AI governance and compliance
that holds up in production.
Policies only matter if your systems follow them. We help you set up AI governance, from inventory and risk assessment to policy, controls, and evidence, and wire it into how your models and agents actually run. Move fast without breaking rules, or your customers' trust.
Governance that
runs in the system.
We start from what your AI systems actually do: the data they touch and the actions they can take. Then we build controls that fit that risk.
AI inventory and risk assessment
A register of every model, agent, and AI feature: who owns it, what data it touches, what it can do, and how risky that is.
Policies people can follow
Acceptable-use, data-handling, and model-change policies written for your teams, not copied from a template.
Technical controls
Guardrails, access controls, logging, and human approval steps that enforce policy in the system itself.
Model risk management
Evaluation, sign-off, and monitoring for every model and agent: before launch, after launch, and whenever a model is swapped.
Agentic AI risk management
Extra controls for agents that take actions: scoped permissions, spending and rate limits, approval for high-impact steps, and full audit trails.
Audit-ready evidence
Test results, approvals, and incident records collected as you work, so audits and customer security reviews go faster.
One set of controls,
many frameworks.
We map your controls to the frameworks you answer to, such as the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, alongside the security and privacy standards you already follow, so one piece of evidence can serve several audits.
Assess, design,
implement, operate.
Assess
Inventory your AI systems, rate their risk, and compare what is in place with the frameworks that apply to you.
Design
Policies and controls sized to the risk, so low-risk tools stay easy to use and high-risk systems get real oversight.
Implement
Put the controls into your systems, including guardrails, access, logging, approvals, and evaluation, working with your engineers.
Operate
Monitoring, evidence collection, and regular reviews as your models, vendors, and regulations change.
What you get.
Proof, not paperwork.
- An inventory and risk register for your AI systems
- A gap assessment against the frameworks you choose
- AI policies written for your teams
- Controls implemented in your stack, with documentation
- An evidence and reporting setup for audits and customer reviews
Questions,
answered.
What is AI governance?
AI governance is how an organization decides which AI systems it uses, what they are allowed to do, who is accountable for them, and how it checks they behave as intended. Good governance is enforced in the systems themselves, through access controls, guardrails, logging, and review, not only in policy documents.
What is AI compliance?
AI compliance means meeting the laws, regulations, standards, and contract terms that apply to how you build and use AI, such as privacy law, sector rules, and customer security requirements, and being able to prove it with evidence.
Do we need AI governance if we only use models from OpenAI or Anthropic?
Yes. The provider is responsible for its model, but you are responsible for how you use it: what data you send, which decisions rely on its output, and what your agents are allowed to do.
How does AI governance apply to AI agents?
Agents take actions, so governance has to cover what they are permitted to do, not just what they say. That means scoped permissions, limits, human approval for high-impact actions, and audit trails of every step. We build these into our agents.
What is the NIST AI Risk Management Framework?
The NIST AI RMF is a voluntary framework from the US National Institute of Standards and Technology for managing AI risk. It is organized around four functions: Govern, Map, Measure, and Manage. Many organizations use it as the backbone of their AI governance program.
Can you help us prepare for an audit or a customer security review?
Yes. We map your controls to the relevant framework, close the gaps, and set up evidence collection, so you can answer audits and security questionnaires with records rather than promises. Red-team results are often part of that evidence.
Build it. Break it.
Govern it.
Ready to deploy AI
you can actually trust?
Tell us about your workflow. We'll show you what's possible — and exactly what it takes to keep it secure.