We provide adversarial testing, safety analysis, and child-safety datasets so AI developers can confidently deploy models without exposing children to harm.
Building with insights from teams at
The rapid advancement of AI technology has created unprecedented risks for child safety online. Current testing approaches are inadequate, inconsistent, and often expose teams to harmful content. The industry needs a better solution.
Cairos generates high-signal, legally compliant synthetic data that helps AI teams detect, classify, and prevent child-safety harms across text, voice, and vision modalities.
Our primary offering is a secure pipeline that generates adversarial, policy-aligned synthetic data designed to expose failure modes and strengthen model defenses. We simulate grooming, coercion, exploitation patterns, and high-risk behaviors using controlled agentic systems enabling high coverage and minimal human exposure.
Cairos runs automated adversarial simulations against your models to identify weaknesses across child-safety risk categories. These evaluations use synthetic scenarios, not real harmful content, and integrate directly into pre-launch or continuous-evaluation workflows.
Our proven methodology ensures comprehensive safety coverage at every stage
Red-team testing identifies vulnerabilities and attack vectors specific to your AI system. We map your risk surface comprehensively.
Benchmark performance against child safety standards using our evaluation datasets. Get measurable, quantifiable safety metrics.
Fine-tune and improve your models using our synthetic adversarial datasets. Deploy with confidence and documented compliance.
Expert perspectives on AI safety, child protection technologies, and the latest developments in adversarial testing and model security.
How generative AI has transformed the child safety threat landscape and why proactive red-teaming is essential for AI companies to identify vulnerabilities before deployment.
UK government creates legal pathways for AI child safety red-teaming, establishing new standards for testing AI systems against child exploitation risks.
Essential insights for AI developers on how generative AI enables synthetic CSAM creation and why traditional safety defenses are inadequate for protecting children.
Experts and decision-makers from leading tech companies recognize the critical importance of child safety testing in AI
After reviewing CAIROS AI's approach to child safety testing, it's clear this addresses a critical gap in the industry. Synthetic adversarial datasets are exactly what we need to test AI systems safely and responsibly.
The concept of specialized red-teaming for child safety is brilliant. CAIROS is tackling one of the most challenging problems in AI safety with a thoughtful, comprehensive solution.
What impressed me most about CAIROS is their understanding that you cannot test child safety systems with actual harmful content. Their synthetic approach is both ethical and effective.
Child safety in AI is no longer optional—it's a regulatory requirement. CAIROS AI is building the infrastructure the entire industry needs to meet compliance standards while protecting kids.
Find the most common questions about our approach to child safety in AI technologies.
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