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AI Assurance

Trust in AI as the key for
acceptance and success

AI Assurance

Trust in AI as the key for
acceptance and success

AI Assurance | Secure & Trustworthy Artifical Intelligence

The deployment of Artificial Intelligence (AI) always involves decisions about risks, responsibility and long-term implications.

Artificial Intelligence creates new opportunities to make processes more efficient, faster and more autonomous. At the same time, it introduces new challenges: organisations must be able to assess reliably whether, and under which conditions, an AI system can be deployed safely, transparently and responsibly.

Particularly in safety-critical and regulated environments, traditional assessment approaches are often no longer sufficient. Risks frequently arise not within the model itself, but only during real-world operation − for example due to changing environmental conditions, incomplete input data or operational constraints. Even minor deviations from expected operating conditions can influence a system’s behaviour.

At the same time, requirements relating to safety, transparency, demonstrability and regulatory compliance are increasing. Organisations therefore face the challenge not only of technically evaluating AI systems, but also of providing robust evidence of their trustworthiness and operational suitability − both today and in light of future requirements.

What Organisations Need to Trust AI

Organisations need reliable evidence of whether an AI system is trustworthy and future-proof, under which conditions it can be deployed, and how uncertainties, errors or operational changes should be managed.

Many organisations still address these questions through isolated measures such as conventional software testing, checklists or individual assessments. While these approaches provide important building blocks, they are often insufficient to reliably assess real operational risks.

A robust AI Assurance approach must therefore answer key questions such as:

  • Which technical, regulatory and ethical requirements must an AI system be assessed against?
  • Which concrete criteria follow from these requirements for robustness, transparency and safety?
  • How can testing, evidence generation and monitoring be implemented reliably and efficiently?
Why AI Assurance?

AI Assurance makes AI systems trustworthy – 
technically, organisationally and regulatorily. 

AI Assurance − Our Approach

AI Assurance is a structured approach to the assessment, assurance and verification of AI systems in real-world operation. It provides the foundation for making informed decisions about the deployment of AI by creating transparency around risks and demonstrating the trustworthiness of a system in a traceable and verifiable manner.

The focus is not solely on the model itself, but on the actual behaviour of the AI system in interaction with data, processes, people and organisations. We assess AI systems consistently within their specific operational context, combining technical evaluation, organisational integration and regulatory requirements into one integrated overall assessment..

What Matters for Trustworthy AI

  • Safety & Security

  • Robustness & Reliablity

  • Traceability & Transparency

  • Compliance & Standardisation

  • Ethics & Governance

How we put AI Assurance into Practice

AI Assurance is not a one-off test, but a structured, lifecycle-oriented process. Its goal is to identify risks at an early stage, define clear requirements and systematically demonstrate the trustworthiness of an AI system.

Our approach consists of five key steps:

  • Context & Risk Analysis

    We analyse the operational context, risks and relevant boundary conditions, including regulatory requirements, standards and domain-specific constraints. These are consolidated into one consistent set of requirements.

    Your benefit:

    • Transparency regarding real operating conditions
    • Early identification of critical risks
    • A clear basis for informed decision-making

  • Deriving Requirements

    Based on the operational context, we define concrete and verifiable requirements, including metrics, thresholds and release criteria. These are operationalised in a way that makes them both technically testable and organisationally applicable.

    Your benefit:

    • Clear criteria instead of abstract principles
    • Traceable foundations for decision-making
    • Structured guidance for development, assessment and release processes

  • Technical Assessment

    AI systems are systematically assessed − from simulations and controlled test environments to realistic operational scenarios. We deliberately analyse rare and critical situations, as well as system behaviour under uncertainty.

    Your benefit:

    • Reliable insights into robustness and performance limits
    • Early identification of vulnerabilities and risks
    • Testable and auditable evidencen und Risiken
    • Prüf- und auditierbare Nachweise

  • Assurance Documentation & Release Support

    All results are structured and translated into auditable evidence. At the same time, we support release and alignment processes with internal and external stakeholders.

    Depending on the outcome, this may result in:

    • Full approval
    • Conditional approval
    • Concrete action plans for risk reduction

    Your benefit:

    • Confidence in audit and certification processes
    • Clear communication with decision-makers and assessors
    • Reduced effort for rework and coordination

  • Operations, Monitoring & Continuous Improvement

    AI Assurance does not end with the initial assessment. Even after release, the system remains under continuous review: we monitor system behaviour, detect changes and perform targeted re-evaluations. This ensures that AI systems remain reliable, traceable and responsibly deployable over the long term.

    Your benefit:

    • Stable and controlled long-term operation
    • Early detection of issues during live operation
    • Sustainable assurance of trustworthiness

AI in the Context of Complex Integrated Systems

AI systems are often deployed as part of complex cyber-physical and mechatronic systems. For this reason, we do not consider AI Assurance in isolation, but always in interaction with sensors, software, hardware, people and operational processes.

Safety & Security Compliance of Mechatronic Systems

Your Benefit

AI Assurance provides the foundation not only for technically assessing AI systems, but also for making informed decisions about their deployment. Risks become visible at an early stage, requirements remain traceable and evidence becomes robust − even in complex and regulated environments.

  • Informed decisions on AI deployment
    AI systems are not assessed in isolation, but within their specific operational context − providing a reliable basis for release and operational decisions.
  • Early and targeted risk management
    Risks arising from the interaction between models, data and operational conditions are identified at an early stage − before they become critical in live operation.
  • Verifiable compliance and regulatory confidence
    Requirements derived from standards and regulation are translated into concrete, testable criteria and auditable evidence.
  • Realistic assessment instead of isolated test resultsWhat matters is not only model performance, but the actual behaviour of a system under realistic operating conditions.
  • Stable and future-proof operation
    Monitoring, re-evaluation and continuous improvement support a controlled and sustainable long-term deployment of AI systems.

To ensure that AI Assurance goes beyond abstract principles, we translate requirements into concrete assessment and verification procedures. The safeAI Kit supports this process as a modular toolbox for technical analysis, evidence generation and monitoring.

safeAI-Kit: Our Tool-Kit for Safe AI

The safeAI Kit combines standardised methods, established best practices and proprietary approaches to systematically assess key characteristics such as robustness, uncertainty, explainability and system limitations. Our methodological work is based not only on practical experience, but also on the active development of standards and guidelines. This creates robust and traceable evidence that is both technically sound and suitable for decision-making and audits.

What does this mean for you?

  • Consistent and traceable assessments
    Standardised and reusable assessment modules ensure that results remain comparable and transparent.
  • Robust insights instead of isolated tests
    By combining different methods, we enable a well-founded assessment of actual system behaviour − beyond isolated individual tests.
  • Efficient implementation of AI Assurance
    Modular building blocks reduce effort and enable structured, scalable assessment and verification processes.
  • Support throughout the entire lifecycle
    The safeAI Kit supports not only initial assessments, but also monitoring, drift detection and continuous re-evaluation during operation.

Our Expertise in Standardisation and Regulation

We combine current insights from AI research, standardisation and regulation with many years of experience in testing, analysis and certification processes.

Through our active participation in national, European and international standardisation committees, we contribute to the further development of requirements and assessment approaches for AI systems.

With the initiation of DIN SPEC 92005 on the “Quantification of Uncertainties in Machine Learning”, we have made a concrete contribution in this field. This work also served as the foundation for the ISO/IEC standardisation project SO/IEC TS 25223 on uncertainties in AI systems. The standard is expected to be developed under IABG leadership until early 2028.

You can also read the interview with our standardisation expert Dr Lukas Höhndorf: “Normung macht den AI Act greifbar” (DIN e.V., 11 December 2025).

safeAI in Action

  • AI Assurance Framework

    The assessment of the safety and reliability of an AI model and its underlying dataset requires evaluation across multiple dimensions within a suitable assessment infrastructure.

  • SIL-/HIL-Testing

    The integration of AI-based software components into a cyber-physical system requires extensive testing in both virtual and real-world environments to ensure that the system can be deployed safely.

  • Generation of Synthetic Data

    The systematic generation of synthetic data provides reproducible meta-annotations and enables a high level of consistency and scalability. Targeted scenarios, such as edge cases, support coverage analysis and validation of the Operational Design Domain (ODD).

Build Trust in Your
AI Systems.
Bastian Bernhardt
Team AI Assurance
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