From Data to Decisions: Closing the AI Context Gap in GRC

Introduction

“Why did this risk increase?” Yet, for many GRC teams, answering it can mean hours of searching, comparing and reconstructing information.

It’s not because the information isn't there. It usually is, as the assessment, controls, findings, incidents, and remediation history are all there. But the executive isn't asking for more data. They're asking for the context: what changed, why it changed, what was decided, and what deserves attention now. 

That context is often scattered across multiple records, making even simple GRC questions surprisingly time-consuming to answer.

The same challenge comes up during external audits. In a recent conversation, the Head of Audit at a financial institution described how an auditor might select a specific scope and a specific sample and ask the team to provide the complete context around it.

The relevant information may span across audits, assessments, controls, evidence, findings, remediation actions, and previous decisions. The challenge is connecting it quickly enough to provide a complete, defensible picture.

This isn't just anecdotal. PwC's 2025 Global Compliance Survey, based on responses from 1,802 executives across 63 territories, found that 63% of respondents said the complexity and disaggregated nature of data across their organization make compliance more difficult. Data reliability and quality were also identified as a challenge by 56% of respondents. The survey also found that 59% reported greater confidence in compliance decision-making through better coordination.

The problem is the gap between having the data and being able to understand the story it tells, when it matters. Organizations don't necessarily have a shortage of GRC data, but a shortage of connected context around that data. And when that question comes from an executive, auditor, or regulator, the cost of that gap can result in delayed decisions, slower responses, and less confidence in the answer being given.

So how do we make the context behind our data available when a decision needs to be made? AI can find the data. But can it explain the decision and show us why that explanation should be trusted?

This is where the conversation around AI in GRC needs to go a little deeper. Most GRC platforms already contain a lot of the information AI would need: risks, controls, assessments, incidents, findings, actions, evidence, and historical records. AI can connect those dots. But connecting the dots is not the same as understanding the decision, and it is certainly not the same as proving that the connections are valid. Consider the questions senior leaders and business teams actually ask:

• Why is this risk still high? 
• What's different from last year? 
• Why wasn't this issue fixed earlier? 
• Why did we accept this risk? 
• What have we already done about it?

All of the above questions have one thing in common. They are requests to understand what happened, why and what was decided. That is the GRC Context Gap.

The Data Doesn't Tell the Whole Story

GRC has come a long way. We've moved from spreadsheets to workflows, from workflows to centralized platforms, and from platforms to dashboards and analytics. We've become much better at capturing and reporting information. However, individual data points don't tell the whole story.

Consider a third-party cyber risk moving from Medium to High. A dashboard might simply tell you: 

Current Risk Rating: High | Previous Risk Rating: Medium

While this is useful, the business leader really wants to know answers to questions such as:

• Why key controls have deteriorated 
• Why remediation actions are overdue 
• Why did a related incident occur during the same period 
• Why was a previous risk acceptance tied to a remediation plan that is still incomplete

With answers to the above questions a leader has more than a risk score. They have context. They can see what shifted, what contributed to the change, what was decided previously, and where attention may be needed now.

That's the difference between finding information and understanding it.

What’s Missing? The Context Behind the Decision

What if, instead of manually searching across multiple records, a GRC platform could bring the relevant context together the moment the question is asked?

Moving beyond “What is the risk?” to answer:

• Why is it High? 
• What changed? 
• What evidence supports that conclusion? 
• What happened previously? 
• What decisions have already been made? 
• What deserves attention now?

This is where I see the value of a context trail. It isn't another place to search. It's about keeping the decision, the evidence, the rationale and what happened afterwards connected, so you can see where things stand today.

The person still owns the decision. The system should keep the supporting context connected.

This isn't just good practice. It also aligns with an important principle emerging in AI regulation. Article 14 of the EU AI Act requires effective human oversight for high-risk AI systems, including enabling people to understand the system's capabilities and limitations, monitor its output and avoid simply deferring to what AI says. The principle is the same one this piece is built on: AI should support human judgment, not replace it.

AI can connect the dots, but only when the organization has captured the decision, evidence, and rationale in the first place.

Think about the compliance analyst who inherits a high-rated vendor risk after the colleague who owned it has left, or the auditor two years later trying to reconstruct why a control exception was approved. Organizations shouldn't rely on memory to explain past decisions. They should be able to retrieve what was already recorded and connected.

And for the person doing the work, this isn't about creating more documentation for the sake of it. The value is in capturing the rationale once, at the point of decision, so that the same work doesn't have to be reconstructed later.

Where I See AI Making a Difference in GRC

This is where AI can make a meaningful difference in GRC.

Summarizing more reports isn't the real opportunity. I think the bigger opportunity is using AI to investigate what happened and why.

“Why has this risk remained High for three years?”

Instead of someone manually searching across multiple reports and records, AI could bring together the risk history, changes in assessments, deteriorating controls, related incidents and findings, overdue actions, supporting evidence, previous decisions, and relevant comments.

The result is more than a summary. It's an explanation.

And in GRC, that distinction matters.

A wrong answer can influence a risk acceptance decision or a board discussion.

And the risk isn't only that AI gets a fact wrong. It could also connect the right facts in the wrong way, producing a convincing explanation that isn't actually supported by the evidence.

That's why AI in GRC has to meet a higher standard.

AI should support the investigation, surface the evidence, and make decisions easier to explain. The answer needs to be evidence-backed, traceable, grounded in the right context, and respectful of permissions. And the person making the decision needs to be able to question, challenge, or override the AI's conclusion.

Technology supports the investigation. The business leader owns the decision.

And that brings us back to the question that matters.

How quickly and confidently can your organization explain:

• What happened? 
• Why did it happen? 
• What was decided? 
• What deserves attention now?

The Future of GRC Is Clarity

The next generation of GRC should not simply tell us what happened. It should help us understand why it happened, what was decided, what changed, and what needs to happen next.

Because organizations don't just have a data problem. They have a context problem. And today, too much of that context is still reconstructed manually by people who know where to look.

The opportunity is to make that context available when a decision needs to be made, so people spend less time searching, comparing, and reconstructing, and more time understanding, acting, and deciding.

For me, this is where I see GRC evolving, from capturing data, to connecting information, to helping people understand the context behind it.

For organizations evaluating where their GRC platform needs to evolve, the question should move beyond asking “How much data do we have?” to “How quickly can we turn that data into a defensible explanation and a confident decision with AI?”

And the answer shouldn't take hours.

A Question Worth Asking Your Organization

If an executive asked tomorrow, “Why did this risk increase?”, could your organization explain the answer immediately, or would someone still need to spend hours putting the story together?

If this challenge feels familiar, I'd be interested to hear how other teams are tackling it and whether AI is helping close that gap.