A Way for AI agents to support you with regulatory compliance

Five terms carry the whole system.

Requirement

What a regulation obliges your organisation to do, written as work an engineer can implement and linked to the provision it comes from.

Control mapping

The connection from a requirement to the security controls that satisfy it.

Evidence expectation

What a reviewer will need to see, defined alongside the requirement before the work begins.

Module

A group of related requirements, sized as a unit of work your team can plan around.

Regulatory Intelligence (INTEL)

The paid plan. Amendments, guidance and court rulings arrive mapped to the requirements they change.

From regulation to verified evidence.

A regulation is engineered into requirements.

Every regulation is reviewed by our experts before release. Each requirement links to its source provision.

Requirements arrive in your tools.

The Jira app, the MCP server for AI agents, and cloud policy output.

Your agents assess your environment.

Coding agents and other AI agents work against the requirements and evidence expectations, in your environment, using data from RuleMesh.

Evidence signals are submitted.

Names and metadata, never the underlying content.

A person reviews and verifies.

Agents can submit evidence and update progress, but only a human can mark an item as verified.

The same answer, for every agent, every time.

Atomic requirements.

Each requirement carries its obligation, condition, control mapping and evidence expectation as one object, linked to the legal text.

An agent retrieves it; it does not interpret the regulation, and anything it relies on can be checked at source.

RuleMesh enables automated compliance checks.

Because the requirements are structured and answer consistently, checks can run without a person driving each one.

A team can put an agent in a loop: retrieve the relevant requirements, assess the environment against them, submit the evidence signals, and repeat, on a schedule or on every change.

What reaches a reviewer is current evidence rather than a quarterly snapshot.

Only a human verifies.

Agents can submit evidence and update progress, but only a human can mark an item as verified.

Checks can run as often as the work changes; the sign-off stays with a person.

What the requirements map to.

Cloud security controls apply across the catalog. Security-framework mappings apply per regulation.

Cloud security controls are benchmark-derived and cover AWS, Azure and GCP, so that one mapping serves whichever cloud your team runs. For covered regulations, requirements also map to control frameworks, and the frameworks follow the regulation’s domain: each regulation arrives with the frameworks its own engineers work in.

Every mapping is readable: open a requirement and see which controls satisfy it. Evidence expectations name the configurations and records that demonstrate the control ran.

In the tools your team already uses.

Requirements make more sense as part of the product or service story: in the workflow the team already uses, where they are not missed. Evidence gets approved at the right moment, while the context is available, and it stays traceable for the engineering team afterwards.

Jira app

Requirements, implementation work and evidence, connected in Jira.

Live on the Atlassian Marketplace

MCP server

For AI agents, coding and otherwise.

Available now

Cloud policy output

For Terraform, OPA and Azure Policy.

Design-partner preview

We are integrating with more of the platforms engineering teams already use to track their work. The API and the MCP server are available when a team wants to build its own flow. Setting up? The MCP documentation has everything your engineers need: rulemesh.com/docs/mcp

This page is also published for machine readers: rulemesh.com/how-it-works.md

See it work against a regulation you face.

Get started free → Request a demo →