GRC is Changing. Traditional Risk Frameworks Are Starting to Fall Short

For a long time, GRC followed a relatively stable and predictable pattern, where risks could be identified, controls applied, and everything documented and reviewed on a consistent cycle, with frameworks like ISO 27001 or SOC 2 providing a structure that, in most cases, worked exactly as intended. 

The issue is that the environment those frameworks were designed for is starting to change. 

AI systems don’t behave like traditional software. They’re sensitive to context and change over time. Their outputs are difficult to explain and can often produce a level of variability that traditional approaches can’t really account for. 

As a result, some of the assumptions GRC has historically relied on, particularly around predictability and accountability, start to become less reliable once AI is in the mix. 

Most GRC frameworks are built on the idea that systems behave in a consistent and controlled way once they’re deployed, with changes occurring deliberately and within defined boundaries, which makes it possible to ensure everything works as expected. 

This model aligns well with frameworks like ISO 27001 and SOC 2, where stability and repeatability are central to how risk is managed over time, and where the relationship between system, risk, and controls stays relatively unchanged. 

For years, this has been an effective way to manage risk, particularly in environments where systems behave in a largely deterministic way and where changes are easy to track.

AI systems operate under a different set of assumptions, where behaviour is not always fixed, and models can evolve over time; either through retraining or through subtle shifts in the data they interact with. 

This means that the same system may not behave the same way over time, even if nothing has visibly changed from a deployment or configuration standpoint, which makes it harder to treat risk as something that can be fully captured at a single point in time. 

That difference has a knock-on effect across governance. 

Risk registers, for example, are typically designed as snapshots, but when risk itself is capable of evolving, static documentation becomes less reliable as a complete picture of the risk landscape. 

Controls are also affected, since many are designed with the assumption that system behaviour remains stable, and when that assumption no longer holds, controls may continue to exist without necessarily addressing the risks they were intended to manage. 

Even accountability becomes less straightforward, particularly when AI systems are introduced through third-party tools, where responsibility is often distributed across multiple parties and where decision-making is influenced by systems that are not always fully transparent. 

None of this means that traditional GRC frameworks are no longer useful, but it does mean they’re being applied to a system they were not originally designed for.

The important point here is that this is not a question of starting over, because the core discipline remains very similar. 

Risk identification, control design, audit thinking, and structured documentation all still apply, and in many ways are exactly what is needed to bring clarity to AI-related risk. 

What changes is the context in which those skills are being applied. 

AI introduces considerations that don’t always fit neatly into existing governance approaches. This can be anything from understanding how models are developed and updated, to managing risks introduced by third-party AI systems. As AI systems evolve, so must the way they’re governed. 

This isn’t an entirely new idea, but it does require a change in perspective. AI risks don’t always present themselves in ways that traditional frameworks are designed to capture. 

Traditional GRC being replaced, but it is evolving. As the systems it is responsible for governing become more complex and less predictable, the way governance works must change to accommodate these new risks. 

As AI becomes more common across business processes, governance needs to adapt accordingly by building on existing frameworks in a way that accounts for how these systems actually behave in practice. 

For those already working in GRC, this represents an opportunity to apply an existing skill set in a more complex and evolving environment, where understanding the limits of traditional assumptions, and knowing how to adjust for them, becomes increasingly valuable. 

Developing that understanding is what allows governance to move beyond being structurally sound on paper, and toward being genuinely effective in practice. 

If you’re looking to progress your career in GRC, check out our AI GRC course catalogue.