Charting a Bold Path: GRC Principles for AI and ML

Many core GRC principles were established years ago, rooted in financial controls and risk management. In the age of generative AI and large language models, we must take bold steps to evolve GRC to enable rapid business uptake of AI, without exceeding the organizational risk appetite.

First, we'll examine ways that AI and ML challenge traditional GRC approaches. These include non-transparency of model logic, potential for training data leakage, and particular risks associated with cloud LLMs.

We'll then explore how GRC can move forward to tackle these unique challenges. This requires a strategic combination of classic GRC fundamentals like establishing system ownership, while embracing new techniques such as MLOps and next-generation testing techniques to ensure your AI workflows are both performant and secure.