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AI Red Teaming

Anticipate the real risks of generative AI
Modern generative AI agents that are integrated into chatbots, customer support platforms, process automation, or development tools are increasingly connected with corporate APIs, third-party services, and internal data repositories. This fast-paced evolution introduces a new attack surface and major risks that are often underestimated.
Illustration of a notebook with access denied to a document folder

Specialized evaluation of GenAI behavior

We systematically assess the robustness of systems that employ generative models, including Large Language Models (LLMs), multimodal models, and other AI architectures. We design and replicate under controlled conditions tailored attack scenarios, based on how models are used and integrated with applications and sensitive data stores.

The model’s resilience is evaluated against threats such as:

Bypassing authorization mechanisms to access protected data

Exfiltrating internal documentation and company know-how

Generating harmful or misleading content (hallucination)

Misusing model capabilities to evade limits or exhaust system resources

Our approach considers the implications of EU Regulation 2024/1689, which requires demonstrable and effective security controls for GenAI systems.