Testing AI Chatbots: What Companies Need to Know About the New EU Regulation

Chatbots are taking on more and more tasks – in customer service, marketing, and sales. But as their use grows, so do the risks: self-learning AI systems can behave unpredictably, expose sensitive data, or violate legal requirements.

This issue becomes even more pressing in light of the EU AI Act, which will be phased in starting February 2, 2025. It sets strict standards for the safety, transparency, and oversight of AI systems – including chatbots based on machine learning.

In this article, I’ll outline what really matters when testing self-learning AI chatbots, highlight key risks to watch out for, and show how to prepare your systems for the new regulatory landscape.

Where and How AI Chatbots Are Already Being Used

AI chatbots take on a wide range of tasks depending on where they’re deployed – both internally and externally. Public-facing systems like Google Gemini, ChatGPT by OpenAI, or Microsoft Copilot interact directly with customers and must respond with a high degree of precision and sensitivity. In contrast, internal enterprise bots are subject to different requirements – particularly around data protection, context awareness, and system integration.

Platform matters, too: some chatbots are cloud-based, while others run locally using open-source models like Meta LLaMa or DeepSeek-R1. Each setup comes with unique challenges around setup, integration, and ongoing maintenance.

The type of training data also varies. While some systems use public data, many companies rely on internal sources – such as documents, knowledge bases, or dynamic web content. To ensure quality responses, self-learning models combined with RLHF (Reinforcement Learning from Human Feedback) play a key role.

Risks You Should Be Aware of When Using AI Chatbots

Implementing AI chatbots comes with several risks, including:

• Manipulation & Data Poisoning: Chatbots can be vulnerable to attacks that alter their behavior unintentionally.
• Lack of Control Over Responses: They may generate inappropriate or biased content.
• Data Leaks: Since they rely on extensive datasets, there's an increased risk of exposing sensitive information.
• Regulatory Challenges: Ensuring legal compliance is complex – cases like DPD- and Air-Canada show that violations can be costly.
• Jailbreaking LLMs: Research shows that persuasive adversarial prompts (PAPs) succeed in 92% of cases, bypassing safeguards in even well-secured models (Persuasive Jailbreaker).

How the EU AI Regulation Will Impact Your AI Systems

The EU AI Act, passed on August 1, 2024, introduces a new regulatory framework for artificial intelligence. Its phased rollout means businesses must now ensure their AI tools – including chatbots – meet strict compliance requirements.

Self-learning AI systems that adapt through user interaction are under particular scrutiny. They must be safe, transparent, and act in line with ethical standards.

⚠️ Warning: Non-compliance can be expensive – fines can reach up to €35 million or 7% of global revenue.

The most serious violations include:

• Banned AI practices

• Non-compliance with data protection and security standards
• Lack of transparency in how the AI operates

How to Test AI Chatbots Effectively and in Compliance

To meet the EU AI Act, testing self-learning chatbots involves more than just checking functionality. Key areas to cover include:

• Transparency & Disclosure: The use of AI must be clearly communicated.

• Data Management: Training data must be strictly controlled to ensure data protection and integrity.

• Accuracy & Reliability: Errors and biases need to be minimized.

• Human Oversight: Mechanisms for monitoring and intervention must be in place.

• Risk Management: Potential risks must be proactively identified and mitigated.

New Testing Strategies Are Needed – Traditional IT Tests Are No Longer Enough. Key Measures Include:

• Clearly defining the test scope: Create detailed test instructions for AI models to establish clear evaluation criteria.

• Optimizing training methods: Apply appropriate constraints to ensure alignment with ethical and operational guidelines.

• Risk- and scenario-based testing: Focus on data processing, compliance, and operational integrity.

• Negative & guardrail tests: Assess how the chatbot responds to invalid or inappropriate inputs.

• Live monitoring: Continuously test the system in production to ensure ethical standards and quality are upheld.

Conclusion

The landscape of AI chatbot deployment and testing is complex, with new requirements and compliance standards are and will become applicable. We recommend commitment to pioneering testing methodologies that not only meet these demands but also advance the reliability and integrity of AI technologies. With the EU AI Act's penalties looming for non-compliance, our test approach is designed to navigate these challenges, ensuring that AI chatbots can continue to serve as innovative and compliant tools in the digital age.