As cited
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ai security
How Tenable brought multi-language support to security detectors with agentic translation
Tenable built a multilingual threat detection system for its artificial intelligence (AI) security tool by creating an agentic translation pipeline that generates high-fidelity training data across nine languages, then fine-tuned a compact pretrained model to detect threats natively without relying on a translation model in production. The new system processes detections roughly 20-800 times faster per item, costs under $10,000 as a one-time expense, and maintains or improves detection accuracy across all supported languages while reducing memory footprint by 12-14 times.
Why it matters: Organizations using AI systems in non-English markets face exposure to prompt attacks and jailbreaks in languages their English-only detectors cannot catch; practitioners should adopt multilingual detection to close this gap without sacrificing speed or adding per-request translation costs.
- Source published
- First seen by Cybersecurity Tracker