Test beyond the surface.
Can you trust your AI model to uphold its safety and alignment commitments across different prompts, representations, languages, and domains?
AI models may appear safe and aligned under standard testing, but the same safeguards can behave differently when inputs are reformulated or presented in unfamiliar ways.
For frontier AI builders, understanding where alignment breaks down, how frequently it happens, and under what conditions is essential to building trustworthy AI systems.
DATAmundi provides independent, evidence-based auditing to measure the robustness of AI safety and alignment in deployed models.
Assess whether deployed AI systems consistently uphold their stated safety and alignment policies, without requiring access to model weights or training data.
Test equivalent inputs across different representations, prompt structures, languages, and domains to identify where alignment behaviors fail.
Measure the frequency and conditions of alignment failures, producing reproducible evidence of where safeguards hold and where they can be bypassed.
Combine structured testing and automated analysis with subject-matter experts who review findings, validate judgments, and provide contextual insight.
Challenge AI models with adversarial prompts and scenarios, leveraging our global DATAtalent network to expose unsafe behaviors and vulnerabilities.
Auditing LLMs’ Hidden Political Stances via First-Order Logic
DATAmundi research presented at EMNLP 2026 | Budapest | PANDORA Workshop
How robust is an AI model’s neutrality when the same question is presented in a different form?
Our research investigates whether frontier AI models maintain their trained political neutrality when natural-language questions are systematically reformulated using first-order logic.
Understand where your AI model’s safeguards hold, where they fail, and how consistently they perform.
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