Proven with Industry Leaders
What our AI governance consulting covers
Most AI pilots that stall in review stall for the same reason. The use case works, and nobody can show how it is controlled. We close that gap in four parts.
| What the risk team needs | What we deliver | The evidence it produces |
|---|---|---|
| A complete view of AI in use | An AI inventory with risk tiering | Inventory entries, owners, and tiers |
| Confidence the system behaves as intended | Validation of GenAI and agent use cases | Validation reports and findings |
| Evidence mapped to the framework you answer to | An evidence pack mapped to NIST AI RMF, ISO/IEC 42001, or sector guidance | A pack your risk committee and auditors can review |
| Control of what agents do in production | Agent identity, decision logs, and a stop control | System records, not documents |
The AI inventory reviewers ask for
Every framework starts in the same place: knowing what AI you run, who owns it, and how much risk it carries. The inventory is where tiering, validation depth, and monitoring frequency get decided.
Most organizations have a list of approved tools. Few have one that captures generative AI features inside vendor products, internal copilots, and agents that call tools.
We build or extend the inventory to cover
- Every AI use case, including AI embedded in third-party software
- The owner, purpose, data sources, and systems each one touches
- A risk tier that drives validation depth and monitoring frequency
- Agents and the tools, APIs, and data each agent is allowed to reach
An evidence pack mapped to your framework
The evidence pack is what your risk committee reviews and what an auditor or regulator asks to see. For each in-scope AI use case it contains eight parts, mapped to the framework you answer to.
- 01 The inventory entry and risk tier, with the rationale
- 02 The use case, its limits, and who is accountable for outcomes
- 03 Data sources, lineage, and access controls
- 04 Validation scope, results, and open findings
- 05 Ongoing monitoring: what is measured, how often, and the thresholds
- 06 Third-party and vendor AI responsibilities. Reliance on a vendor does not transfer responsibility.
- 07 Agent controls: identities, permitted tools, decision logs, and the stop procedure
- 08 Change history and approvals
Frameworks we map evidence to
One evidence pack, mapped to whichever of these applies. Sector rules sit on top and are covered on the industry pages below. For the most common starting point, read our NIST AI RMF guide.
NIST AI RMF
The US voluntary framework, organized around four functions: Govern, Map, Measure, and Manage. Its Generative AI Profile (NIST AI 600-1) extends it to generative AI.
ISO/IEC 42001
The international standard for an AI management system. We build the evidence and prepare you for the certification audit. Certification itself comes from an accredited body.
EU AI Act
For organizations that place AI systems on the EU market or use them there. What applies depends on the risk class of each system.
OWASP Top 10 for LLM and Agentic Applications
The security risk lists we use to scope testing of generative AI and agents, from prompt injection to tool misuse.
Agent controls: identity, decision logs and a stop control
Governance is decided in committee and enforced at runtime. For agents, four questions decide whether a control is real. Each answer has to come from a system record.
Which agent took the action?
An identity registered for that agent, not a shared service account.
On whose authority?
A delegation record that carries both the agent and the person it acted for.
What did it touch, and under which policy?
A gateway access log tied to the policy decision that allowed the call.
Can you stop it?
A revocation path that works in seconds, tested before go-live.
We implement the controls that produce those records in the gateway and identity layer. See AI gateway and agent governance for how that works, and AI and agent governance on WSO2 if WSO2 is your platform. Our practitioner writing on the topic is collected under Agentic AI Governance & Model Risk.
AI governance in regulated industries
The method is the same everywhere: inventory, validation, evidence, and runtime controls. What changes is the rulebook the evidence is mapped to and who reviews it.
Model risk management, SR 26-2, independent validation, and the evidence pack a model risk committee and examiner expect.
AI model risk management for banks HealthcarePHI stays inside the perimeter, every agent has its own identity, and every record access leaves a trail your compliance team can sign off.
Healthcare AIAI audit services and independent review
Internal audit increasingly asks whether the AI governance program itself is sound, not only whether individual models are.
We review the program against the framework you have adopted and your own policies: inventory completeness, tiering, validation coverage, monitoring, and agent controls. The output is a findings report with a remediation plan.
See the data behind one AI use case
Reviewers ask what data feeds an AI system, where it came from, and what happens to it on the way. Our team maps it for one use case with Cloudera Data Lineage, from source systems through pipelines to the model, and produces the data sources and lineage section of the evidence pack from system metadata.
Your lawyers write the policy. The proof that it holds comes from systems. We build that proof.
- Price: free for one closely defined use case. Up to $8K for a wider scope.
- Timing: about seven working days after access is set up. Three to run the tool, four to write the report.
- Your data stays put: only metadata leaves your environment.
AI governance, answered plainly.
AI governance
What is AI governance consulting?
AI governance consulting sets up how an organization knows what AI it runs, tests that it behaves as intended, proves it to reviewers, and controls what it can do in production. The output is an AI inventory, validation results, an evidence pack, and runtime controls on agents.
Which AI governance frameworks do you map to?
NIST AI RMF and its Generative AI Profile, ISO/IEC 42001, and the EU AI Act where it applies, with the OWASP Top 10 lists for LLM and agentic applications as the basis for testing. Sector rules sit on top: SR 26-2 in banking and HIPAA in healthcare.
Do you certify organizations against ISO/IEC 42001?
No. Certification comes from an accredited certification body. We build the management system evidence and prepare you for the audit.
What does an AI data lineage map cost, and how long does it take?
For one closely defined AI use case it is free. A wider scope runs up to $8K. Our team runs Cloudera Data Lineage against the systems that feed the use case, and the report arrives about seven working days after access is set up. Only metadata leaves your environment.
Inventory, agents and vendors
What is an AI inventory?
A register of every AI use case in the organization, including AI inside vendor products, with its owner, purpose, data, systems touched, and risk tier. The tier decides how much validation and monitoring each use case gets.
How do you govern AI agents?
At runtime, not only in policy. Each agent gets its own identity, every tool call passes through a gateway that applies policy and logs the decision, and a tested stop control can revoke the agent in seconds.
Who is responsible for AI inside vendor products?
You are. Relying on a vendor does not transfer responsibility. We review vendor AI as part of the inventory and validation work: what the vendor discloses, what you can test, and which controls you apply around the vendor system.
Does AI governance slow down delivery?
It slows delivery when it arrives at the end. Built in from the first design decision, it is what lets a pilot clear review and reach production instead of stalling there.
Running AI at a bank? Model validation, the SR 26-2 evidence pack and examiner review are covered under AI model risk management for banks.