The debates over artificial intelligence seem never-ending: Are we in an over-valuing “hype cycle,” or are we at the cusp of a new AI-powered age? Governance, risk management and compliance (GRC) professionals are divided on the future of the field. Some believe that AI can take over many of the existing GRC workflows, while others argue that AI and machine learning create too much risk within the regulatory framework.
The answer, of course, is somewhere in the middle. Artificial intelligence is a valuable tool that needs to be treated like any other tool: used with common sense and plenty of human supervision. The ideal balance will draw on AI’s capacity for speed and scale while ensuring that human experts provide context, ethics and strategic alignment.
Learn how to achieve that balance and what the future of governance programs should look like.
AI and GRC: Playing to AI’s Strengths
Artificial intelligence excels at identifying patterns and automating processes. GRC professionals can harness those capabilities to bring greater efficiency and a level of automated compliance to GRC workflows.
Any process that involves data collection and analytics can benefit from AI. Organizations today collect data on a massive scale, making it challenging for human teams to sift through that information, manage it effectively and conduct internal audits or risk analysis.
This is where AI comes in. AI-powered tools can rapidly organize huge databases, flag anomalies and identify patterns.
Here are some of the areas where AI can make a difference in GRC systems.
Risk Identification
AI tools can “read” and analyze data from a wide range of sources to discover new threats and conduct security reviews, from market disruptions and cyber threats to supply chain snarls and vendor risk management.
Fraud
AI’s predictive analytics capabilities make it an excellent fraud detection tool. It can identify anomalies in spending, for example, that could point to financial fraud. It can also identify unusual patterns in emails and text messages that could indicate a cyber risk.
Cybersecurity
AI tools can monitor networks for signs of an intruder. They can also stay up to date on the latest cyber threats and issue alerts. In some cases, automation can apply network security patches.
Audits
AI tools can collect and organize data for internal and external audits so that GRC teams have the information they need at their fingertips. AI-powered analytics can create visualizations to make it easier to extract insights and produce reports analyzing the data from various standpoints. In the healthcare sector, for example, AI tools analyze medical record access system-wide to find instances of unauthorized access based on details such as location, time of day, or physician specialty.
Compliance Frameworks
Compliance frameworks change frequently, and it can be difficult to keep up with the latest developments. Connected GRC AI tools can monitor the relevant regulatory bodies for changes in legislation. Generative AI systems can draft memos explaining any relevant changes to management and staff.
The Limitations of AI in GRC
AI performs best when used alongside a team of human experts. On its own, an AI tool lacks the judgment to successfully assess risk or ensure compliance. A lot of GRC work is not simply a matter of spotting anomalies and collecting data; it requires human skills like interpretation and contextualization.
Lack of “Soft” Interpersonal Skills
In many cases, GRC requires strong interpersonal skills. Performing risk assessments often entails interviewing people from other departments or other organizations to understand their views on the risks facing your operation.
AI tools can deliver valuable insights by scanning for risks online. But that doesn’t replace human insight or the kind of analysis that grows out of human conversation.
Interpersonal skills are also needed when it comes to third-party audits, or conveying information about a new security posture to employees. Even advanced agentic AI cannot meet with auditors or have meaningful communication about data privacy with a group of employees. Such responsibilities need to be carried out by human beings.
Lack of Higher Cognitive Skills
AI-automated processes can collect and organize data, but it can’t make mental leaps based on that data. These systems often struggle to contextualize data. AI tools can’t talk to other risk assessment experts to develop a refined analysis.
In addition, modern AI systems are unfortunately prone to hallucinations, making up inaccurate or untrue information. This is particularly a danger in risk assessment, which requires accuracy.
Difficulty Setting Risk Tolerance Levels
Lacking human judgment, AI struggles with determining acceptable levels of risk. Determining an organization’s risk tolerance and appetite for risk is a value-based process, which means it is not well-suited for AI.
AI generally performs poorly in areas where there is no universal framework. Risk tolerance depends heavily on context, personal and organizational values, and goals. All of this makes it an area best left to human experts to determine.
Balancing AI With Human Expertise
AI certainly can be a useful tool in the GRC toolbelt. The technology enables fast, accurate data analytics, making it a key part of any modern GRC effort.
However, AI’s limitations are also clear. It’s incumbent on GRC professionals to create strategies that make the most of the technology’s capabilities while minimizing the risk factors.
Creating Shared Workflows
AI is most effective when it is continually supervised and reviewed by experienced human employees. It’s also best to treat AI’s output as a rough draft, never as a finished product.
Everything that AI produces should be checked over with care by a human operator. Experienced employees should review AI-generated analysis for signs of hallucinations and bias. Of course, if bias is discovered, the team should conduct a detailed investigation and retrain the algorithm as necessary to root out that bias.
What the Future of GRC Will Look Like
The future of GRC will feature AI as a valued but limited tool. GRC professionals will use AI tools to deliver the speed, insight and scale needed to successfully conduct audits and monitor for risk. At the same time, GRC staff, as the humans on the team, will provide constant guidance on context, ethics and strategic alignment.
Human team members will determine risk tolerance levels and draw up policies that reflect those levels. Likewise, human staff will meet with risk assessment professionals throughout the organization to create a dynamic and fully contextualized picture of both current and emerging risks. Humans are also needed for the education and outreach to drive effective compliance policies, working closely with employees at all levels to ensure that the policies are clear and achievable.
Broadly, the future of GRC will see AI tools taking on the manual labor of collecting data, scanning vast databases and organizing data into reports. Using AI at scale enables organizations to stay aware of new threats and to monitor new regulatory obligations.
At the same time, human expertise will continue to be needed throughout the GRC process. At every stage, human experts are needed to review AI’s work, direct its progress and build on its preliminary analysis. Human experts are also crucial for the soft skills they possess, from communication to risk tolerance analysis.
Looking to the Future of Risk Management
It’s vital to stay up to date on AI’s evolving capabilities and the potential risks as well as the benefits posed by the ever-expanding technology. Watch our webinar to learn more about the future of AI risk management, and get started on a journey to streamlined, accurate and scalable GRC workflows.
