What Happens to Frameworks in an AI-Powered GRC World?

Before we get started with this week's article. Join me at my Webinar Tomorrow at 10am PST. Nick and I will discuss five (or maybe more) AI use cases in GRC, how to attack some of them with free products, and how vertical-specific solutions like Trustero are progressing against the advanced use case.

By George Totev, Chief Information Security Officer

Governance, risk, and compliance (GRC) professionals have long relied on control frameworks like SOC 2, ISO 27001, and NIST 800.53 to guide their strategies. These frameworks provide a structured approach to managing risk, offering a common language and baseline for compliance. But as AI becomes more integrated into GRC processes, the role of frameworks is poised to evolve dramatically.

So, what happens to frameworks in an AI-powered GRC world?

1. Frameworks Are a Starting Point, Not the Endpoint

Traditionally, achieving compliance with a control framework was seen as the evidence that a particular risk area is properly managed. You are concerned about credit card risk - you implement PCI DSS; you are concerned about general cyber risk - you go with ISO27001. Control frameworks are the result of efforts and experience of many smart people, who wanted to help us manage risk through standardization. Without frameworks we have to perform a deep risk analysis, decide what we need to manage, how to manage it, and design the necessary controls. In fact, we are so used to control frameworks “shortcut” that any time there is a new regulation or risk we anxiously await for the corresponding control framework to be announced. It is easier to show compliance with the regulation and it is easier for the auditors to verify that compliance.

Since the control frameworks are designed to cover as many use cases as possible, there is always the applicability/scope analysis - which controls are really applicable to our specific use case. On the other hand, we tend to “extend” the standard baseline with additional, custom controls that may be outside the scope of the framework but they are relevant to our particular environment. For example, SOC2 allows for such extension and SOC2+ is commonly used for evidencing HIPAA compliance.

In other words, we start with a “one size fits all” framework (or a combination of them) and evolve into a more tailored one that fits our needs. We often call it “Common Control Framework”, or CCF. Adobe was one of the pioneers in that approach - having a tailored internal CCF that maps to standard control frameworks for external consumption.?

While CCF helps in reducing complexity, it does come with its own challenges. The standards constantly evolve and we need to constantly re-map. The environment - internal and external - also evolves and we need to make sure that the control set is still adequate. Furthermore, do we have the optimal control set? “Controls rationalization” is one of the favorite topics for many Internal Auditors.

Unfortunately, keeping up with the CCF requires not only a lot of effort but also effort from specialized SMEs - specialized in the particular risk area and with deep understanding of our environment.?

2. AI role

This is where AI can help. Its ability to contextualize vast amounts of data, both internal and external, help us increase the efficiency of the process. Also, it gives us confidence that we don’t miss an important detail that we were not aware of.

goal. In an AI-powered world, frameworks serve as a foundation—useful for defining basic requirements but not sufficient for addressing the unique risks of a modern organization.

AI enables GRC teams to move beyond one-size-fits-all frameworks by tailoring controls and policies to the specific needs of the business. With AI’s ability to process vast amounts of data and context, compliance becomes not just about meeting standards but optimizing for actual risk mitigation.

With AI assistance mapping between different control frameworks is accelerated dramatically leading to much faster “What If?” analyses. During our planning process we are often faced with a familiar prioritization problem - do we focus on a framework that is “closer” to our current state with smaller immediate revenue potential; or we start with a more difficult one but with a higher potential. The optimal point is somewhere in between and the ability to accelerate the analysis will allow us to be much more “agile”. Not to mention the satisfaction of having a quick, rational answer to a sales person who is asking us “When are we going to have X?”

3. Frameworks Will Evolve Faster

One of the biggest challenges with frameworks is their lag time. That is manifested in two dimensions:?

• Lag behind the regulation. It usually takes years before we have a control framework to point to and claim compliance with the law. GDPR is the perfect example. Absent a control framework we have to perform a risk analysis, borrow from other frameworks and come up with our own controls.
• Lag behind a changing environment. By definition, most frameworks are reactive. More common frameworks undergo a refresh every 5-7 years. Given the speed of technology development this is not sufficient. By the time a framework is updated to address new risks, the landscape may already have shifted.

One of the main reasons for this lag is the vast amount of information that needs to be considered and analyzed. In an AI-powered GRC world this lag could be significantly shortened so frameworks could evolve in near real time. AI could help us analyze global trends, identify emerging risks, and provide rapid updates to existing frameworks. Imagine ISO or NIST using AI to generate recommendations and publish updates months—or even years—faster than before.

This will inevitably have an impact on the organization. Imagine a scenario where ISO27001 revisions are issued several times per year. While the standard will be “up to date” how will an organization be able to follow? Unlikely, without an AI assisted GRC. “Agile Compliance” could have a profound impact on how we think about risk management.

4. Personalized Frameworks for Every Organization

Risk is inherently contextual, and AI excels at customization. In the future, frameworks might be dynamically adapted for each organization based on its industry, size, geography, and unique risk profile. AI assisted GRC could be contextualized with hundreds of frameworks and select controls that are applicable to the specific use case.

For example, instead of adopting a generic SOC2 standard, removing non-applicable controls and adding specific new ones, an AI-powered GRC system, using its vast control database and understanding of the environment, could create a personalized CCF that aligns with your business model, customer expectations, and operational complexities. And keep it up to date.

5. From Frameworks to Continuous Compliance

Frameworks have historically guided periodic audits and certifications, offering a snapshot of compliance. More advanced programs (like FedRAMP) require (almost) continuous monitoring and many organizations see the benefits of that approach - issues are discovered earlier and dealt with accordingly, without having to rush in the very last moment, just before the audit starts. Or, worse, finding out when the audit is underway.?

Unfortunately, such continuous monitoring is very resource intensive and, as much as it is good risk management practice, it is difficult to implement and maintain. With AI assistance we could reduce the efforts necessary and shift the paradigm by enabling continuous monitoring and compliance. When we combine the continuously updating CCF with continuous monitoring we can maintain an ongoing view of our risk posture, automatically aligning our practices with multiple frameworks simultaneously. This eliminates the need for the start-stop cycle of audits, replacing it with real-time assurance.

6. Frameworks as a Foundation for AI Collaboration

AI-powered GRC doesn’t eliminate the need for frameworks; it makes them more valuable. Frameworks provide the structure and common language that AI tools use to interpret, map, and enhance compliance processes.

In fact, frameworks may become the basis for AI-driven collaboration. Imagine a world where AI generates compliance reports, answers vendor questionnaires, and evaluates new regulations—all while referencing the frameworks your organization adheres to.

The New Role of Frameworks

In an AI-powered GRC world control framework's role will change. They’ll shift from being static, standalone tools to dynamic, integrated elements of a broader risk management strategy.

AI doesn’t replace frameworks (yet)—it enhances and evolves them, enabling organizations to adapt to the complexities of a rapidly changing regulatory landscape. At Trustero, we’re at the forefront of this transformation, combining AI’s power with the proven structure of frameworks to help organizations achieve compliance and security in a smarter, faster, and more sustainable way.

Frameworks will still guide us, but with AI, they’ll guide us further and faster than ever before.