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GDPR Requirements Engineers Can Actually Implement

RuleMesh defines what to implement across your cloud infrastructure, how to execute it with framework-specific controls, and what evidence proves it was done — ready for engineers and AI agents.

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RuleMesh DSL

Article 32(1)(a) — Security of Processing

Requirement: Implement pseudonymisation and encryption of personal data as appropriate measures to ensure a level of security appropriate to the risk.

SHALL implement encryption_at_rest AND
encryption_in_transit
IN cloud_infrastructure
USING provider_KMS WITH key_rotation
Cloud Implementation
  • AWS: KMS Key Management, S3 SSE, RDS Encryption
  • Azure: Key Vault, Storage Service Encryption
  • GCP: Cloud KMS, Cloud Storage Encryption
Evidence
  • encryption configuration export
  • key rotation policy
  • TLS certificate settings

The Engineering Protocol

From one MCP command to a shareable evidence signals report in minutes.

01
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Connect your agent

One command adds RuleMesh to Claude Code, Cursor, or any MCP-compatible agent.

02
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Scan your codebase

Your agent evaluates your repo against 192 GDPR IT requirements and records evidence.

03
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Evidence Signals Report

A shareable report of what was found, what is partial, and what is missing.

04
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Track in Jira

Turn findings into Jira tickets with verification checklists and evidence tracking.

GDPR Compliance Dashboard inside Jira

Turn evidence signals into verified engineering work

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What happens in Jira

Your agent scans your codebase locally and reports evidence signals — file names, confidence scores, checklist matches — directly into Jira tickets. Checklists update automatically. The risk matrix shows your current state across all bundles.

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Why Jira

Engineers don't want another dashboard. RuleMesh injects requirements directly into the project management flow where work already happens.

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Privacy by design

RuleMesh never accesses your source code. Scans run locally via your AI agent. Only evidence metadata — file names and signal scores — is reported.

192 GDPR requirements. 7 engineering modules.

We decomposed 99 GDPR articles into structured requirements mapped to cloud controls, security frameworks, and evidence checklists.

32High

Controller Governance & Accountability

Art. 24-39: DPO, DPIA, processor agreements, records of processing

19High

Access Control & Security Measures

Art. 5, 9, 28-32: encryption, pseudonymisation, personnel controls

11High

Lawful Basis & Consent Engineering

Art. 6-8, 13: consent capture, legal basis, child protection

15High

Data Subject Rights Operations

Art. 12-22: access, rectification, erasure, portability, objection

16High

Breach & Change Notification

Art. 33-34: 72h notification, risk assessment, communication

16Moderate

International Transfer Governance

Art. 44-49: adequacy, SCCs, BCRs, derogations

9Moderate

Codes, Certifications & BCR

Art. 40-43, 47: codes of conduct, certification, binding rules

Mapped to 281 cloud security controls across AWS, Azure, GCP, and OWASP.

TECHNICAL SERIES 04MARCH 2026

Agent-Agnostic Compliance: How Three AI Models Interpret Identical Regulatory Data via MCP

Our technical study explores the elimination of “Agent Drift” in regulatory mapping. By using structured MCP servers, we achieved consistent compliance coverage across Claude, Gemini, and GPT.

Read the White Paperarrow_forward

Ship compliant code with confidence

Join forward-thinking engineering teams using RuleMesh to ship compliant products faster.

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