AI Governance
Put controls around AI before it scales beyond your ability to manage it. We build the AI inventory, risk ranking, approval workflows, permissions, and audit logs, mapped to NIST AI RMF and ISO/IEC 42001, for enterprises that need what they've decided AI should and shouldn't do to actually be enforced. So an audit starts with a report instead of a scramble.
Writing your organization's AI policy stays with legal and compliance. We build the system that enforces whatever policy you've already decided on.
Enforced Policy, Not a Policy PDF
A policy written in English and stored in a wiki is advice, not governance. We build the inventory, risk scoring, approval workflows, and audit logs that make what you've decided AI should and shouldn't do something the system actually enforces.
What is AI governance?
AI governance is the set of controls that makes your AI policy real in the running system, not just on paper: an AI inventory, risk ranking, approval workflows, permissions, and audit logs. It covers who owns each AI system, what it's allowed to do, and how you prove it. You may also see it called AI risk management or AI controls.
Tools We Use
Control that's enforced, not just documented: access rules, credentials, who-owns-what, and audit trails built into the systems themselves, not left in a policy PDF.
Recognize the symptoms
When You Need AI Governance
If two or more of these are already true, this isn't a tuning problem.
- Every team spins up its own models and agents, and nobody tracks the total
- Agents ship with tool permissions nobody reviewed
- Nobody can produce a current list of what AI is running in production
- It's unclear who's responsible when a model or agent does something wrong
- There's no audit log showing what data a decision used, or who approved it
Built toward the frameworks that matter.
AI governance consulting that produces evidence, not just a mapped policy document.
NIST AI RMF
A risk management approach built around NIST's Govern, Map, Measure, and Manage functions. It's voluntary in the US, but regulators, insurers, and enterprise customers increasingly expect you to show it, not just claim it.
The system that turns policy into enforcement, not documentation.
- AI Governance covers the technical systems that enforce policy, manage risk, and keep AI systems in production accountable. Policy alone isn't governance. A policy written in English and stored in a wiki is just advice.
- Governance means building the actual system: registries, approval steps, live enforcement, audit logs, and visibility into every decision. Those are the things that make a policy real and checkable, not just written down.
- We go past writing a policy document to building the system that registers every AI system, scores its risk, requires human approval for high-risk actions, and keeps a tamper-proof record of everything.
- This is different from compliance consulting, which defines the policy, and from AI Operations, which runs the infrastructure. This is the system that enforces what you've already decided to require.
What We Build
Eight parts that work together, organized around what actually makes a rule enforceable, not which vendor's dashboard the policy lives in.
AI inventory
One place that tracks every AI system, agent, and model in use: who owns it, what it's for, how risky it is, and whether it's approved. Not a spreadsheet someone updates when they remember to.
Agent registry
A record of every agent in production: its version, where it came from, what tools it can use, and whether it's approved. So "which agents can touch customer data?" has a real answer.
Risk ranking
A score for how much damage a system could cause, how much it acts on its own, how explainable it is, and how sensitive its data is, so high-risk systems get tighter controls and low-risk ones aren't slowed down for no reason.
Permissions
Access kept to the minimum by default: which tools, which data, and which actions an agent is allowed to touch, and what still needs approval first.
Approval workflows
Sign-off rules that scale with risk level, with who approved it, when, and why all captured, not a Slack thread nobody can find later.
Audit trail
Tamper-proof logs of every decision and action, so anything a system does can be traced back to the data and approval behind it.
Change process
One consistent process for changing any AI system (propose, test, roll out, check, and roll back if needed), tracked the same way every time, not differently by team.
Rule enforcement
Live checks that block or flag actions that break the rules, so the rule is enforced in the moment instead of just sitting in a document.
Writing organizational AI policy or compliance frameworks stays with your legal and compliance function. Building the agents and models this governs is part of AI Agent Development and AI Development. Defending against attacks and protecting data is part of AI Security.
What Good AI Governance Needs
Eight parts that work together, not a policy document and a spreadsheet.
AI inventory
A list of which AI systems exist, what they do, and who owns them.
Risk ranking
A way to rank systems by risk: how much damage they could cause, how much they act on their own, and how sensitive the data they touch is.
Approval steps
A sign-off required before a system or update reaches production, scaled to how risky the change is.
Registry and versions
A registry that tracks every model or agent's version, where it came from, whether it's approved, and which version of the rules it follows.
Permissions
Permission controls that define which agents can use which tools and data.
Rule enforcement
Live checks that block actions that break policy, instead of just writing the policy down.
Audit log
Tamper-proof logs of every decision, action, and approval, so any outcome can be traced back to what caused it.
Incident response
Detection, pause, and investigation when a system misbehaves, tracked against a response-time target.
Governance Systems We Build
Organized around what the system is used for, not the framework underneath.
Company-wide AI inventory and risk register
A live, current record of every AI system in use, scored by risk, replacing the spreadsheet nobody updates.
Approval workflows for agents
Sign-off rules for agents that scale with risk level, with who approved what, when, and why captured automatically.
Evidence for NIST AI RMF
The inventory, risk ranking, and control evidence mapped to NIST's Govern, Map, Measure, and Manage functions.
Controls for ISO/IEC 42001
Policy, risk assessment, lifecycle controls, and audit evidence organized the way a certification body's audit actually checks for them.
Live rule enforcement
Rules enforced in the system itself, so a violation gets blocked in the moment instead of caught in a quarterly review.
How Enforcement Is Built In
Built into the system, not just written into a policy.
Policy as code
Rules written and enforced in code rather than a document, so a violation is blocked automatically instead of relying on someone reading the policy.
Approval that scales with risk
Sign-off requirements that scale with a system's risk score, so low-risk systems move fast and high-risk ones get real scrutiny.
Registry with signed records
Every model, agent, and prompt registered with a signed record of what was tested and approved before it went live.
Checks in the request path
Checks that sit in the request path itself and can block or flag an action in real time, not just log it after the fact.
What You Receive
AI Governance vs Writing an AI Policy
A policy document says what should happen. AI Governance is the system that makes it happen: a live inventory, a way to score risk, approval steps, and checks that enforce the policy instead of just describing it. We build that system. Writing the policy and interpreting the law stays with your legal and compliance team.
AI Governance vs AI Security
AI Security tests whether the boundaries hold, by trying to break in and seeing if an attacker can trick an agent into bypassing a permission check. AI Governance builds those boundaries in the first place: the inventory, the risk ranking, the approval steps, and the permission rules that get tested.
From our experts.
Writing on agent risk, safe rollout, and evaluation credibility.
Our AI Governance Process
The same nine-phase process we use everywhere, with a checkpoint at every step. For this kind of build, testing checks whether approvals actually work, whether permissions are actually enforced, and whether the audit log is complete, well past clicking through one dashboard.
Related Engineering Services
Common questions.
Bring us the AI inventory nobody can produce on request.
Whether you need a risk score and approval process proven on one high-risk system, or a full inventory, permission setup, and audit trail across the company, we'll look at where your AI systems stand before recommending anything.
No hype · No forced roadmap · Just a clear view of what the system needs next