What Is an AI-Native GRC Platform?
An AI-native GRC platform is a governance, risk, and compliance system in which AI is the operating substrate: it reasons over governed, connected compliance data instead of being a chatbot bolted onto static records. Every answer traces back to evidence with a hash, lineage, and confidence score, and queries below the confidence threshold go to a human.
Kyūdō is an AI-native Governance, Risk, and Compliance platform for regulated organizations that need to automate evidence collection, map controls across frameworks, manage vendor risk, govern AI risk, and produce audit-ready proof from Microsoft security data. It deploys inside the customer's own Azure tenant, and this page explains what makes that architecture different. See the platform overview for the full module map.
Why traditional GRC is not enough
Traditional GRC tools were built as systems of record: places to file controls, upload evidence, and track assessment dates. That design has three structural limits that no amount of added AI chat can remove.
Disconnected records
Controls, risks, policies, and vendor assessments live in separate modules or spreadsheets. When a control fails, nothing tells you which risks, frameworks, or vendor relationships are affected.
Point-in-time assessments
Evidence is gathered for the audit window, then goes stale the day after. Between assessments, posture is assumed rather than observed.
Manual evidence collection
Teams export screenshots and configuration dumps by hand, then repeat the work for every framework. The effort scales with the number of obligations, not the size of the estate.
For a side-by-side view of the two models, see Kyūdō vs. traditional GRC.
How Kyūdō uses AI with governed evidence
AI is only as trustworthy as the data it reasons over and the guardrails around its output. Kyūdō pairs the Compliance Graphwith four mechanisms, and all inference runs on Azure OpenAI Service inside the customer's tenant. Read how the customer-hosted architecture keeps the entire loop in your environment.
Deterministic retrieval
Sensei AI Advisor does not free-associate. It retrieves from the Compliance Graph, a governed data layer, so the same question over the same data returns the same grounded answer.
Citations to source nodes
Every answer cites the graph nodes it draws from: the specific control, evidence artifact, or policy. Reviewers verify the source, not the model.
Confidence scoring
Every artifact and every answer carries a confidence score alongside its hash and lineage. Scores are visible, not hidden, so teams know how much weight an output can bear.
Human-in-the-loop routing
Queries that fall below the confidence threshold are routed to human review rather than answered speculatively. The platform prepares; people approve.
The Compliance Graph, explained
The Compliance Graph is the data layer that makes AI-native operation possible. It models compliance as connected entities rather than rows in separate tables: controls, risks, policies, vendors, evidence, frameworks, and AI systems all reference each other. When one entity changes, everything connected to it knows.
1,400+ controls in a unified catalog
80+ frameworks via SCF crosswalk
Each artifact hashed, scored, and lineaged
Linked to the controls that treat them
Connected to the controls they mandate
Third parties tied to shared controls
Governed by 156 AI controls
Vendor relationships and AI systems are first-class entities, which is why vendor risk and AI governance run on the same graph as controls and evidence rather than in bolt-on modules. Terms unfamiliar? The glossary defines each one.
What changes operationally
The architectural difference shows up as a workflow difference. Two examples illustrate it. For the signal pipeline itself, see Microsoft-native GRC.
Add a framework without starting over
When a new framework enters scope, the Secure Controls Framework crosswalk with STRM semantic mapping (NIST IR 8477) maps your existing controls to the new requirements. You review the mapping; you do not rebuild the program.
Evidence recalculates on every signal
Signals from Microsoft Defender XDR, Sentinel, Purview, Entra ID, and Azure Policy, plus AWS, Google Cloud, and Oracle Cloud, flow in continuously. Control status reflects the estate as it is now, not as it was at the last assessment.
See the Compliance Graph reason over your estate
Deploy inside your Azure tenant and watch evidence, controls, and frameworks connect from your own signals.
Questions, answered
An AI-native GRC platform is a governance, risk, and compliance system in which AI reasons over governed, connected compliance data as the platform's operating substrate, rather than serving as a chat feature added to static records. Kyūdō implements this with a Compliance Graph that connects controls, risks, policies, vendors, evidence, and frameworks, and with Sensei AI Advisor, which retrieves deterministically from that graph and cites its source nodes.
GRC with AI features bolts a language model onto records that were designed for manual workflows, so answers cannot be traced to governed data. AI-native GRC inverts this: the data layer is a connected Compliance Graph built for machine reasoning, every artifact carries a hash, lineage, and confidence score, and AI answers cite the specific nodes they draw from. The difference is architectural, not cosmetic.
No. Kyūdō is agentic with human-in-the-loop, not unattended. Sensei AI Advisor answers from the Compliance Graph with citations and a confidence score, and queries that fall below the confidence threshold are routed to human review instead of being answered speculatively. People approve; the platform prepares.
Kyūdō reasons over evidence converted from Microsoft Defender XDR, Microsoft Sentinel, Microsoft Purview, Microsoft Entra ID, and Azure Policy signals, plus signals ingested from AWS, Google Cloud, and Oracle Cloud. Each evidence artifact carries a hash, lineage, and confidence score, and it is linked in the Compliance Graph to the controls, risks, policies, vendors, and frameworks it supports.
In Kyūdō's case, AI inference runs on Azure OpenAI Service inside the customer's own Azure tenant. The platform deploys through Azure Managed Applications with private endpoints only and no cross-tenant SaaS data plane, so prompts, evidence, and answers never leave the environment the customer controls.
No platform certifies an organization; independent assessors do. Kyūdō supports readiness by keeping evidence current, mapping controls across frameworks through the Secure Controls Framework crosswalk, and producing audit-ready artifacts with provenance that assessors can verify.
Looking for more? See all frequently asked questions.