How a Product Team in Vienna Improved Governance Aligned with EU AI Act Using AI Risk Classification Automation

In Vienna, a dynamic product team faced the growing challenge of aligning their AI solutions with the stringent demands of the EU AI Act regulation📊. The manual governance and reporting of AI risk classification, especially focusing on high-risk screening, was labor-intensive, error-prone, and lacked transparency. This case study explores how automating compliance requirements replaced cumbersome manual processes, significantly improving risk management, transparency, human oversight, technical documentation, and traceability.

In this article, you will learn about the technical architecture and workflow design powered by RestFlow in conjunction with popular automation platforms like n8n, enabling seamless AI risk classification automation. We will dive into the client context, the challenges faced, our solution approach, detailed workflow steps, and key business results achieved. Whether you are a startup CTO, automation engineer, or operations specialist, discover practical insights and hands-on instructions to replicate this success.

Continue reading to see how automation-as-a-service can be a game-changer for AI governance aligned with the EU AI Act.

Case Context & Problem: AI Governance Challenges for a Vienna-based Product Team

The client is a product team based in Vienna, Austria, operating within the technology sector developing AI-powered solutions. The increasing regulatory pressure from the newly introduced EU AI Act mandated rigorous risk management for AI systems, especially when determining whether AI applications are classified as high-risk.

Previously, the team’s AI risk classification and reporting process was manual. They reviewed AI components through spreadsheets and emails, routing cases for approvals and storing evidence in decentralized folders. This fragmented approach resulted in:

• Up to 30 hours per month spent on manual data collection and risk assessments.
• High error rates and inconsistencies in classification decisions leading to compliance risks.
• Delays in approvals causing slowed go-to-market timelines.
• Lack of audit-ready documentation and traceability, raising compliance concerns.
• Limited transparency and human oversight complexity for compliance officers and product managers.

These inefficiencies put pressure on the product operations and compliance teams, increased their operational costs, and exposed the organization to potential penalties under the EU AI Act.

Our Approach: Designing a Compliance-First Automation Strategy with RestFlow

RestFlow was engaged as a compliance-first automation partner to analyze and transform the client’s AI risk classification process. Our approach involved several key steps:

• Discovery & Process Mapping: We conducted detailed workshops with product owners, compliance officers, and IT to document the end-to-end manual workflows, data touchpoints, and pain points.
• Regulatory Alignment: Embedded EU AI Act compliance themes — risk management, transparency, human oversight, technical documentation, and traceability — into the automation design.
• Technology Evaluation: Assessed automation platforms n8n, Make, and Zapier for their flexibility, integration capabilities, and scalability. We selected n8n for its open-source extensibility and strong API integrations.
• Architecture Blueprint: Designed a modular architecture where AI risk classification requests trigger automated workflows that classify risk, route approvals, collect evidence, and produce audit-ready documentation.

This strategic approach ensured the automated process not only improved operational efficiency but was also fully compliant with EU AI Act requirements.

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The Solution: Architecture & Workflow Overview

The architecture implemented for this automation leveraged n8n as the central orchestration platform integrating multiple systems and services seamlessly:

Global Architecture Components:

• Triggers: Incoming AI risk classification requests received via secure webhook or form submission.
• Orchestration Tool: n8n manages workflow execution with condition branches and error handling.
• External Services Integrated:
  • Slack: For alert notifications and approval requests.
  • Google Sheets: Central repository for enriched records and reporting.
  • HubSpot CRM: Synchronization with relevant project and stakeholder data.
  • Cloud Storage (Google Drive): Storage of evidence and technical documentation.
• Outputs: Automated status reports, audit logs, dashboards, and traceability documentation.

End-to-End Workflow Walkthrough

1. Request Trigger: The workflow starts when an AI feature owner submits a risk classification form via a secure web portal.

2. Data Validation & Enrichment: The workflow validates inputs (mandatory fields, format checks) and enriches data with metadata from HubSpot via API.

3. Risk Classification Logic: Conditional nodes apply EU AI Act criteria to classify the risk level as “high-risk” or otherwise.

4. Routing and Approvals: For high-risk cases, automated Slack messages request approvals from legal and compliance teams.

5. Documentation Collection: The workflow collects evidence documents and technical documentation links from Google Drive.

6. Logging & Reporting: All transactions and decisions are logged in Google Sheets, generating traceable audit records.

7. Notifications & Dashboards: Stakeholders receive status updates via Slack, and reports are available in dashboards.

Step-by-Step Node Breakdown 📋

1. Webhook Trigger Node 🚦

Role: Listens for new AI risk classification requests submitted by product owners through a REST API endpoint.
Key Fields: Captures submission data including AI model name, description, usage context, and initial risk inputs.
Configuration: Secured with API keys passed in HTTP headers to ensure authenticated access.

2. Data Validation Node ✔️

Role: Checks the required fields such as AI purpose, dataset descriptions, and compliance questions.
Logic: Filters out incomplete or malformed requests. Returns error responses with clear messages for resubmission.
Mapping: Utilizes JSON path queries to verify presence and format.

3. Data Enrichment Node 🔍

Role: Calls HubSpot API to enrich request with owner contact info and project metadata.
Fields: Uses “Find Contact by Email” and “Get Project Details” API operations.
Mapping: Maps enriched data back to the workflow for decision steps.

4. Risk Classification Logic Node 🧠

Role: Implements business rules aligned with EU AI Act risk criteria.
Conditions: If AI application processes biometric data, interacts with critical infrastructure, or impacts safety, classify as high-risk.
Branching: Route high-risk cases to approval; others marked compliant.

5. Approval Routing Node 📨

Role: Sends Slack messages to compliance and legal teams requesting review and approval.
Fields: Includes request ID, risk classification, and evidence links.
Configuration: Slack interactive buttons enable approvers to confirm decisions directly.

6. Documentation Collection Node 📁

Role: Pulls associated evidence and technical documentation from Google Drive links provided.
Mapping: Consolidates links and metadata for traceability.

7. Logging and Reporting Node 📊

Role: Records all data, decisions, timestamps, and user actions into Google Sheets.
Fields: Includes unique request IDs, status, approvers’ names, and timestamps.

8. Notification & Dashboard Update Node 📢

Role: Posts final status updates to Slack channels.
Outputs: Updates a centralized dashboard source for operations visibility.

Error Handling, Robustness & Security

Error Handling & Retries

Each node in the workflow is configured with retry strategies, including exponential backoff for transient failures such as API rate limits. Failures trigger alerts to designated Slack channels for immediate human attention. Critical errors pause the workflow and log incidents in an error queue spreadsheet for monitoring.

Logging & Observability

All workflow activities, including errors and approvals, are logged with unique identifiers for audit purposes. RestFlow’s built-in monitoring tools track run history, step durations, and throughput metrics.

Idempotency & Deduplication

To prevent duplicate processing, webhook node checks for unique request IDs. Subsequent workflow executions verify prior completion through Google Sheets logs before proceeding.

Security & Data Protection

API keys and tokens are stored securely in n8n credentials with least-privilege scopes. All PII and sensitive AI configuration data remain encrypted in transit. Access to the workflow and data is role-based, ensuring auditability and compliance with data protection regulations.

Performance, Scaling & Extensibility

The workflow is designed to smoothly scale with increasing request volumes by utilizing webhooks for real-time triggers and queue mechanisms for batching approval notifications. Parallelization is implemented for servicing concurrent requests effectively.

New teams or AI product lines can be onboarded by reusing modular workflow components with minor configuration changes. Geographical expansion and new regulatory rules are accommodated by maintaining versioned workflows and deploying updates safely in staging before production.

RestFlow’s managed hosting environment ensures stable operations with continuous monitoring and auto-scaling capabilities.

Comparison Tables

Automation Platform	Cost	Pros	Cons
n8n	Free for self-host; Subscription starts at €20/month	Open-source; highly customizable; self-host option; extensive API integrations	Requires more technical skills; set-up complexity
Make	Starts at €9/month	Visual scenario builder; great for multi-step workflows; extensive app integrations	Pricing scales with operations; limited custom coding
Zapier	Starts at $19.99/month	User-friendly; wide app ecosystem; fast workflow creation	Less suited for complex logic; higher cost for volume

Trigger Method	Latency	Scalability	Pros	Cons
Webhook	Real-time (seconds)	High	Efficient; reduces load; immediate processing	Requires endpoint exposure and security management
Polling	Interval based (e.g., 5 mins)	Moderate	Simple implementation; no endpoint needed	Inefficient; latency delays; potential rate limits

Data Store	Cost	Data Structure	Pros	Cons
Google Sheets	Free up to limits	Spreadsheet rows and columns	Easy to use; accessible; quick setup	Not ideal for complex queries; limited concurrency
Cloud Database (e.g., Firestore)	Varies with usage	NoSQL or SQL schemas	Highly scalable; complex queries; concurrency	Requires more setup; cost overhead

Results & Business Impact

The implementation of the AI risk classification automation delivered measurable benefits:

• 80% reduction in manual processing time (down from 30 to 6 hours monthly) [Source: to be added].
• Near elimination of classification errors due to consistent automated rules.
• Approval response times accelerated by 50%, enabling faster releases.
• Full audit-ready documentation on-demand improved compliance confidence.
• Enhanced transparency through Slack notifications and shared dashboards.

The product and compliance teams experienced calmer, more predictable operations. Stakeholders could trace every decision and action effortlessly, aligning with the strict transparency and traceability requirements of the EU AI Act.

Pilot Phase & Ongoing Maintenance Disclaimer

As with any complex automation project, we conducted a controlled pilot phase where the workflow ran with real but limited data. During this phase, minor bugs, edge cases, and user feedback led to iterative refinements.

Following successful pilot completion, RestFlow assumed full responsibility for ongoing hosting, monitoring, updates, and compliance audits. This ensures that the automation remains robust, secure, and aligned with evolving regulatory requirements and business needs over time.

Frequently Asked Questions

Conclusion: Transforming AI Risk Governance through Automation with RestFlow

The case of the Vienna product team highlights how automating AI risk classification processes can dramatically improve governance and reporting aligned with the EU AI Act. By replacing inefficient manual procedures with a robust automated workflow, the team achieved enhanced risk management, transparency, human oversight, and traceability.

RestFlow’s role as a compliance-first automation partner was instrumental in the design, implementation, and ongoing operation of this scalable and secure workflow using n8n and key integrations such as Slack, Google Sheets, and HubSpot.

For organizations looking to confidently automate their AI compliance requirements and achieve audit-ready governance, RestFlow offers end-to-end Automation-as-a-Service, including hosting, monitoring, and maintenance.

Take the next step in modernizing your compliance operations: Explore the Automation Template Marketplace or Create Your Free RestFlow Account today.