GDPR requirements your engineers and AI agentscan actually implement.
RuleMesh defines what GDPR requires, maps it to the controls your team should implement, and specifies the evidence that proves it was done. This page shows that loop end to end.
Three acts
Curate. Deliver. Close the loop.
RuleMesh takes a cited GDPR obligation, structures it as a rule, serves that rule over MCP to the agent doing the work, and sends the resulting task and evidence back into the team workflow. That is the mechanism.
ACT 01
We turn cited obligations into structured rules.
Legal text is not a technical specification. RuleMesh takes a cited GDPR obligation, keeps the article and paragraph attached, maps it to the cloud and security controls the team should implement, and defines the evidence a reviewer will expect.
That closes the first gap. Compliance keeps the legal citation. Engineering gets something it can build from. Audit gets a rule that can be checked before the scramble starts.
policyCited back to source law.
commitVersioned so requirement changes are visible.
apiDelivered in a form engineers and AI agents can act on.
ACT 02
The requirement is delivered over MCP, not trapped in a portal.
Once the rule is structured, it should move directly into the work. RuleMesh serves it over MCP so an agent can pull the requirement before it updates code, infrastructure, or the evidence workflow, and a compliance workflow can pull that same cited rule with provenance intact before sign-off.
This is where the machine-readable form matters. The requirement does not have to be rewritten into tickets, prompts, or audit notes at every handoff.
codeEngineering agents pull requirements, controls, and evidence expectations before implementation.
account_balanceCompliance workflows can retrieve the cited rule, its mapped controls, and its provenance.
hubOne curated rule graph reduces interpretation drift across teams.
ACT 03
The work lands in the systems the team already uses.
Compliance work dies when it lives in a portal nobody opens. RuleMesh pushes the requirement, the checklist, and the evidence signals into the project system the team already works from. Today that means Jira.
The point is not another dashboard. The point is to put the right task in front of the team that owns the surface, with traceability back to the requirement and evidence attached as the work moves.
assignmentRequirements become Jira work with the rule reference, owner, and priority already attached.
notifications_activeEvidence signals update the ticket as implementation lands and review happens.
extensionThe same pattern can extend to other work surfaces over time.
lock
Your source code never leaves your machine.
The agent scans locally. RuleMesh MCP receives file names and evidence signals — never file contents.
Run the loop on your own codebase.
Start with a free local scan. Keep source local. Add Jira when the team is ready to run compliance work inside the project it already uses.
GDPR requirements your engineers and AI agentscan actually implement.
RuleMesh defines what GDPR requires, maps it to the controls your team should implement, and specifies the evidence that proves it was done. This page shows that loop end to end.
The full loop, on one page.
Inputs · RuleMeshYour projectResults
Three acts
Curate. Deliver. Close the loop.
RuleMesh takes a cited GDPR obligation, structures it as a rule, serves that rule over MCP to the agent doing the work, and sends the resulting task and evidence back into the team workflow. That is the mechanism.
ACT 01
We turn cited obligations into structured rules.
Legal text is not a technical specification. RuleMesh takes a cited GDPR obligation, keeps the article and paragraph attached, maps it to the cloud and security controls the team should implement, and defines the evidence a reviewer will expect.
That closes the first gap. Compliance keeps the legal citation. Engineering gets something it can build from. Audit gets a rule that can be checked before the scramble starts.
policyCited back to source law.
commitVersioned so requirement changes are visible.
apiDelivered in a form engineers and AI agents can act on.
rulemesh · gdpr/art-32.rmCurate
GDPR Art. 32(1)(a)Pseudonymisation and encryption of personal data.
GDPR Art. 25(1)Data protection by design and by default.
south
// curated by RuleMesh · gdpr requirement graph · v2026.05
SHALL encrypt personal_dataATrest_and_in_transit
// TLS 1.2+ in transit · AES-256 at rest · customer-managed keys where required
The requirement is delivered over MCP, not trapped in a portal.
Once the rule is structured, it should move directly into the work. RuleMesh serves it over MCP so an agent can pull the requirement before it updates code, infrastructure, or the evidence workflow, and a compliance workflow can pull that same cited rule with provenance intact before sign-off.
This is where the machine-readable form matters. The requirement does not have to be rewritten into tickets, prompts, or audit notes at every handoff.
codeEngineering agents pull requirements, controls, and evidence expectations before implementation.
account_balanceCompliance workflows can retrieve the cited rule, its mapped controls, and its provenance.
hubOne curated rule graph reduces interpretation drift across teams.
rulemesh-mcp · requirement deliveryDeliver
hub
RuleMesh MCPgdpr requirement graph · v2026.05
code
Claude CodeEngineering · execution
“Implement encryption controls for personal data in the production data lake.”
check_smallPulled GDPR Art. 32 requirements, updated infra/data-lake.tf, and returned evidence signals for KMS, bucket policies, and key rotation.
“Show the control mappings and provenance behind this requirement.”
check_smallReturned the cited requirement, mapped controls, and the evidence a reviewer should expect before sign-off.
ACT 03
The work lands in the systems the team already uses.
Compliance work dies when it lives in a portal nobody opens. RuleMesh pushes the requirement, the checklist, and the evidence signals into the project system the team already works from. Today that means Jira.
The point is not another dashboard. The point is to put the right task in front of the team that owns the surface, with traceability back to the requirement and evidence attached as the work moves.
assignmentRequirements become Jira work with the rule reference, owner, and priority already attached.
notifications_activeEvidence signals update the ticket as implementation lands and review happens.
extensionThe same pattern can extend to other work surfaces over time.
jira · bundle ticket · updated by signalsIn the loop
COMP-184In Progress
Enable customer-managed key rotation on prod data lake
GDPR Art. 32(1)(a)High@platform-team
commitLinked to PR #4127 · evidence signals attached on review
Other surfaces in view
Jira
Slack
GitHub Issues
Linear
ServiceNow
MS Teams
Run the loop on your own codebase.
Start with a free local scan. Keep source local. Add Jira when the team is ready to run compliance work inside the project it already uses.