AI in GRC: Takeover or Gamechanger?

We had a great conversation with Andrew Gunter and Jason Rohlf from Cential about how generative AI and large language models (LLMs) are poised to transform the way we work in GRC. Dubbed the “fourth Industrial Revolution” by the World Economic Forum, we are on the precipice of a new age marked by significant advancements. Predictions for 2025 include 30% of corporate audits being performed by AI and the integration of AI into corporate boards.

These developments have the potential to revolutionize governance, risk, compliance, and industry practices. While AI won’t replace human involvement entirely, it can automate tasks— like control testing, data analysis, and report generation—much to the relief of small, overworked teams. The future promises exciting possibilities. But what should you know now to prepare and protect your organization?

Is AI going to take over GRC?

Applying artificial intelligence to governance, risk, and compliance (GRC) can yield significant efficiency gains for teams looking to expedite results. Like the chain theory of automation suggests, automating repetitive functions can strengthen and expand processes by reducing reliance on tedious labor. But supporting technologies, like AI, serve as components, not necessarily replacements within this chain.

AI is not expected to replace all existing processes in your program but rather become an additional resource within the ecosystem of integrated risk management. Just like a tree relies on various sources of energy and nutrients, risk management relies on various components, including people, processes, and technologies. Proper utilization of AI can enhance and strengthen the work people do within this ecosystem.

It’s also important to note that not all aspects of risk and compliance will require the same AI solutions.

Where will AI start in GRC?

When someone mentions AI, most people automatically think ChatGPT. But as AI pundit Cobis Greyling reports, there’s a vast and expanding field of AI technologies, including various applications of large language models. These applications range from writing assistance and content creation to data-centric tooling. OpenAI’s models and hubs, including ChatGPT, are part of this broader AI landscape.

You want to choose the right AI tool for the right task. While ChatGPT excels in generating content based on prompts, it may not be suitable for data analytics. To determine when to apply ChatGPT or similar language models, try the “blank page challenge.” ChatGPT shines in completing content based on short prompts, resembling an advanced auto-complete feature. In the GRC space, you can use targeted prompt approaches to leverage ChatGPT in generating content.

Ready for some examples of AI use cases in GRC? Read the continuation of our conversation with Cential in “How to Use AI and LLMs in GRC Effectively and Securely.”

If you’re interested in learning how Onspring can help you track risk and manage your compliance, reach out to us at hello@onspring.com.