regardless of how many agents touched it on the way.
No. Your AI gateway is in the right place doing the right job: routing across providers, failover, caching, cost attribution. We sit behind it. Gateways stop at the wire: they see prompts and responses, and that's it. Trust3 AI enriches every gateway log with the things gateways can't see: agent identity, declared purpose, end-to-end data lineage from source row to user, and the PBAC verdict that authorized the call in the first place. Native plugins ship for Portkey, LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, Bifrost, and Apigee. Enriched traces forward to your existing OTel pipeline (Datadog, Splunk, Snowflake) with no rework.
Without proper governance, agentic systems create real, measurable risk:
- Inconsistent policy enforcement across teams and platforms
- Performance bottlenecks from manual review processes
- Ethical violations and compliance failures
- Audit gaps that block production deployment
Governance is the difference between AI projects that ship and AI projects that get stuck in pilot.
Pilot purgatory is the stuck state most AI initiatives fall into: working prototypes that never become production systems because security, compliance, and access concerns can't be resolved at scale. Trust3 AI dissolves the blockers (secure data foundations, automated tracking, flexible governance) so you can operationalize AI in production, not just demo it.
- Infrastructure complexity: legacy governance tooling can't keep up with modern stacks like Snowflake and Iceberg.
- Fragmented ownership: data and AI teams operate in silos, leading to inconsistent policies.
- Consumption crisis: agents consume data at a scale manual oversight simply can't match.
- Operational stagnation: governance is treated as a blocker, delaying every initiative.
- AI governance gap: without dedicated AI controls, organizations face bias, ethical, and compliance risk.
- Compliance violations resulting in fines and legal exposure
- Erosion of trust with customers, regulators, and investors
- Reputational damage from breaches or unexpected AI behavior
- Stalled AI adoption, with opportunity cost compounded quarter over quarter
- Ethical risk from unchecked agent behavior at scale
Trust3 AI proactively aligns with the regulations that matter most to enterprise AI: GDPR, HIPAA, the EU AI Act, and the NIST AI Risk Management Framework. The Trust Agents handle discovery, natural-language-to-policy creation, real-time guardrails, and policy enforcement automatically, turning compliance from a quarterly project into a continuous property of the platform.
Enterprise-grade security is built in:
- Unified governance across data and AI as a single trust layer
- Proactive detection of bias and ethical violations
- Full visibility and control across multi-cloud deployments
- Fine-grained, context-aware access control via PBAC
- Audit-ready trails for every agent action
Yes, and it was built for it. Trust3 AI's observability layer captures the agent inventory, risk-assessment, and decision provenance the EU AI Act expects, and the Trustscore offering gives compliance and security teams an enforceable view of every agent's posture ahead of regulatory deadlines.
Trust3 AI is priced based on your environment, integrations, and scale. Most engagements start with a tailored advisor session so we can size the deployment correctly. Reach out at [email protected] or request a walkthrough and we'll come back with a fit-for-purpose proposal.
Yes, we offer guided proofs-of-concept for qualifying enterprise teams. The standard POC scopes one or two agents in your environment, end-to-end discovery and observability, and a representative PBAC policy. Most POCs run two to four weeks. Talk to an advisor to scope yours.