The recent advances in generative and conversational AI have disrupted the technology industry. Even as the technology is evolving, a report suggests that more than 50 percent of the top 1000 companies are starting to apply AI to larger organizational processes. As AI becomes more integral to businesses and economies, GRC (governance, risk and compliance) practices will take a quantum leap forward. Here’s an overview of how AI is transforming GRC software platforms.

To understand how AI is shaping the future of GRC, one must first consider the limitations of existing GRC practices. For example, consider the enterprise regulatory landscape. Modern enterprises are under pressure to keep ahead of rapidly evolving regulatory changes in the business environment. New regulations lead to changes in compliance demands because of various requirements, such as new data, processes, and reporting structures. Keeping up with these changes in compliance demands is a huge challenge.

Many companies still have structured and unstructured data stuck in siloed operations. Additionally, many firms still follow a manual approach to manage GRC programs or use spreadsheet solutions. Siloed operations and traditional methods are error-prone and inadequate when processing vast data volumes and drawing insights. These limitations hamper real-time decision-making abilities. Processes critical to risk management, like risk prediction, risk assessment, quantification, and prioritization of critical assets, are impossible to implement without sophisticated GRC tools.

Now, modern AI-powered GRC solutions are helping businesses overcome these challenges. By analyzing vast amounts of data, identifying patterns, and providing valuable insights, AI can help enhance productivity and deliver targeted outcomes with GRC systems. Here’s a look at how the technology is transforming GRC practices:

Managing Risk
Businesses can use AI capabilities to identify risks and anomalies in third-party mandatory reports such as SOC 2 and SOC 3. Similarly, AI can help automatically classify risks reported from the front-line. As the front-line is not well versed with GRC processes, any tool that allows them to minimize their work will also help the second line to work with more meaningful data.

AI-based GRC systems can recommend risk treatment strategies. AI can assess real-time risks and automatically suggest controls by analyzing historical data. Powerful AI algorithms can sift through data, identifying unusual patterns and anomalies. With predictive analytics abilities, the technology can identify emerging risks and predict where those risks will come from. It can alert risk professionals to potential dangers or breaches, enabling them to mitigate them proactively.

The data library and model defining risks and controls in a large organization is complex. Here, AI can help overcome one of the biggest technical challenges by rationalizing the taxonomy.

AI can process large volumes of structured and unstructured data while offering actionable insights. These insights can speed up decision-making processes.

For example, AI can connect several systems and data sources, develop connected insights from underlying GRC data and improve the performance of existing GRC programs. The technology can help check potential control violations and risks using pre-built analytics to analyze and understand similar issues and control failures. AI can help review and accept automatically generated action plan recommendations. This can be done for a specific group or category of issues, reducing or removing duplicate issues. Those remediated with the same action plans can be identified and categorized. This is particularly useful for organizations with large control data volumes that lead to duplication, over-testing and under-testing.

AI is a potent tool in the GRC arsenal for mitigating cyber risks. AI-powered systems can help businesses scale up their cyber risk defence capabilities through advanced threat detection, predictive analytics, and real-time monitoring. Organizations can leverage AI-powered threat intelligence systems to detect emerging cyber threats and develop mitigation strategies. Monte Carlo simulation techniques can help predict and quantify losses to cyber risks and their probability of occurrence.