The Dirty Secret of Agentic AI in GRC

Key Takeaways

• Agentic AI Is an Architecture Problem, Not a Model Problem: The limiting factor for agentic AI in GRC is often not the intelligence of the model but the quality, structure, and connectedness of the underlying GRC environment.
• Context Is What Separates Assistance From Agency: While chatbots, copilots, and drafting tools can provide valuable support, true agents require a deep understanding of how obligations, policies, controls, assets, third parties, and business processes relate to one another.
• Most GRC Environments Remain Fragmented: Many organizations have the necessary information, but it exists in disconnected repositories, workflows, and taxonomies that prevent AI from reasoning effectively across the enterprise.
• Connected Data Models Are Becoming a Competitive Advantage: The vendors best positioned for agentic AI may not be those with the most impressive demonstrations, but those that have invested in architectures capable of representing enterprise relationships and dependencies.
• Agentic AI Is Forcing a Long-Overdue Conversation About Integration: Organizations should be asking how connected their data, processes, decisions, and understanding of the enterprise really are before expecting AI to deliver meaningful autonomous outcomes.

Deep Dive

Last week I argued that much of what is being marketed as agentic AI in GRC is not actually agentic. The market response was interesting because very few people challenged the core premise. Most practitioners already sense that something is off. They sit through the demonstrations and hear the language. They watch the AI summarize documents, answer questions, generate narratives, and produce recommendations. Then they leave wondering whether they just witnessed the future of GRC or a very polished presentation wrapped around capabilities that have existed in various forms for years.

The more uncomfortable question is not whether the market is exaggerating agentic AI. The more uncomfortable question is whether most GRC platforms are even capable of supporting it. That is where the conversation becomes much more interesting because agentic AI is not primarily a model problem. It is an architecture problem.

Much of the market is focused on the intelligence layer. Which large language model is being used? What reasoning capabilities does it have? How many tools can it access? How quickly can it generate responses? Those questions matter, but they are not the first questions buyers should be asking.

The first question should be much simpler. What exactly is the AI operating on? Because an agent can only be as intelligent as the environment it understands.

The Context Problem

The reality is that many GRC environments remain fragmented. Risks live in one area. Controls live somewhere else. Policies sit in another repository. Regulatory obligations may be managed separately. Third-party risk operates in its own universe. Audit maintains its own records. Compliance has its own workflows. Information security has its own tools. Operational resilience often exists as an entirely separate discipline with separate taxonomies and separate reporting structures.

Organizations may call this integrated GRC. An agent would call it a mess.

The challenge is not that the information does not exist. In most organizations it does. The challenge is that the information often lacks meaningful relationships. The system may know that a control exists. It may know that a policy exists. It may know that a regulatory obligation exists. What it frequently cannot do is understand how all of those elements connect to each other in a way that allows meaningful reasoning and action.

That distinction becomes critically important when organizations begin talking about agentic AI. Consider a seemingly simple objective. A new regulation is published, and the organization wants to understand the impact:

• A chatbot can summarize the regulation.
• A copilot can answer questions about the regulation.
• A document assistant can compare the regulation to existing policies.

All of that is useful. None of that is particularly difficult. A true agent faces a much more demanding challenge:

• Identify affected obligations.
• Understand which policies are connected to those obligations.
• Identify the controls that support those policies.
• Determine which business units own those controls.
• Understand which products, services, assets, and third parties may be affected.
• Identify evidence requirements.
• Recognize existing exceptions and open issues.
• Determine whether additional review is necessary.
• Know who should be involved, when escalation is required, and what actions are permitted under organizational policy.

That is not a language problem. That is a context problem, and context is where many GRC architectures begin to struggle.

Why Integration Suddenly Matters Again

The dirty secret of agentic AI is that most organizations do not need a smarter model nearly as much as they need a more connected operating model.

For years, the GRC market has talked about integration. Vendors have promised single views of risk, unified platforms, connected governance, and integrated management. Some have delivered impressive progress. Others have simply accumulated modules under a common logo and called it integration. The difference matters because agents depend on connected context in a way that traditional workflows never did.

Consider the distinction:

• A workflow can operate on a narrow slice of information. An agent cannot.
• A workflow can follow a predetermined path. An agent must understand why that path exists.
• A workflow can execute rules. An agent must understand objectives.
• A workflow follows instructions. An agent must evaluate context.
• A workflow completes tasks. An agent pursues outcomes.

That requires something many organizations still lack: a connected data model that reflects how the enterprise actually operates. This is one reason I continue to believe that knowledge graphs, relationship models, semantic layers, and connected enterprise architectures will become increasingly important in the next phase of GRC technology. The industry is spending enormous energy debating models while often ignoring the quality of the context being supplied to those models.

The Next Competitive Battleground

The result is predictable. Organizations become impressed by conversations that sound intelligent while overlooking the fact that the system lacks the underlying understanding required to support meaningful action.

The future leaders in agentic GRC may not be the vendors with the most impressive demonstrations. They may be the vendors that spent the last decade quietly building connected architectures capable of representing how objectives, risks, controls, obligations, policies, assets, third parties, incidents, issues, and business services relate to one another.

That work is considerably less exciting than a flashy AI demonstration. It is also considerably harder, and it may ultimately prove far more important. The irony is that agentic AI is forcing the GRC market to confront questions it should have been asking long before artificial intelligence entered the conversation:

• How connected is our data?
• How connected are our processes?
• How connected are our decisions?
• How connected is our understanding of the enterprise itself?

Those questions matter because agency does not emerge from language alone.

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