AI compliance is not a one-time project—it is an ongoing process. Our AI compliance monitoring service monitors your AI systems in production for performance degradation, fairness drift, data quality, and regulatory requirements. You receive early warning signals in case of problems and automated compliance reports for auditors and authorities.

🔄 Continuous | 📊 Real-time dashboard | 💰 From €2,500/month | ⚡ Setup: 1-2 weeks

Problem 1: Data drift undermines compliance

Your AI models were trained on historical data. In production, they see new, changed data. The problem: “concept drift” and “data drift.”

Examples:

• COVID-19: Consumer behavior changed radically → E-commerce recommendation systems had to be adapted
• Economic crises: Credit scoring models trained on boom years fail during recessions
• Demographic change: HR systems must adapt to changing applicant pools and evolving educational concepts

The risk:
A system that was fair at deployment may develop discriminatory patterns after 6-12 months. You won’t notice it until a complaint or audit is filed.

EU AI Act perspective:
Article 72 explicitly states: “Providers shall establish and document a post-market monitoring system” – compliance without monitoring is illegal.

Problem 2: Model decay and performance degradation

Machine learning models “age.” Their accuracy declines, predictions become unreliable, and error rates increase.

Causes:

• Data changes (see drift)
• Correlations break down
• New patterns emerge that the model does not recognize
• Adversarial attacks (malicious manipulation)

Business Impact:

• Credit scoring system: Rising false positive rate → Good customers are rejected → Revenue loss
• Fraud Detection: Declining True Positive Rate → More Fraud Undetected → Direct Financial Loss
• HR screening: Bias creeps in → Violations of the AGG → Legal problems

Without monitoring: You only notice degradation when it’s too late

Problem 3: Regulatory documentation requirements

The EU AI Act not only requires you to monitor—you must also document it.

Article 72 Requirements:

• Establish and document a post-market monitoring system
• Systematically collect data on system performance
• Analysis of continuous compliance
• Documentation of incidents
• Regular reviews

The problem:
Manual documentation is:

• Time-consuming (data scientists have better things to do)
• Prone to errors (people forget, overlook)
• Not scalable (10 systems = 10x effort)
• Not audit-proof (inconsistent documentation)

Without automation: Compliance becomes a full-time job.

Problem 4: Incident Reporting & Response

Article 73 of the EU AI Act requires: “Providers shall report serious incidents to market surveillance authorities.”

Definition of “serious incident”:

• Death or serious damage to health
• Serious violation of fundamental rights
• Serious disruption to critical infrastructure
• Significant property damage or environmental damage

The challenge:

• How can you recognize an incident in time?
• How do you document the incident in an audit-proof manner?
• How do you ensure that all stakeholders are informed?
• How do you report to authorities within the legal deadlines?

Without structured incident management: You risk not only fines, but also system shutdowns by authorities.

1. Automated drift detection
You don’t have to manually check whether your system is still working—we do that automatically.

We monitor:

• Data drift (has the input data changed?)
• Concept drift (have relationships changed?)
• Prediction drift (does the model deliver different outputs than before?)
• Performance metrics (accuracy, precision, recall, F1)
• Fairness metrics (bias over time)

Technology: Alibi Detect, Evidently AI, Custom Statistical Test

2. Real-time compliance dashboard
All relevant metrics at a glance—for you, your teams, and auditors.

Dashboard features:

• Live metrics for every monitored system
• Trend analyses (how do metrics develop over weeks/months?)
• Threshold visualizations (green/yellow/red)
• Drill down into details (which features are drifting? Which subgroups?)
• Multi-system view (all your AI systems on one interface)

Access: Web-based, multi-user, role-based access rights

3. Incident Management & Reporting Identifying
problems is step 1—responding to them is step 2.

Our system:

• Alert system (email, Slack, Teams, PagerDuty)
• Threshold-based escalation (minor alert → major alert → critical incident)
• Incident tracking (automatically documents: What, When, Who informs?)
• Quarterly compliance reports (automatically generated for auditors)
• Annual Reviews (in-depth analysis with our experts)

Output: Audit-proof documentation for EU AI Act Articles 72 & 73

💡 More than just monitoring

We use open source software and tailor the selection of AI monitoring tools to your needs and use case. We take a close look at which metrics and thresholds are relevant and appropriate, and in which time frames you need information about your AI system. The end result is an AI compliance monitoring system that helps you strategically monitor AI systems while meeting regulatory requirements.

Afterwards, we support you in interpreting trends and, if necessary, correcting the systems.

The setup (once per system):

☑ Monitoring infrastructure (installed & configured)
☑ Monitoring dashboard (live, accessible 24/7)
☑ Alert setup (Slack/email, according to your preferences)
☑ Baseline metrics (reference values for comparison)
☑ Monitoring documentation (for audits)
☑ Team training (2-hour workshop: using the dashboard, interpreting alerts)

Is continuous AI compliance monitoring relevant for you?

✅ You MUST monitor if:

• High-risk AI systems in production (EU AI Act requirement)
• More than 10,000 predictions per month
• Business-critical decisions (credit, HR, healthcare)

✅ You SHOULD monitor if:

• AI system has been in production for >6 months
• Input data could change (e.g., market changes)
• Fairness is important (risk of discrimination)
• You want to sleep (instead of waking up with every alert)

❌ AI monitoring is not yet necessary if:

• System still in development/testing
• Only a few predictions (<1,000/month)
• Your application is not classified as a critical application under the AI Act.

📅 Week 1: Setup & Integration

• Kickoff call (1 hour): Requirements, access, timeline
• Infrastructure setup: Installation of monitoring stack
• Integration: Connection to your AI system (API/webhook)
• Baseline creation: Reference metrics from historical data

📅 Week 2: Configuration & Training

• Dashboard configuration: Customization to your use cases
• Alert setup: Define thresholds, notification channels
• Documentation: Monitoring plan (EU AI Act compliant)
• Team training: 2-hour workshop for your teams

📅 Week 3+: Live operation

• Monitoring runs automatically 24/7
• You receive alerts for critical events
• Monthly: Support check-in (optional)
• Quarterly: Compliance report + review meeting

📅 Annual: Deep-Dive Review

• 12-month analysis
• Retraining recommendations
• Risk assessment update