Data Privacy

While generative AI is widely accessible and useful, businesses need to know when to use other AI tools, like traditional machine learning.

From risk management policies to the five stages of AI ethics, here’s how some organizations approach ethical AI.

Companies need a plan for when employees use unapproved, publicly accessible generative artificial intelligence tools for work-related tasks.

Using the wrong datasets to train AI models can result in legal risks, bias, or lower-quality models. The Data Provenance Initiative’s tool can help.

Security leaders must strengthen their defenses against everyday threats while preparing for potentially devastating attacks. Here’s how to get it done.

Businesses have identified two types of generative AI: broadly applicable tools that boost personal productivity, and tailored solutions for specific purposes.

The AI Risk Repository, a database of over 700 risks posed by AI, aims to provide a shared framework for monitoring and maintaining AI risk oversight.

Company leaders need to be on top of best practices and legal requirements for data protection, including mandatory incident reporting and bans on ransomware payments.

Cloud misconfigurations, more sophisticated ransomware, and exploitation of vendors are contributing to rising cyberattacks.

A “red light, yellow light, green light” framework can help companies streamline AI governance and decision-making.