Why Traditional GRC Tools Fail and How AI Is Fixing Them

Have you ever wondered why companies still get blinded by risks even though they’ve invested in Governance, Risk, and Compliance (GRC) tools?

It’s because most of those tools were designed for a slower world, where compliance updates trickled in and risks didn’t evolve overnight. In fact, half of GRC professionals admit they can’t keep up with compliance changes.

That means a huge number of businesses are probably out of step with what regulators expect, without even realizing it. And this is exactly where AI is changing the game.

But first, let’s discuss why traditional GRC tools fail.

Why Traditional GRC Tools Fail: Key Weaknesses

Traditional GRC platforms were built for a different era, one where risk and compliance moved at a slower pace. Today, businesses face more complex threats, faster regulatory updates, and growing operational demands. Yet many organizations still rely on outdated tools that can’t keep up. Let’s break down the biggest gaps these systems create and why they matter.

1. Data Silos and Disconnected Systems

One of the most common challenges with older GRC tools is that they keep data locked away in separate locations. Risk information might be stored in a spreadsheet, compliance updates in emails, and audit results on another platform. Despite their own innovation efforts to be more streamlined to offer data via API integrations and automation, many have layered those capabilities on to their architectures that are still oriented around manual compliance workflows.

When teams need a full picture, they’re forced to stitch everything together manually. This fragmented view makes it easy for risks to slip through unnoticed and slows down critical decision-making.

2. Manual Workflows and Slow Response Times

In addition to disconnected systems, most legacy GRC tools rely heavily on manual work. Instead of automated alerts or real-time dashboards, teams spend hours updating documents, checking compliance lists, and chasing approvals.

When first built, GRC platforms were primarily designed to support point-in-time compliance by providing controls to auditors during periodic review. They relied heavily on manual data collection such as evidence uploads and risk register entries, and served as document repositories to centralize policies, audits, and risk records.

These repetitive tasks leave little time for strategic risk management. Even worse, they slow down responses during urgent situations, when speed matters most.

3. Inability to Keep Up with Regulatory Change and Compliance Drift

The regulatory environment is changing faster than ever, but traditional GRC tools weren’t designed to keep pace. New laws like NIS2, DORA, SEC cyber disclosure rules, state privacy laws require faster reporting and more granular evidence.

Traditional GRCs lack the agility and integrations to adapt quickly without long customization cycles. The growing need to connect with vulnerability management or continuous threat exposure management tools, essential for addressing IT and cybersecurity compliance in new regulations, only adds further friction.

Without automation, businesses fall into compliance drift, where controls look fine on paper but are no longer aligned with new rules. This gap often stays hidden until regulators step in, exposing the company to penalties and reputational damage.

4. Poor Scalability and Adaptability as the Business Grows

As organizations expand, the cracks in outdated systems become even more apparent. For instance, a GRC tool that worked well for a team of 50 employees managing a few compliance regulations can quickly become a burden as the company grows to 500 employees and faces new regulations every year. Which brings another issue, that many GRC platforms charge by framework or compliance requirement which causes the cost for GRC to balloon. And when specialized departments need specific requirements, such as IT or cybersecurity, the workarounds may lead to more hassle than convenience in helping to meet IT and cybersecurity compliance.

Instead of scaling smoothly, the system grows more complex, slows down workflows, and creates bottlenecks across departments. Instead of enabling growth, it becomes an obstacle to it.

5. Limited Analytics, Visibility, and Proactive Risk Detection

The biggest weakness is the tools' inherent reactiveness. They tend to highlight issues only after they’ve occurred, offering reports that are more historical than forward-looking.

Without predictive analytics or holistic visibility, leadership can’t anticipate threats or spot emerging patterns. This leaves organizations stuck in a cycle of catching up instead of staying ahead.

How AI and Modern Tools Address These Failures

Image by Exploding Topics.

Recent data shows that over 60% of organizations are planning to add AI into their business operations, such as compliance, in the next year. This means AI will become one of the most important supportive agents for GRC tools, and here’s how they’ll be solving the biggest weaknesses:

1. Real-Time Data and Automated Compliance Alerts

Modern platforms give you live dashboards so you can see compliance status at any moment, not just after monthly or quarterly reviews. This shift matters because 85% of executives now expect AI to deliver compliance benefits.

That means real-time oversight is what leaders believe will actually drive compliance success. This is where GRC tools, together with AI, continuously pull data from all your systems to deliver key advantages such as automating evidence collection, performing initial compliance reviews to produce gap analyses or recommendations, and accommodating the growing number of data sources required for compliance. These include Cloud, SaaS, IoT, OT, third parties, DevOps pipelines, geopolitical risk, fraud, and AML requirements. When something deviates, you and appropriate team members receive immediate alerts.

2. Predictive Analytics and Risk Forecasting

Rather than just looking back at what went wrong, AI helps you forecast where things might go wrong. In the latest IMCT Survey, 46% of organizations reported an increase in compliance testing related to AI and predictive analytics.

Using historical incident data, patterns of non-compliance, and external risk signals, these tools estimate exposure and identify where risks may escalate. That lets you act ahead of time and fix issues before they become crises.

3. Integrated Workflows and Breaking Down Silos

AI-powered GRC systems connect various parts of your company, including risk, audit, operations, and compliance, under one roof. Instead of each department working in isolation, data flows between them.

That way, compliance teams see operational issues and operations see compliance rules. This shared view cuts down on duplicated effort, missed risks, and delays caused by waiting on other teams.

Solutions like the FortifyData Cyber GRC platform are often cited in research as examples of technologies designed to bridge these silos. The concept itself, however, applies broadly across modern GRC tools.

4. Flexible and Adaptable Tools That Map to Changing Regulations

Regulations change often. There are new standards, new privacy laws, and updated industry guidelines. That’s why, if your system is rigid, you fall behind.

An interesting fact is that only 1.6% of firms have currently integrated AI into their compliance processes, although 32% are in the early stages of doing so.

The best part in all of this?

AI-based platforms are built to adapt. They update regulatory libraries, support multiple standards, and allow you to map controls to those standards automatically. So when a regulation shifts, your tooling shifts too.

5. Automation of Routine Tasks

There’s a lot of repetitive work in GRC, such as gathering evidence, filling out checklists, and following up on control tests.

AI eliminates this by automating data collection, running routine control tests, and even sending reminders for overdue tasks. This frees people up to focus on interpreting results and making strategic decisions instead of manual grind.

Steps for Transitioning to AI-Enhanced GRC

Now that you know why traditional GRC fails and how AI can make it better than before, let’s explore how you can transition to AI-enhanced GRC solutions.

1. Audit Your Current GRC Tools for Weak Spots

Start by mapping every process in your GRC system, including where data resides, who enters it, and where handoffs occur. Candidly, your team may already be using AI to help with their jobs. Interview your staff to understand the use cases they are getting benefit from in using AI to expedite the work they do.

Look for gaps, such as data silos, manual handoffs, and disconnected modules. That audit gives a clear blueprint of what needs fixing first. Leading organizations use this diagnostic step to pinpoint where AI can deliver the highest impact.

2. Spot Where AI Can Fit Best

Only 22% of organizations have defined AI strategies, indicating a significant gap in the market. However, research suggests that companies with an AI adoption plan in place are 3.5 times more likely to reap the benefits of AI.

And you wouldn’t want to miss out on them.

For that, match the weak spots you found and match them with areas where AI performs best. For example, if your monitoring process is slow, AI can help scan data continuously. If compliance alerts are delayed, AI can send real-time notifications.

3. Choose Tools That Adapt, Integrate and Address Security

When choosing tools, focus on three things: adaptability, integration, and security.

Adaptability means the tool can adjust to new regulations or standards without needing to rebuild your entire system. Integration means it should connect easily with your existing platforms, such as HR systems, ERP, or incident management software. For security, when evaluating an AI provider, companies should go beyond performance claims and carefully examine the security and assurance practices behind the technology. Key questions include how the provider secures training data, models, and outputs from unauthorized access or tampering; what controls are in place to prevent data leakage or misuse; how they manage vulnerabilities and third-party dependencies in the AI supply chain; and whether they undergo independent audits or attestations such as SOC 2, ISO 27001, or NIST 800-53. Just as important, organizations should ask how the provider ensures transparency, explainability, and governance of the AI system so risks can be understood, monitored, and managed over time.

Before buying, test how easily the tool pulls in data from your systems. Also, check whether it allows you to adjust controls and reporting without requiring expensive custom coding.

4. Train Teams, Clean Data, and Set AI Rules

Remember, AI won’t succeed without people who know how to use it. Plan training sessions so staff understand how the system works, what tasks AI automates, and how to review its outputs.

At the same time, improve your data quality by removing duplicates, correcting errors, and setting clear ownership for each dataset. Clean data ensures the AI makes accurate predictions. Finally, establish governance rules for AI use, such as:

• Determine who approves AI recommendations
• Define when human review is required
• Document how decisions are recorded for audit purposes

Featured Image by Freepik.