AI Governance Agent for Policy, Risk and Audit Evidence

An AI governance agent is a deployed AI system that inventories the models and agents a company runs, maps them to internal policy and external frameworks, routes risk assessments and collects audit evidence continuously. Where many AI governance tools stop at a register, the agent also covers AI workflow automation governance, moving intake, approvals and evidence through the process without manual chasing. Kanerika builds and operates these agents on the client’s own cloud, with human owners making every approval decision.

No tool makes an organization certified, and any vendor claiming that deserves suspicion. Certification requires an accredited external assessment of your management system. What the agent does is map your controls to ISO/IEC 42001 and NIST AI RMF and keep the evidence organized, so the assessment you pursue moves much faster.

Data governance answers where data lives, who touches it and how it is classified. AI governance answers which models exist, what they are approved to do and whether their behavior still matches that approval. The two connect, and Kanerika delivers both, but an AI inventory and assessment trail is its own discipline.

The agent runs in your own Azure, AWS, GCP or on-premises environment, and all inventory, assessment and evidence records stay in your tenant. Access follows your role-based controls, nothing is used to train external models, and Kanerika operates under SOC 2 Type II, ISO 27001, ISO 27701 and ISO 9001 credentials.

Weeks, following four phases. Kanerika builds the initial inventory and loads your policies, maps controls to the frameworks you answer to, then pilots intake and evidence collection with one portfolio or business unit. Enterprise go-live follows once the process has proven itself on real requests, with governed access from day one.

The agent discovers AI usage across the environment and compares it to the approved register, so unregistered tools and models surface quickly. Each discovery is routed to an owner for a decision rather than silently blocked, which keeps teams cooperative. Over time the register converges on reality instead of on what people remembered to report.

kanSuite is Kanerika’s modular governance services program, covering kanGovern for data governance strategy, kanComply for regulatory compliance frameworks and kanGuard for data security, all delivered on Microsoft Purview. The AI governance agent is a natural companion, extending the same discipline to the models and agents that consume all of that governed data.

Two published case studies anchor this page. A single governed ontology on Microsoft Fabric cut investigation time by 70% for one client, and a Microsoft Purview implementation gave a leading bank classified, policy-enforced control of its regulated data. Both case studies are linked above with full architecture and outcome detail for each engagement.

Start with a live inventory that gives every model and agent an owner, a purpose and a risk tier, so shadow AI surfaces instead of hiding. Then check that policies map to NIST AI RMF, ISO/IEC 42001 and the EU AI Act, that new use cases enter through structured intake, and that evidence is captured as work happens and traced to its source. Kanerika’s agent covers each of these inside your own tenant, with people signing off every decision.

Register every agent before it goes live, tier its risk at intake and route approval to the right owner. Re-assess automatically when a model or agent changes materially, because yesterday’s approval does not cover today’s system. Watch actual usage against the approved purpose, data scope and user group, and file approvals, test results and access reviews against the controls they satisfy. The governance agent applies these practices as part of normal work.