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Strategic risk control for AI systems under the EU AI Act

EU AI Act Risk Management System

The EU AI Act requires solid risk management systems for high-risk AI systems. We support you in developing and implementing comprehensive, compliance-conformant risk control processes.

  • ✓Full EU AI Act compliance for risk management systems
  • ✓Systematic risk identification and assessment for AI systems
  • ✓Integrated governance and oversight frameworks
  • ✓Continuous risk control and adaptation mechanisms

Your strategic success starts here

Our clients trust our expertise in digital transformation, compliance, and risk management

30 Minutes • Non-binding • Immediately available

For optimal preparation of your strategy session:

  • Your strategic goals and objectives
  • Desired business outcomes and ROI
  • Steps already taken

Or contact us directly:

info@advisori.de+49 69 913 113-01

Certifications, Partners and more...

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EU AI Act Risk Management System

Our Expertise

  • In-depth knowledge of EU AI Act requirements and best practices
  • Experience in implementing risk management systems across various industries
  • Comprehensive approach from technical implementation to organisational integration
  • Effective methods for automating and optimising risk processes
⚠

Regulatory Note

The risk management system must be proportionate to the risk class of the AI system and continuously updated throughout the entire lifecycle. A proactive, systematic approach is essential for successful compliance.

ADVISORI in Numbers

11+

Years of Experience

120+

Employees

520+

Projects

We develop a regulation-compliant risk management system under Article 9 EU AI Act with you, systematically integrated into your existing processes.

Our Approach:

Inventory: Identification and classification of your AI systems according to the AI Act risk categories (Annex I and III)

Risk assessment: Systematic analysis of identified risks per Article 9(2) — risks under intended use and reasonably foreseeable misuse

Measure design: Development of appropriate risk mitigation measures prioritising elimination, reduction and residual risk acceptance

Testing and validation: Definition of metrics and probabilistic thresholds per Article 9(7) for systematic pre-market testing

Integration and operation: Embedding the risk management system into existing governance structures with continuous monitoring and updates

"A solid risk management system for AI is not only a regulatory requirement, but a strategic building block for trustworthy AI. With systematic approaches, organisations can ensure compliance while continuously improving the quality and reliability of their AI systems."
Asan Stefanski

Asan Stefanski

Head of Digital Transformation

Expertise & Experience:

11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI

LinkedIn Profile

Our Services

We offer you tailored solutions for your digital transformation

Risk Analysis and System Assessment

Comprehensive assessment of your AI systems and existing risk management processes to identify compliance gaps and optimisation potential.

  • Systematic classification and risk assessment of your AI systems
  • Gap analysis of existing risk management processes
  • Identification of regulatory requirements and compliance gaps
  • Development of a prioritised implementation roadmap

Risk Management System Design and Implementation

Development and implementation of tailored, EU AI Act-compliant risk management systems with all required processes and controls.

  • Design of systematic risk assessment and classification procedures
  • Development of risk mitigation and control measures
  • Building continuous monitoring and reporting processes
  • Integration into existing governance and IT infrastructures

Our Competencies in EU AI Act High-Risk AI Systems

Choose the area that fits your requirements

EU AI Act Data Governance

Article 10 of the EU AI Act imposes strict requirements on training, validation and test data for high-risk AI systems. We support you in building data governance that ensures data quality, detects bias and meets the documentation obligations under the AI Regulation.

EU AI Act Human Oversight

Article 14 of the EU AI Act requires providers and deployers of high-risk AI systems to implement effective human oversight. We help you establish human-in-the-loop processes, stop mechanisms, and monitoring frameworks — compliant by the 2 August 2026 deadline.

EU AI Act Record Keeping

Article 12 of the EU AI Act requires providers and deployers of high-risk AI systems to implement automatic logging of all system-relevant events throughout the lifecycle. We support you in building compliant logging systems, audit trail structures and retention policies.

EU AI Act Technical Documentation

The EU AI Act places high demands on the technical documentation of high-risk AI systems. We support you in creating comprehensive, compliance-conformant documentation that meets all regulatory standards.

Frequently Asked Questions about EU AI Act Risk Management System

What does Article 9 of the EU AI Act specifically require from providers of high-risk AI systems?

Article

9 EU AI Act requires providers of high-risk AI systems to establish, implement, document and maintain a risk management system. The system must be planned and run as a continuous iterative process throughout the entire lifecycle of the AI system. Specifically, it covers: (a) identification and analysis of known and foreseeable risks, (b) estimation and evaluation of risks arising from intended use and reasonably foreseeable misuse, (c) evaluation of further risks based on post-market monitoring data under Article 72, and (d) adoption of appropriate risk management measures. The implementation deadline for high-risk AI systems is August 2026.

What steps does the Article 9 AI Act risk management process involve?

The risk management process under Article

9 AI Act comprises four core steps: First, risk identification — the systematic determination of all known and foreseeable risks to health, safety and fundamental rights. Second, risk assessment — the estimation of identified risks considering intended use and reasonably foreseeable misuse. Third, risk mitigation — determining appropriate measures following the principle: first elimination, then reduction, finally documented acceptance of residual risks. Fourth, validation — testing against predefined metrics and probabilistic thresholds before placing on the market.

How does the AI risk management system differ from traditional risk management?

The risk management system under Article

9 AI Act differs from traditional risk management in several ways: It is specifically designed for the entire lifecycle of an AI system — from conception to decommissioning. It requires consideration of AI-specific risks such as algorithmic bias, lack of explainability and unintended impacts on fundamental rights. The system must also account for interactions between the various requirements in Chapter III Section

2 (Article 9(4)). Unlike traditional risk approaches, the AI Act also mandates specific testing requirements with predefined metrics and probabilistic thresholds.

What testing requirements does Article 9 AI Act impose on high-risk AI systems?

Article 9(7) AI Act requires high-risk AI systems to be tested at appropriate points throughout the development process — and in any case before placing on the market or putting into service. Tests must be performed against predefined metrics and probabilistic thresholds appropriate for the intended purpose. Particular consideration must be given to whether the system is intended for minors or other vulnerable groups (Article 9(9)). Testing under real-world conditions may be conducted in accordance with Article 60.

What does residual risk acceptance mean under Article 9 AI Act?

Under Article 9(6) AI Act, residual risks that cannot be fully eliminated through mitigation measures must be classified as acceptable and documented. The residual risks, taken together with the measures adopted, must not pose an unacceptable risk. The provider must inform users about residual risks and provide appropriate instructions for use. Documentation of residual risk acceptance forms part of the technical documentation under Article

11 and is reviewed by market surveillance authorities.

What is the deadline for implementing the risk management system under the EU AI Act?

For high-risk AI systems under Annex III of the EU AI Act, the transition period runs until

2 August 2026. From that date, all providers of such systems must be able to demonstrate a complete risk management system in accordance with Article 9. For AI systems used as safety components of products under Annex I, the requirements apply from

2 August 2027. We recommend starting implementation now, as building a comprehensive risk management system typically takes

6 to

12 months.

How does ADVISORI support Article 9 AI Act risk management system implementation?

ADVISORI supports the complete implementation of Article

9 AI Act in four phases: (1) inventory and classification of your AI systems according to the AI Act risk categories, (2) building the risk management system with risk assessment procedures, metrics and documentation templates, (3) integration into existing governance structures such as ERM frameworks or ISO 31000, and (4) support with testing and validation before placing on the market. We account for interactions with other requirements including the quality management system (Article 17), technical documentation (Article 11) and post-market monitoring (Article 72).

Success Stories

Discover how we support companies in their digital transformation

Digitalization in Steel Trading

Klöckner & Co

Digital Transformation in Steel Trading

Case Study
Digitalisierung im Stahlhandel - Klöckner & Co

Results

Over 2 billion euros in annual revenue through digital channels
Goal to achieve 60% of revenue online by 2022
Improved customer satisfaction through automated processes

AI-Powered Manufacturing Optimization

Siemens

Smart Manufacturing Solutions for Maximum Value Creation

Case Study
Case study image for AI-Powered Manufacturing Optimization

Results

Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization

AI Automation in Production

Festo

Intelligent Networking for Future-Proof Production Systems

Case Study
FESTO AI Case Study

Results

Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products

Generative AI in Manufacturing

Bosch

AI Process Optimization for Improved Production Efficiency

Case Study
BOSCH KI-Prozessoptimierung für bessere Produktionseffizienz

Results

Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime

Let's

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