Practice · AI Governance & Trust

Deploy agents faster, with the right data

We help enterprises stand up a Unified Trust Layer for AI agents — purpose-based access control, just-in-time grants, agent discovery and audit-ready evidence — so you can ship agentic AI without losing control on Databricks, Snowflake and multi-cloud estates.

Diagram of a Unified Trust Layer where an agent reaches governed data through a gateway with PBAC policy and just-in-time grantsAI agentGatewayPBAC policyJIT grantGoverned dataDeny + log
Zero standing access — agents get just-in-time, purpose-bound grants, then they expire and everything is logged.

Yerbabuena Digital

Yerbabuena Digital brings hands-on agentic AI governance experience to your AI program. We implement PBAC, zero-standing access with just-in-time grants, LLM guardrails and gateway integrations, and run agent discovery and Trustscore evaluation — aligned to EU AI Act and GDPR programs for finance, healthcare and government.

Who this practice is for

Organizations moving from AI pilots to production who need governance that auditors accept and engineers can live with.

CISO & security teams

Pain: Agents are reaching sensitive data with broad standing access and no audit trail.

Fit: Zero-standing, just-in-time grants and real-time policy enforcement on cloud data platforms.

Data & AI leaders

Pain: AI governance is slides and spreadsheets; nothing enforces policy at the data plane.

Fit: PBAC and a Unified Trust Layer enforced on Databricks, Snowflake and multi-cloud estates.

Risk, compliance & legal

Pain: EU AI Act and GDPR obligations with no evidence trail for agents and models.

Fit: Agent discovery, Trustscore evaluation and audit-ready evidence for regulated industries.

Governance at the data plane, not on paper

Most AI governance programs stop at policy documents. We implement enforcement where agents actually reach data — purpose-based access control (PBAC), zero standing access, and just-in-time grants that expire — so policy is real, not aspirational.

We build a Unified Trust Layer across your AI stack: gateways (Portkey, LiteLLM, Kong), MCP-aware policy, and enforcement on Databricks, Snowflake and multi-cloud estates with Apache Ranger / Privacera lineage.

Agent discovery and observability close the loop: we inventory every agent, what it can reach, and how it performs — with Trustscore-style evaluation and tracing across heterogeneous frameworks.

The output is audit-ready evidence — who/what accessed what, for what purpose, when, and with what approval — mapped to EU AI Act and GDPR obligations. That is what boards, regulators and your CISO will ask for.

Return on investment

Where the numbers come from

Illustrative scenarios based on typical governance engagements — results vary by estate, tooling and scope. We scope every engagement together before you commit.

Diagram of a Unified Trust Layer where an agent reaches governed data through a gateway with PBAC policy and just-in-time grantsAI agentGatewayPBAC policyJIT grantGoverned dataDeny + log
Zero standing access — agents get just-in-time, purpose-bound grants, then they expire and everything is logged.

Bank — governed agent rollout

A financial services group wanted to let internal agents query customer data without giving standing access to sensitive tables.

Standing access
Zero — Just-in-time, purpose-bound grants
Time to provision access
Minutes — Was days of tickets
Audit evidence
100% — Per-purpose, per-agent logs

PBAC + JIT lets agents move fast on sensitive data while auditors get exactly the evidence they need.

Healthcare — EU AI Act readiness

A provider deploying clinical-support agents needed evidence of purpose, data scope and human oversight for EU AI Act and GDPR.

Agents discovered
Full inventory — Tools, data, models mapped
Policy coverage
Enforced — At the data plane, not paper
Audit prep effort
−70% — Evidence exports on demand

Governance built at the data plane turns AI Act readiness from a project into a byproduct.

Tell us what's blocking progress

Capabilities

What we implement

A coherent trust layer — from policy to enforcement to evidence — across your AI and data estate.

Unified Trust Layer

One control plane for agents, models and data access.

  • PBAC (purpose-based access control) design and rollout
  • Zero standing access with just-in-time grants
  • Real-time policy enforcement on cloud data platforms
  • Apache Ranger / Privacera lineage and integration

Agent discovery & observability

Know every agent and what it can do.

  • Agent, tool and data-access inventory
  • Trustscore evaluation and tracing
  • Coverage across LangChain, CrewAI, Bedrock, Copilot, custom
  • Drift, failure and anomaly detection

Gateway & MCP-aware policy

Enforce policy where agents call models and tools.

  • Gateway integrations: Portkey, LiteLLM, Kong
  • MCP-aware policy and A2A delegation security
  • LLM guardrails and content policies
  • Prompt and tool-call logging

Compliance evidence

Audit-ready proof for regulators and boards.

  • EU AI Act readiness mapping and evidence
  • GDPR data-purpose alignment and RoPA ties
  • Per-purpose, per-agent access logs
  • Evidence exports and review workflows

Use cases

Where this lands hardest

Financial services

Governed agent access to customer data

Challenge: Agents needed customer data for workflows; standing access was a regulator red flag.

Outcome: PBAC + JIT grants with per-purpose logs; agents move fast, auditors get evidence.

Healthcare

EU AI Act readiness for clinical agents

Challenge: Clinical-support agents with no purpose, scope or oversight evidence.

Outcome: Agent discovery, Trustscore and evidence exports mapped to EU AI Act and GDPR.

Public sector

Audit-ready agentic e-Administration

Challenge: New AI functions with no enforceable policy or access trail.

Outcome: Unified Trust Layer with logging and exports aligned to e-Admin expectations.

Engagement

How a governance engagement runs

From discovery to enforced policy and audit-ready evidence — in clear phases.

Discover

We inventory agents, models, tools and data; map regulations and risk. You get a governance gap map and quick wins — not a generic framework.

Architect

We design the Unified Trust Layer: PBAC model, JIT grants, gateway and MCP policy, and the evidence pipeline — co-authored with security, data and legal.

Implement

We enforce policy at the data plane on Databricks, Snowflake and multi-cloud; wire gateways; stand up agent discovery and Trustscore evaluation.

Govern & evidence

We hand over runbooks and evidence exports — or run a governed retainer with monitoring, policy reviews and audit support.

Before & after

What changes with a trust layer

Before

  • Policy on paper; broad standing access in practice
  • No inventory of agents, tools or data reach
  • Gateways and MCP with no enforceable policy
  • AI Act and GDPR evidence assembled by hand each audit

After

  • PBAC with zero standing access and JIT grants
  • Agent discovery and Trustscore across every framework
  • MCP-aware policy enforced at gateways and the data plane
  • Audit-ready evidence exports on demand
Diagram of a Unified Trust Layer where an agent reaches governed data through a gateway with PBAC policy and just-in-time grantsAI agentGatewayPBAC policyJIT grantGoverned dataDeny + log
Policy enforced where agents reach data — purpose-bound, just-in-time, logged.

Outcomes

What clients typically see

Ship agents faster

Clear guardrails and JIT access let teams move on real data without waiting for standing-access tickets.

Zero standing access

Purpose-bound, time-boxed grants replace permanent broad access — least privilege by construction.

Audit-ready by design

Per-purpose, per-agent evidence exports make EU AI Act and GDPR reviews a byproduct, not a fire drill.

One control plane

A Unified Trust Layer across models, gateways and data platforms — instead of scattered point controls.

Frequently asked questions

Frequently asked questions

What is PBAC and how is it different from RBAC?

Purpose-based access control grants access for a specific purpose and time, not a permanent role. Agents get just-in-time, purpose-bound access that expires — far safer than standing role-based access for non-human callers.

Which data platforms do you enforce on?

Databricks, Snowflake and multi-cloud estates, with Apache Ranger / Privacera lineage. We meet your data where it lives rather than forcing a rip-and-replace.

Can you help with EU AI Act readiness?

Yes. We map obligations to your agents, models and data, then produce the evidence regulators expect — purpose, scope, human oversight and access trails.

Do you integrate with our LLM gateway?

We work with Portkey, LiteLLM, Kong and similar gateways, plus MCP-aware policy and A2A delegation security, so enforcement covers model and tool calls too.

What is agent discovery?

An inventory of every agent, the tools it can call and the data it can reach, plus tracing and Trustscore-style evaluation across frameworks — so nothing is shadow.

Is this only for large enterprises?

No. Mid-size companies deploying agents benefit most from getting governance right early — it becomes an enabler, not a tax. We right-size it to your estate.

Yerbabuena Digital

Still navigating between pilot and production?

If you're working through questions around access, architecture, compliance, cost control, or moving a working system into a governed production environment, we can help clarify the next practical step. We respond within one business day with a direct assessment and, where it makes sense, an initial conversation.