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.

AI skills are no longer optional. The US Department of Labor recently released an AI Literacy Framework, making AI knowledge a basic workforce skill for the future. This means every worker should understand: Basic AI principles AI use cases Prompting AI correctly Evaluating AI outputs Using AI responsibly AI literacy is quickly becoming a core job skill across all industries, not just tech.

AI tools like ChatGPT, Gemini and other LLMs are powerful — but what happens when sensitive data gets sent to them? In this video, we demonstrate how Protecto AI prevents sensitive information from reaching LLMs using Masking APIs and Unmasking APIs. You’ll see a real workflow where user prompts containing credit card details and personal data are automatically masked before being processed by an AI model like Gemini 2.5 Flash.

How are governments using AI while protecting citizens’ data and privacy? In this episode of AI on the Edge, Ciara Maerowitz, Chief Privacy Officer for the City of Phoenix, explains how cities implement AI governance, manage bias, ensure transparency, and assess AI risks. Learn how responsible AI frameworks, policies, and risk management help governments safely adopt artificial intelligence.