AI Trust & Governance
We find what’s actually exposed before anyone drafts a policy to cover it.
Governance that holds up where it matters.
AI governance helps you understand where your models, agents, and data are actually exposed, design the technical and policy controls that hold up under pressure, and build the operating cadence that catches problems before they become crises.
AI governance framework and guardrails
What we do
Board-level policies for how AI gets used, who owns the data, and who decides what. We build frameworks that integrate with your existing risk and compliance structure instead of running beside it.
A five-dimension diagnostic across data, models, processes, talent, and governance. Productized at fixed scope and timeline, designed to give leadership a defensible baseline within weeks.
Mapping your AI program against the regulations that actually apply. EU AI Act, US state-level rules, sector-specific frameworks, and the ones coming next, translated into what you need to do now.
Technical controls and adversarial testing on the agents and models you’ve actually shipped. We find the failure modes, document the exposures, and build the safeguards before someone external does.
Statistical testing on model outputs across demographic and use-case dimensions. We produce the documentation regulators and plaintiffs will eventually request, with the rigor that holds up under scrutiny.
Retrieval validation, citation enforcement, and the fallback logic that prevents fabricated content from reaching customers. We build the controls that turn confident wrong answers into caught wrong answers.
Production instrumentation that catches a model going bad before it shows up in a complaint. We set up the monitoring, the alerting, and the review cadence that keeps models honest over time.
Playbooks and runbooks for when an AI system fails. We design the containment, communication, and root-cause process, integrated with your existing incident response so it works under real pressure.
The rhythm that keeps governance working. Committee design, model review protocols, board reporting cycles, and the quarterly resilience refresh that keeps the framework current as the technology shifts.
The biggest AI risk in your supply chain is the contracts written before AI mattered. We assess and remediate LLM and embedded-AI exposure across your vendors.
AI governance FAQs
Your questions, answered
The policies, roles, and controls that decide how AI gets used: who owns the data, who approves a model or agent, what gets tested before launch, and how problems are caught and handled after. A good one plugs into your existing risk and compliance structure instead of running beside it.
The technical controls that keep AI systems inside safe limits in production. That means input and output filters, accuracy and hallucination checks, limits on what an agent can do, human approval on high-stakes steps, and monitoring for drift. Policies say what should happen; guardrails make it happen.
It depends on your industry and where you operate. Common reference points are the NIST AI Risk Management Framework, the EU AI Act, and sector rules such as model risk expectations for banks and HIPAA for healthcare. We map your AI program against the ones that apply and close the gaps.
AI Trust & Governance is part of Cabin’s Strategy & AI Advisory practice. Governance is designed alongside the AI strategy and roadmap, so controls are built in from the first use case instead of added after launch.
