As organizations examine ever-increasing volumes of data to get their arms around their risk exposure, it’s tempting to simply push the AI button and assume you’re covered. But as Gaurav Kapoor, MetricStream’s co-CEO and co-founder, explains, while AI holds transformational potential, it can’t and shouldn’t do everything.
Artificial Intelligence (AI) went mainstream in 2023 across many industries, including governance, risk and compliance (GRC). Corporate integrity and risk management leaders are now embracing AI because each year, as compliance and risk grow more complex, AI technologies get better. Risk management teams are struggling to keep up with the increasing scale and intricacy of requirements, especially when it comes to tracking changes in regulatory compliance and maintaining efficiency of internal audits.
Furthermore, companies and their leadership teams are prepared to invest in technology to help their governance and security practices run more efficiently. According to MetricStream’s and OCEG’s recent survey, 18% of businesses intend to invest in GRC technologies in 2023, with nearly 30% planning to do so in the next three years.
As leaders think about how to onboard new technologies and search for solutions that are scalable and adaptable for their business, they need to first understand how different types of AI solutions can revamp their GRC strategies, protect their businesses from risk and maintain compliance.
AI powers risk assessment and compliance monitoring
As organizations grow and scale, it becomes more challenging for risk management teams to stay organized, maintain security and manage the risks and costs that come with everyday operations. With the risk landscape, and the regulatory environment that governs it, changing every day, risk managers need real-time, dynamic options for doing their job more efficiently. The push for digital transformation also necessitates the use of AI: Organizations deal with huge volumes of data, with much of it still unstructured, textual data on physical documents, making it challenging to fit into common taxonomies and quantified methodologies of managing risks.
AI technologies assist with data processing, identification, categorization and analysis-driven tasks, helping risk managers respond to potential risks faster and with more efficiency. AI-powered technologies can be developed and deployed to help risk leaders with:
- Identification: In the modern enterprise, risks are interconnected. Organizations must take a connected and holistic approach to understanding their risk posture. In the past, leaders have struggled against organizational silos within business practices and across time horizons. It’s challenging (or impossible) for the human mind to process all the data signals within the organization and then draw parallels between risks across different areas of the business. AI excels at taking in data, identifying patterns and testing for duplicates or discrepancies in datasets or in risk controls already in place. GRC leaders need to take advantage of the semantic analytics and natural language processing capabilities intrinsic in AI technology to overcome the hurdle of a vast and siloed data ecosystem. If they do, leaders will see significant cost reduction along with increased efficiency of their risk program.
- Streamlining classification: When risk issues are reported, they also need to be classified correctly for risk managers — and AI-powered systems — to take appropriate next steps. Oftentimes in risk assessments, issues and actions may be duplicated or reported incorrectly by third parties or frontline users who are not risk experts. Reports made inconsistently at high volume become tedious and time-consuming for risk teams to sort out, delaying the important work of triage and decision-making. AI can be incorporated into a risk management system in multiple ways to better classify reports and streamline the takeaways for human evaluation.
- Risk scoring and quantification: The power of cognitive AI to turn data into real-time decisions is immense. But it’s harder for risk leaders to act or get other leaders on board with a decision if they cannot quantify or measure the impact of a risk or decision. Risk reports, particularly SOC2 and SOC3 reports from third parties, can be voluminous and require detailed analysis to spot irregularities. Risk leaders can benefit from AI tools that are geared toward computing and ranking risks in these reports to make real-time recommendations. With a better understanding of risk data, risk leaders can more effectively measure risk, action to protect the organization and decrease the windows of opportunity for threat actors to strike.
Specific guidance for AI may vary by application and use case, but it’s clear that AI-powered solutions are not just a trend. AI is a growing part of a GRC leader’s toolbox and a key area of future investment for businesses.
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Risk leaders must understand what generative AI is — and what it is not. Both AI and generative AI tools rely heavily on machine learning techniques, algorithms and datasets to perform tasks. But unlike AI, which is limited to processing and computational skills, generative AI is a specialized subset of AI technology that is capable of generating original content — data, text, images — responsively in a human-like way.
While AI can continuously monitor for risks, identifying, classifying and scoring risk issues and controls, generative AI can be used for processes like generating reports and recommendations, simulating threat scenarios, generating synthetic data patterns to test security solutions and extracting relevant information from complex regulatory and compliance documents.
Generative AI is so promising for this industry because business leaders are under more pressure than ever to anticipate risks, take action and help their organization not just manage but thrive on risk. The responsive and iterative features of generative AI technology, from predictive modeling to generative analysis and reporting, can unlock new capabilities for risk leaders who want to transform their risk management strategy from defensive to proactive.
Future considerations for AI in GRC
One barrier to widespread AI adoption in GRC is risk managers’ desire to have the perfect AI copilot that catches everything. The stakes of risk management demand perfection, airtight security and comprehensive, connected control of risk and compliance. But AI isn’t and never will be 100% perfect in any practice, including GRC. Over the past few years, we’ve seen organizations realize and accept this truth that AI alone can’t achieve perfection. Human oversight and human review are an essential ingredient in GRC, even when AI takes on the heavy lifting. This is a big step toward growth in AI for GRC.
Another consideration is that generative AI’s potential is substantial, but GRC leaders still need to keep in mind that AI also introduces new challenges that demand careful review. These challenges include the need to address biases within AI systems, ensuring the ethical use of AI, safeguarding data privacy, adhering to regulatory frameworks and maintaining transparency in AI operations. Effectively managing these challenges is vital for harnessing the full capabilities of generative AI.
Broadly speaking, the future of AI for our industry is already here. It’s happening now, and it’s exciting for leaders who are already unlocking efficiencies and better managing risk within their organization through using this technology. With real-time monitoring of risk exposures and changes in regulatory compliance, AI for GRC supports a preventive, predictive and diagnostic approach to GRC that ensures stakeholders receive accurate risk insights they can act on with confidence.
Gaurav Kapoor is co-CEO and co-founder of 
