One platform for AI you can trust. Holistic AI is an AI Governance platform that empowers enterprises to adopt and scale AI confidently.

Artificial intelligence is evolving rapidly, from tools we use directly to intelligent systems that act on our behalf. This shift has ushered in the rise of autonomous AI agents: software entities ...

The harmful and benign prompts were sourced from a Cornell University dataset designed to rigorously test AI security, drawing from established red-teaming methodologies. While not a reasoning-based ...

As part of our ongoing commitment to AI safety and reliability, Holistic AI conducted a preliminary audit of xAI's latest model, Grok-3. This evaluation offers critical insights into Grok-3's ...

At Holistic AI, we built AI governance from the ground up because it is not simply an extension of existing data or cloud governance platforms. I’ll explain this more in a future blog, but essentially ...

As artificial intelligence continues to integrate into our daily lives, it is paramount to ensure new and popular models remain safe and reliable. At Holistic AI, we are committed to rigorous testing ...

Large Language Models (LLMs) have become a central focus in AI research, demonstrating strong capabilities in processing and generating complex language. However, their practical use often requires ...

In the age of big data, machine learning (ML) has become a cornerstone of countless industries that are being applied in a wide range of applications. With the ability to process massive datasets and ...

AI is transforming every business function, and enterprises must quickly embrace and operationalise AI to stay competitive. Traditional governance tools were not designed to manage risk of AI systems ...

This research presents a novel multimodal data fusion methodology for pain behavior recognition, integrating statistical correlation analysis with human-centered insights. Our approach introduces two ...

The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and lack of a fairness ...