Compliance requirements your engineers and AI agents can actually implement.
Implement regulations faster, with less manual work.
A regulatory obligation is not an implementation requirement.
Doing this manually is slow and requires specialist expertise.
High-performance teams need structured requirements, mapped controls, and evidence expectations - ready for implementation
Your team gets engineering work, not legal text.
A regulation arrives as modules. Each module is a unit of work you can hand to a person, with the control that satisfies it and the evidence that defends it already attached.
GDPR requirements
The evidence exists before anyone asks for it.
The requirement, the evidence submitted against it, and its review status stay connected from the day the work is defined.
A request for proof can pull engineers back into old work: searching tickets, locating configuration records, reconstructing decisions. RuleMesh establishes what evidence is expected as the work is defined. Agents submit evidence. A person reviews it.
How a regulation becomes work, and work becomes evidence.
Your coding agent pulls the requirements, runs the checks in its own environment, and files the findings where your team tracks work.
From one MCP command to a shareable evidence signals report.
Connect your agent
Evaluate your code & environment
Review the evidence signals
Humans make the final determination.
Track the work in your issue tracker
Connect your agent
Evaluate your code & environment
Review the evidence signals
Humans make the final determination.
Track the work in your issue tracker
Delivered into the tools your team already works in.
The Jira app is live; we are building more issue or project integrations.
Showing Jira dashboard: Dashboard overview
RuleMesh for Jira screenshots. Illustrative data.
View the app on the Atlassian MarketplaceThe Jira app is live
Findings arrive as tickets carrying the requirement, its citation, and the evidence checklist that closes it.
Works with your existing tools
Engineers do not want another dashboard. The work lands in the tracker your team already runs, and the evidence stays attached to it.
The security work you already do counts toward the regulation.
Requirements arrive mapped to controls your team already implements and audits against. Cloud security controls for AWS, Azure and GCP apply across the catalog; each released regulation lists the frameworks it maps to.
Change the agent. Keep the cited rule.
RuleMesh gives each connected agent the same requirement, control mapping, and evidence criterion. The model can change without asking every team to reinterpret the regulation from scratch.
Read the methodology→An open protocol for machine-verifiable compliance exchange.
RuleMesh structures the work inside an organisation. HCAP is our open protocol proposal for exchanging compliance information across system and organisational boundaries.
Start with scope, terms, or the regulation itself.
Use these reference surfaces when you need applicability and definitions before implementation.
What Applies To Me
A guided scope interview backed by the RuleMesh rules engine. Answer a few plain-English questions; get your role, what specifically applies, and the verbatim article text for every conclusion.
Open the checker→GDPR Hub
The engineering-facing read of GDPR: scope, key terms, and the obligation clusters that matter first. Packaged end to end.
Open the GDPR hub→AI Act Hub
The terms, roles, and value-chain obligations that shape how AI systems are built, shipped, and governed.
Open the AI Act hub→This page is also published for machine readers: rulemesh.com/index.md
Latest reports and analysis
Written by the team building RuleMesh and updated as we publish. Older pieces are in the library.
The New Economics of Audit Evidence
Analysis · Audit automation · 26 September 2026 · 6 min read
Audit sampling exists because auditor attention is expensive. As that stops being true, evidence has to come from the running system rather than a folder assembled before fieldwork.
Your team can get started straight away with the AI agents they already use.
It's free to get started. See the evidence before you decide.