Configurable governance for AI systems
Repeatable product foundations, configured for each enterprise environment.
Govern360 (Under development)
A configurable control product for regulated AI systems.
Govern360 will give regulated teams a repeatable AI control set.
Each deployment will be configured to the enterprise environment: cloud platform, identity model, data routes, tools, approvals, evidence needs, and release process.
What Govern360 Is
Govern360 provides a repeatable set of control modules for AI systems that need to survive security, audit, and operational review. Each deployment is adapted to the client’s platforms, policies, risk appetite, and operating model.
It exists for teams that already know governance cannot stay in policy documents alone. Govern360 is part of the system build: the controls need to exist inside the system, the cloud platform, and the way changes are released.
What the Product Covers
- Access: Users, services, data, tools, approved routes, and gateway rules. This answers the access question: who or what can reach the system, through which route, and under which conditions.
- Action and Approval: Approved actions, blocked actions, approval points, limits, and stop conditions. This answers the control question: should this action happen now, and what should happen if the answer is no.
- Evidence: Records showing the request, the approval, the tool or service used, the result returned, and the final outcome. This answers the evidence question: what happened, who or what caused it, and what can still be proven later.
- Change: Checks before release, monitoring after release, rollback paths, and named ownership. This answers the change question: how do the controls stay true when the system changes.
Where It Fits
Govern360 sits in the build path, after the business-process outcome, future workflow, and controls are defined and before the system is treated as production-ready.
The handoff is a governed workflow specification containing the business-process outcome, future-state workflow, human and system responsibilities, autonomy boundary, data and tool routes, exception paths, evidence requirements, measures, ownership, and change triggers.
It is normally configured in one of two ways:
- after an AI Control Pack, when agreed controls need to become working parts of the system
- inside a production architecture engagement, where the product becomes the governance layer across the client’s cloud, identity, data, workflow, approval, and release constraints
Example Use Cases
- AI Gateway and access controls for regulated workloads
- Rules for AI systems that use tools or trigger workflows
- Records for high-consequence workflows
- Release and change controls for cloud-hosted AI systems
- Approval and evidence design for systems under audit or security scrutiny
What It Is Not
- Not a legal opinion
- Not a compliance certification service
- Not a one-click tool that ignores enterprise constraints
- Not a claim that one control replaces all the others
- Not a business-process discovery, workflow redesign, or investment-prioritisation product
- Not a substitute for the process owner or accountable business decision