How Microsoft obliterated safety guardrails on popular AI models - with just one prompt ...

Corrupted training data is silently undermining AI investments, leading to inaccurate recommendations that waste resources ...

It only takes 250 bad files to wreck an AI model, and now anyone can do it. To stay safe, you need to treat your data pipeline like a high-security zone.

AI is rapidly expanding in construction safety, but poor implementation can create alert fatigue, mistrust, and a false sense of security. Workforce-centered deployment is critical for real risk ...

The debate should no longer be whether or not to use AI, but rather how to deploy it without losing perspective and control ...

In the SDLC, there should be no shortcuts. Developers must view AI as a collaborator to be monitored, rather than an autonomous entity to be unleashed.

Google finds nation-state hackers abusing Gemini AI for target profiling, phishing kits, malware staging, and model ...

"An AI system can be technically safe yet deeply untrustworthy. This distinction matters because satisfying benchmarks is necessary but insufficient for trust." ...

Human validation is what prevents bias, filters false alarms and ensures technology remains accountable to the people it ...

As LLMs and diffusion models power more applications, their safety alignment becomes critical. Our research shows that even minimal downstream fine‑tuning can weaken safeguards, raising a key question ...

Overview: AI automation is rapidly reshaping white-collar roles, prompting professionals to rethink traditional career ...

Read more about Bias, safety, and accountability gaps persist in deployed healthcare AI systems on Devdiscourse ...