Why quantifying the value of AI Is critical

The way organizations have responded to the evolving threat landscape has been a bit of a mixed bag. On the one hand, it’s good that more than two-thirds of risk teams say they have seen increased GRC funding over the past three years — but on the other hand, many say they still struggle to demonstrate the ROI needed to justify continued investment in their efforts. While executives broadly understand the need to protect against data breaches, threats are now emerging more quickly than ever. Unfortunately, GRC efforts are notoriously difficult to quantify, and it can be challenging to provide hard numbers to illustrate why additional resources are needed.

This is an area where AI is already playing a significant role. While it’s true that AI tools will play an important part in helping security and risk teams face AI-based threats, it will also help businesses grow in ways you might not expect. One of the most important roles for AI in the GRC space is in turning risk management from a qualitative exercise into a quantitative, metrics-backed growth strategy — one that helps to contextualize risk across the entire enterprise. By leveraging GRC in a more holistic manner, risk teams will be able to clearly demonstrate the value of good risk management to key decision-makers, helping to justify the additional investment needed to successfully navigate the rapidly evolving risk landscape.

AI’s impact on GRC is felt throughout the business

One of the most important and quantifiable ways that AI impacts risk management is through the improvement of resource efficiency. GRC teams have wide-ranging responsibilities, and as a result can become bogged down in repetitive and time-consuming tasks like collecting and classifying evidence, updating controls, or sending email reminders. These tasks can all be automated to one degree or another with modern AI-based GRC solutions, allowing risk professionals to operate at the speed and scale necessary to meet the challenge of modern AI-based threats. By freeing human employees to focus on more impactful and engaging tasks, these AI tools are having a significant impact on risk operations. Quantifying that impact is as simple as estimating the number of hours spent on repetitive tasks and multiplying it by the employee’s hourly rate. Across an entire organization, that adds up quickly.

AI also does a great job of creating new opportunities for revenue and market access, thanks to the greater transparency and data-driven governance it enables. AI is being used to improve modern GRC platforms with features like cross-mapping, automated evidence testing, and enhanced data analysis, reducing the risk of regulatory or compliance violations by evaluating alignment on a continuous basis. By making it easier to meet audit requirements and comply with compliance frameworks, these AI capabilities are allowing businesses to enter new markets and unblock deals that may be stalled in the pipeline. Quantifying the revenue generated from signing new contracts (or shortening sales timelines) and entering new markets should be relatively simple and can provide a valuable data point for further investment in AI-backed GRC.

Finally, AI can help reduce risk exposure by balancing innovation and growth with calculated oversight and control. It’s difficult to quantify something that didn’t happen: for example, if risk management efforts successfully prevented a data breach, how can that result be measured? This problem can be addressed by proactively identifying key risks (ranging from data breaches and insider threats to regulatory penalties or operational downtime) and assigning a structure to them. By estimating the potential impact of each risk, you can track how that exposure changes over time as controls and processes are improved. Annual studies on the cost of an average security incident or data breach can help attach a dollar value to certain variables.

Estimating the impact of a data breach under current conditions also allows you to understand how that risk would evolve based on changes like the addition of a new security control. This enables you to more clearly gauge how your risk metrics respond to changing conditions, and tracking those metrics over time makes it easier to draw clear trend lines that illustrate how GRC efforts have impacted the organization and its bottom line. Modern AI capabilities are making that process easier than ever, allowing organizations to analyze and correlate significantly more risk data and track a wider range of variables. It’s worth noting that AI-powered security tools can also respond more effectively to the scope and sophistication of AI-based attacks, and capturing and quantifying those metrics is critical.

AI is a risk management ally

When it comes to managing AI risk, GRC teams have no greater ally than AI itself. The technology is driving new efficiencies across the risk management space, improving resource efficiency, enabling new sources of revenue, and ensuring that security teams are equipped to successfully navigate the modern threat landscape. Today’s AI-based GRC tools are doing more than just reducing risk. They’re allowing risk management teams to provide business leaders with the information they need to make contextual, risk-aware decisions that drive the enterprise forward in a meaningful way. In a risk landscape dominated by AI, the ability to fight fire with fire is increasingly critical.