AI Governance
Most AI platforms treat governance as a documentation deliverable. We treat it as part of the runtime. Model risk monitoring, explainability of every output, human oversight as a configurable control, decision audit on every action - all built into how the platform operates, not bolted on for compliance review.
Approach
Data governance asks “where is this data, who has access, how is it protected.” AI governance asks a different set of questions: how does this model behave, who is accountable for its decisions, what happens when it gets something wrong, how do we know it hasn’t drifted, can we explain a specific decision to a regulator or a customer six months from now. We built the platform around those questions.
A risk policy document. A model inventory spreadsheet. An annual review process. Mostly: documentation produced after the model is built, often by people not involved in building it.
Continuous: monitoring that catches drift in production. Explainable: a clear chain from input to decision, traceable months later. Reversible: a kill switch you can pull without breaking the rest of the system. Accountable: a logged decision owner for every meaningful action.
Continuous monitoring built into the runtime. Decision logs auto-generated for every agent action. HITL configurable per workflow as a governance control. Kill switches at the agent, workflow, and workspace level - exercisable in seconds, not days.
Model risk
Traditional model risk management was designed for one statistical model doing one job. An AI agent platform has many models - orchestrators, sub-agents, evaluation judges, embedders - each potentially from different providers, updated on different schedules. We treat each model as an inventory item, each version as an artifact, and each deployment as a tracked use case.
Explainability
We don’t claim mechanistic interpretability of the underlying LLM - nobody can honestly claim that today. We do provide explainability at the level a risk committee actually needs: for any specific agent decision, you can reconstruct the input, the knowledge retrieved, the tools called, the sub-agent chain, the reasoning trace, and the final output. Six months later, in an audit, in a regulator’s office, in a customer complaint review.
Human oversight
HITL is often discussed as a way to improve agent accuracy. That’s true, but secondary. The primary purpose of HITL in a governance-regulated environment is to keep a human accountable for decisions the regulation requires a human to be accountable for. We make HITL a first-class control - configurable per workflow, per risk threshold, per amount, per outcome type.
Continuous validation
LLM providers update models on their own schedules. A model that scored 94% accuracy in March may behave differently in May - even on the same prompts. New document types arrive. New user patterns emerge. The world around the agent shifts. Drift monitoring is how governance survives in production.
Accountability
The hardest governance question isn’t “what did the AI do.” It’s “who is accountable for what the AI did.” Our model: every agent operates under a designated process owner. Every HITL decision is owned by a named reviewer. Every workflow change is approved by an authorized role. Every override is attributed. The audit isn’t about catching bad actors - it’s about being able to answer a regulator’s question six months from now.
Regulatory alignment
We do not certify against these frameworks. We build the platform so your team can comply with these frameworks while operating on top of us. The capabilities below are what we provide to support your compliance work - the certification is yours to obtain, with our documentation and controls as inputs.
We align to them. Your institution certifies its operations on top of us.
See the compliance posture in SecurityHonest limits
The AI governance category is full of vendors claiming capabilities they can’t defend. We’re explicit about the things we don’t claim - because over-claiming on governance damages your audit more than under-claiming.
We don’t claim to explain why a language model produced a specific token. Nobody can honestly claim that today. What we provide is decision-level traceability - input, retrieval, reasoning chain, tools called, output - which is what a regulator or auditor actually asks for.
We’re not SR 11-7 certified, DORA certified, EU AI Act certified, or MAS certified - and neither is anyone else, because most of these frameworks don’t have a certification mechanism. We align to them. Your institution certifies its operations on top of us.
We don’t guarantee an AI system will be free of bias. We provide bias evaluation capability - measurable per use case, per protected attribute - and documentation supporting your fairness assessment. Bias-free is a moving target; measurable bias is a defensible posture.
Read next
AI Governance is one of four enterprise pillars. The others cover delivery model, security controls, and infrastructure deployment.
Four-stage delivery model. Governance checkpoints exist at every stage - Stage 1 risk scoping, Stage 2 eval gate, Stage 3 go-live approval, Stage 4 continuous review.
See processThe security controls that governance depends on. Encryption, access control, audit, AI-native security primitives.
See securityWhere governance gets enforced - cloud, hybrid, on-prem. Deployment model affects what governance evidence is available.
See deploymentGet started
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