--- title: "Understand and test AI" description: "How to test AI systems reliably, fairly and in accordance with the rules: Discover modern approaches and risks in the field of artificial intelligence." type: article version: 1 version_id: "772c3c8c-8df0-4aca-8297-fa71834a8ffa" generated_at: "2026-04-29T10:29:44.105Z" author: "clicking submit below" language: en reading_time: "28 min" word_count: 5480 keywords: ["We can enable you.", "TestSolutions Academy"] url: "https://testsolutions.de/en/focus/ai-testing" --- # Understand and test AI > How to test AI systems reliably, fairly and in accordance with the rules: Discover modern approaches and risks in the field of artificial intelligence. ## Key Takeaways - Which AI Systems Do We Assess? - No rivets. You always win with us. - We can enable you. - Let's talk about your AI quality assurance needs - contact us! - TestSolutions Academy ## Contents - [Which AI Systems Do We Assess?](#which-ai-systems-do-we-assess) - [No rivets. You always win with us.](#no-rivets-you-always-win-with-us) - [We can enable you.](#we-can-enable-you) - [Let's talk about your AI quality assurance needs - contact us!](#let-s-talk-about-your-ai-quality-assurance-needs-contact-us) - [TestSolutions Academy](#testsolutions-academy) # Understand and Test Artificial Intelligence Artificial intelligence is no longer a topic for the future. Today, it is used productively in almost all industries - from automated decisions to generative systems. However, with its increasing use comes a central challenge: how do you ensure that AI systems function reliably, fairly and in accordance with the rules? This page provides a structured overview of modern AI, typical risks and the role of AI testing as a key success factor. Request expertise #### Comprehensive AI testing We test AI systems along their entire life cycle - from data to models to application. #### **Recognizing risks** We identify weaknesses such as bias, misconduct and security risks in AI systems. #### **Creating transparency** We make decisions made by AI systems comprehensible and easy to understand. #### **Enabling trust** We support the safe, fair and compliant use of AI systems. ### Confidence to Move AI Forward >  “At TestSolutions, our focus is to bring state-of-the-art testing capabilities to AI-augmented systems. > > Given their non-deterministic nature, we ensure that the right technical and compliance guardrails are in place, so that you can deploy them with confidence.” > > \-- **Anupam Krishnamurthy**, Head of AI Testing  ### What is modern artificial intelligence? Modern AI systems can be divided into three main categories. [ ##### Multimodal AI - understanding data and reality Multimodal systems combine different types of data such as text, images, audio or sensor data. Typical applications include - Damage detection in insurance companies - Quality control in industry - Fraud detection in the financial sector These systems enable a more comprehensive understanding of complex situations. ](https://testsolutions.de/blog?hsLang=en) [ ##### Generative AI - content generation Generative AI creates new content based on data and models. This includes - Texts and reports - Software code - Analyses and summaries Typical fields of application are copilots, chatbots or automated knowledge processing. ](https://testsolutions.de/blog?hsLang=en) [ ##### Agentic AI - systems that act Agentic AI describes systems that not only analyze or generate, but also actively act. They make decisions and execute processes, for example - automatic scheduling - autonomous workflows - Decision support in real time The greatest added value is created when understanding, generation and action are combined. ](https://testsolutions.de/blog?hsLang=en) ### What risks does AI pose? With the increasing use of AI systems, new risks arise that differ significantly from traditional software. While traditional systems work deterministically, AI models make probabilistic decisions - with corresponding new challenges for quality, safety and control. Recent years have shown: faulty chatbot responses lead to legal disputes. Manipulable systems are publicly exposed. Discriminating models create liability risks. Agents that act beyond their scope trigger uncontrollable processes. These are not isolated incidents. They are systematic weaknesses that remain invisible without professional testing. Request more information - Wrong Decisions AI systems can deliver incorrect, incomplete or contextually inappropriate results - especially with complex or unexpected inputs. - Lack of Transparency Many AI systems are difficult to understand. Decisions often cannot be clearly explained or verified. - Bias and Discrimination Models can adopt distortions from training data and thus systematically disadvantage certain groups. - Security Gaps New forms of attack such as prompt injection or data manipulation can specifically influence the behavior of AI systems. - Regulatory Risks The EU AI Act and other regulations create clear requirements for the traceability, documentation and testing of AI systems. - Poor Data Foundation Errors, duplicates and outdated content reduce reliability and usefulness of the tool. ### What is AI Testing? AI testing refers to the systematic testing of AI systems over their entire lifespan. In contrast to classic software testing, it is not just about functionality, but about the behavior of systems under uncertainty. Typical questions are: - Does the system make reliable decisions? - Is the behavior stable and robust? - Are the results comprehensible and fair? - Does the system meet regulatory requirements? The areas of safety, governance and fairness in particular are becoming increasingly important. Certain KPIs have been developed and proven useful as baseline for testing AI systems.  ### Confidence in AI Starts With Evidence > "Testing AI means more than measuring technical performance. > > It also means verifying whether governance, accountability and oversight are strong enough to support responsible deployment.” > > \-- **Prof. Dr. Marco Barenkamp**, Advisory Board Member & AI Expert [More on Marco Barenkamp](https://testsolutions.de/en/blog/prof.-dr.-marco-barenkamp-joins-testsolutions-advisory-board?hsLang=en) ### Prevent AI Risks Through Testing with KPIs in Mind **Factual reliability** leads to fewer wrong decisions and complaints. **Demonstrable security** is a result of hardened systems and documented test results. **Legal protection** of providers and users is based on compliance evidence for regulations such as the EU AI Act and GDPR. **A** **stable foundation** of the model leads to better data and less rework in production. Doing things right reduces costs. We help you validate your AI and analyze potential issues. But what are the **KEY METRICS** for that? ##### **F1-Score** How well do responses match verified references? *Objective, comparable statement on answer quality*  ##### **Hallucination Rate** How often are factually unreliable statements produced? *Reduced risk in critical use cases*  ##### **Injection Success Rate** How often does an attack on the system succeed? *Reliable evidence of security hardening*  ##### **Demographic Parity Difference** Does the system treat all groups equally? *Legally relevant metric for non-discrimination*  ##### **PSI / Drift Score** How much do production data deviate from training data? *Early warning of gradual quality deterioration*  ##### **Task Success Rate** How reliably does an agent complete its tasks? *Transparency on reliability and automation maturity*  ### When Should You Get Your AI Tested? - Validation and issue analysis regarding your AI KPIs - Before go-live of a new AI system - After model changes, prompt updates or system changes - When experiencing quality issues in production - Before audits, approvals or regulatory reviews - When choosing between models or architectures - As a permanent part of your quality process Consult us - we scope testing needs ## Which AI Systems Do We Assess? We help our clients in testing a selection of prominent use cases of modern AI - and consult on much more. ##### Chatbots & Assistants LLM-based dialogue systems must do more than provide good answers – they must be reliable, secure and consistent. Even in edge cases. **Typical risk:** Incorrect information, tone failures, weak fallback behaviour, missing AI disclosure **What we assess:** - Answer quality & factual accuracy - Robustness against reformulations - Handling of uncertainty & refusal - Security & manipulation resistance ##### Knowledge Assistants (RAG) For knowledge-based systems, not only the answer matters but also its derivation. We assess whether relevant content is found, correctly used and traceable to the right sources. **Typical risk:** Wrong sources, outdated content, weak retrieval despite plausible answer, unauthorised access to confidential documents **What we assess:** - Retrieval quality & source fidelity - Hallucination rate on knowledge questions - Data leakage from knowledge base - Document currency ##### AI Agents Agents must do more than give good answers. They plan, use tools and execute actions – reliably, safely and in a controlled manner. **Typical risk:** Unintended actions, error propagation across steps, prompt injection via external sources, irreversible actions **What we assess:** - Task completion & efficiency - Tool usage & scope compliance - Injection resistance & security boundaries - Irreversibility of actions ##### Decision Systems & ML Models Automated decisions in credit, HR or public administration are regulatorily high-risk. We assess fairness, accuracy and explainability – as the basis for compliance evidence. **Typical risk:** Discrimination by protected attributes, model drift, lack of explainability towards affected individuals **What we assess:** - Fairness & bias per group - Model accuracy & drift detection - Explainability of individual decisions - Regulatory compliance ##### Complex AI Landscapes (Enterprise) When AI is deployed across multiple departments with different risk profiles, you need a unified quality framework – not a patchwork of individual tests. **Typical risk:** Inconsistent quality standards, missing governance across systems **What we assess:** - Portfolio inventory & risk classification - Unified quality framework - Governance & compliance evidence - Continuous monitoring ##### AI Advisory Not every organisation needs a test first. Sometimes what is needed first is clarity – about strategy, risks and the right next steps. **Typical risk:** Missing AI strategy, unclear responsibilities, regulatory exposure **What we offer:** - AI Act Readiness Assessment - Governance structure & AI policy - Regulatory risk mapping - Management briefing & roadmap ## No rivets. You always win with us. We know iGaming systems inside out - scratch the boxes. LOTTERYFORCE Central omnichannel lottery management. SCRATCH Brightstar Volaris Proven IGT platform for transactions. RUBBLE Brightstar Aurora next-gen high-performance core system. RUBBLE Imperia CMS content management for web portals. RUBBELN AEGIS Regulatory Monitoring & Compliance. RUBBLE Symphony Secure workflow automation. RUBBELN \* Mouseover or touch to reveal.  | TestSolutions MethodologyThe TestSolutions AI Quality FrameworkBehind our assessment services stands a structured methodology: the TestSolutions AI Quality Framework.It combines three pillars that together enable a complete evaluation:Governance, technical quality, and system-specific testing.Pillar 1Governance & AccountabilityTechnical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001Pillar 2Technical Quality TestingSix quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every controlPillar 3System & Context SpecificsEach system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | Pillar 1Governance & AccountabilityTechnical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001 | Pillar 1Governance & Accountability | Technical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001 | Pillar 2Technical Quality TestingSix quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every control | Pillar 2Technical Quality Testing | Six quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every control | Pillar 3System & Context SpecificsEach system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | Pillar 3System & Context Specifics | Each system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Pillar 1Governance & AccountabilityTechnical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001 | Pillar 1Governance & Accountability | Technical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001 | Pillar 2Technical Quality TestingSix quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every control | Pillar 2Technical Quality Testing | Six quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every control | Pillar 3System & Context SpecificsEach system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | Pillar 3System & Context Specifics | Each system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | | | Pillar 1Governance & Accountability | Technical testing alone is not sufficient. A system can pass quality tests and still remain a risk if responsibilities, oversight and documentation are unclear.EU AI Act risk classificationHuman oversight (Art. 14)Accountability structuresDocumentation and transparency requirementsAligned with EU AI Act, GDPR and ISO 42001 | Pillar 2Technical Quality Testing | Six quality dimensions with 46 measurable controls assess whether the system does what it should — correctly, safely, fairly and with a sound data foundation.6 quality dimensions46 measurable controlsClear metric for every control | Pillar 3System & Context Specifics | Each system type has its own risks and therefore needs a dedicated testing methodology.LLMsRAG systemsAgentsML modelsComputer visionAutomated decision systems | | | | |  ### Confidence in Your AI Testing Processes > "The real question is not whether AI can write code. It's whether your organization can verify that software is actually fit for purpose. > > Independent testing helps make that visible — before defects, compliance gaps, or hidden quality risks reach production." > > \-- **Florian Fieber**, Chief Process Officer, Head of Academy, Keynote Speaker [Florian Fieber's Blog](https://testsolutions.de/en/blog/author/florian-fieber?hsLang=en) ### Why traditional software testing is not enough AI systems behave differently from conventional software. Their outputs are probabilistic, sensitive to changing inputs, and can evolve over time as data and models change. That is why traditional testing methods are no longer sufficient on their own. Effective AI testing requires approaches such as scenario-based testing, adversarial testing, bias and fairness analysis, prompt and input variation, and continuous monitoring after deployment. In other words, AI systems cannot be validated once and considered done. They need ongoing testing and assurance throughout their lifecycle to remain reliable, responsible, and under control. Contact us to learn more ### AI is used in high-risk areas. Testing is non-optional. Today, AI is being used in a growing number of business-critical and high-risk areas. These include HR and recruiting, lending and credit scoring, medical diagnostics, public administration, customer service and chatbots, as well as fraud detection. Many of these use cases involve elevated risks and therefore require structured testing and verification procedures. As AI becomes more deeply embedded in operational decision-making, ensuring reliability, accountability, and compliance is no longer optional. Let us risk assess your AI ## We can enable you. TestSolutions Academy offers practical AI training for testers and users. Learn how to test AI-based systems, use AI effectively in testing, and apply AI confidently and responsibly in daily project work. [](https://testsolutions.de/en/academy/istqb/seminar-ai-testing?hsLang=en) ##### ISTQB Certified Tester - AI Testing Acquire a basic understanding and skills for testing AI-based software systems and the use of AI technologies in testing. [](https://testsolutions.de/en/academy/istqb/seminar-ct-genai?hsLang=en) ##### ISTQB Certified Tester - Testing with Generative AI Gain a basic understanding of generative AI in software testing, including testing GenAI systems and using GenAI to support and automate testing. [](https://testsolutions.de/en/academy/a4q/elearning-ai-essentials?hsLang=en) ##### A4Q AI Essentials This e-learning and certification provides an introduction to AI compliance, ethics and risk awareness - no prior technical knowledge is required. [](https://testsolutions.de/en/academy/a4q/seminar-ai-foundation?hsLang=en) ##### A4Q AI Foundation Gain a comprehensive understanding of how generative AI can be used responsibly and effectively in accordance with regulatory requirements. You will acquire basic AI skills in accordance with the EU AI Act. [](https://testsolutions.de/en/academy/originals/seminar-ai-testing?hsLang=en) ##### TestSolutions Originals - Basics of AI Testing  Learn the basic concepts, terms and procedures of testing AI-based systems. It is suitable for anyone who is interested in AI testing and wants a quick and easy introduction to the topic. ### AI News from TestSolutions Stay informed on our newest developments, projects, products and get sector insights. [ ](https://testsolutions.de/en/blog/ai-evals-explained-evaluating-llm-outputs-and-the-challenges-involved?hsLang=en) [ #### AI Evals Explained: Evaluating LLM Outputs and the challenges involved ](https://testsolutions.de/en/blog/ai-evals-explained-evaluating-llm-outputs-and-the-challenges-involved?hsLang=en) Apr 28, 2026 If you've been following news on technical developments in AI, you'd have probably seen the term 'evals'... [ ](https://testsolutions.de/en/blog/ai-writes-the-code.-who-tests-it?hsLang=en) [ #### AI Writes the Code. Who Tests It? ](https://testsolutions.de/en/blog/ai-writes-the-code.-who-tests-it?hsLang=en) Apr 21, 2026 There's a widespread assumption quietly spreading through software development teams: AI tools are so good... [ ](https://testsolutions.de/en/blog/prof.-dr.-marco-barenkamp-joins-testsolutions-advisory-board?hsLang=en) [ #### Prof. Dr. Marco Barenkamp joins TestSolutions Advisory Board ](https://testsolutions.de/en/blog/prof.-dr.-marco-barenkamp-joins-testsolutions-advisory-board?hsLang=en) Apr 2, 2026 TestSolutions is pleased to welcome Prof. Dr. Marco Barenkamp, LL.M. to its Advisory Board. With his deep... [ ](https://testsolutions.de/en/blog/testing-sdui-ki-agents?hsLang=en) [ #### Testing SDUI and AI agents ](https://testsolutions.de/en/blog/testing-sdui-ki-agents?hsLang=en) Mar 10, 2026 Software development is currently experiencing a paradigm shift that is fundamentally changing the... [More](https://testsolutions.de/en/blog?hsLang=en) ## Let's talk about your AI quality assurance needs - contact us! ###### +49 (0) 69 15 02 46 61 Telephone ### Case Studies Find out how we turn complex test projects into measurable successes. Our practical examples show how we work with our customers to ensure quality and minimize risks. [ ](https://testsolutions.de/case-studies/kleines-release-gro%C3%9Fe-folgen-tests-im-lottierewesen?hsLang=en) [ #### Kleines Release, große Folgen: Ein Praxisfall aus dem Lotterieumfeld ](https://testsolutions.de/case-studies/kleines-release-gro%C3%9Fe-folgen-tests-im-lottierewesen?hsLang=en) Apr 15, 2026 Drei Tage nach einem unspektakulären Release einer mobilen Anwendung im Lotterieumfeld häuften sich erste... [ ](https://testsolutions.de/case-studies/die-einf%C3%BChrung-und-der-betrieb-von-servicenow?hsLang=en) [ #### Qualitätssicherung für Einführung und Betrieb von ServiceNow ](https://testsolutions.de/case-studies/die-einf%C3%BChrung-und-der-betrieb-von-servicenow?hsLang=en) Feb 12, 2026 Ein führendes Unternehmen setzt ServiceNow als zentrales Infrastruktur-Service-Management-Tool ein. In einer... 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Our training courses are theoretically sound, practical and directly applicable. Whether ISTQB, A4Q, IREB, Xray or individual workshops - with us you learn what really matters. For companies or private individuals - we deliver the know-how! [To the academy](https://www.qualitydojo.com/) ### News from TestSolutions Stay informed about our latest developments, projects and industry insights. [ ](https://testsolutions.de/blog/en/blog/ai-evals-explained-evaluating-llm-outputs-and-the-challenges-involved?hsLang=en) [ #### AI Evals erklärt: LLM-Outputs evaluieren und die Herausforderungen dahinter ](https://testsolutions.de/blog/en/blog/ai-evals-explained-evaluating-llm-outputs-and-the-challenges-involved?hsLang=en) Apr 28, 2026 Wer die technischen KI-Neuigkeiten verfolgt, wird den Begriff „Evals" in letzter Zeit überall gesehen haben.... [ ](https://testsolutions.de/blog/life-sciences-qa-bug-fixing?hsLang=en) [ #### Software Testing in den Life Sciences: Mehr als Bug Fixing ](https://testsolutions.de/blog/life-sciences-qa-bug-fixing?hsLang=en) Apr 22, 2026 Im traditionellen Softwarekontext wird Qualitätssicherung häufig auf funktionale Tests und Bug-Fixing... [ ](https://testsolutions.de/blog/blog/ki-schreibt-den-code-wer-testet-ihn?hsLang=en) [ #### KI schreibt den Code. Wer testet ihn? ](https://testsolutions.de/blog/blog/ki-schreibt-den-code-wer-testet-ihn?hsLang=en) Apr 21, 2026 Es gibt eine weit verbreitete Annahme, die sich in Software-Entwicklungsteams heimlich verbreitet: Die... [ ](https://testsolutions.de/blog/datenschutz-in-der-cloud-standort-daten?hsLang=en) [ #### Cloud-Nutzung in deutschen Unternehmen: Chancen, Risiken und Datensouveränität ](https://testsolutions.de/blog/datenschutz-in-der-cloud-standort-daten?hsLang=en) Apr 9, 2026 Die Digitalisierung schreitet voran – und mit ihr die Nutzung von Cloud-Diensten. Ob Microsoft Azure, Jira... [View all](https://testsolutions.de/blog?hsLang=en) --- ## About This Content **Source:** [Understand and test AI](https://testsolutions.de/en/focus/ai-testing) **Author:** clicking submit below *This content is provided for informational purposes. Please visit the original source for the most up-to-date information.*