Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your AI works perfectly during testing… but suddenly fails in production. Why? The problem usually isn’t the model — it’s the data. Synthetic data looks clean and structured. But real-world data is messy: typos, missing values, broken formats, and unexpected edge cases. When AI models train only on synthetic datasets, they never learn how to handle real-world complexity. In this video, we explain why synthetic data can break AI systems and how using real production data safely can make AI more reliable.

In the second match of our Vibe Coding Challenge series, we put two powerhouse AI platforms to the ultimate test: Vercel’s v0 and Base 44. We gave both platforms the exact same prompt: build a DIY Home Repair community forum.

As AI coding assistants accelerate software development, the volume of pull requests at Datadog has grown to nearly 10,000 per week, increasing the risk that malicious changes slip through due to review fatigue. To address this, Datadog built BewAIre, an LLM-powered code review system designed to identify malicious source code changes introduced by threat actors. By reducing approval fatigue for developers while increasing friction for attackers, BewAIre guides human reviewers to the areas where judgment matters most, without slowing developer velocity.