There is no doubt that we now live in an AI-driven, automation-powered world. Across industries and markets, leaders and professionals are achieving the utility of AI in their processes. The same applies to Governance, Risk, and Compliance (GRC) management, but when one looks at the actual implementation, the data shows that there’s still a long way to go.

According to one recent report, only 21% of GRC leaders use AI to perform GRC activities. This figure includes those who have deployed AI systems directly as well as those who access it through a secondary platform.

Many businesses might have automated systems in place, but the ceiling of automation is limited without AI integration. That’s why automation and AI must be considered together.

In any case, the market conditions are strong, and there is a lot of progress being made, with automation and AI transforming various aspects of GRC management. There are also important lessons for leaders to apply to their organizations and boost their defenses, especially as GRC continues to become increasingly intertwined with cybersecurity and data privacy concerns.

Predictive Analytics

The ability to anticipate potential compliance issues and risks before they materialize is crucial for maintaining a secure and lawful business environment. By analyzing patterns and trends, companies can use predictive models to identify areas of vulnerability. For example, if a pattern of late audit evidence submissions is noticed in a specific department, models can flag this as a potential risk area.

Issues identified may not even be outright regulatory violations yet and may only tend in that direction, but accurately catching them before they become a serious danger is the very reason for integrating AI and automation in a company’s GRC process.

Automated Auditing

Traditional auditing processes are foundational to maintaining integrity and compliance, especially in cybersecurity, but they are often labor-intensive and prone to human error. Given the many tasks can be automated using platform integration tools for evidence collection, it is all the more inefficient for compliance auditors to manually sift through vast amounts of data, cross-referencing logs, checking for inconsistencies, and ensuring that every detail adheres to regulatory standards.

Meanwhile, automated auditing provides real-time vigilance that not only reduces the workload on auditors but also ensures that compliance checks are comprehensive and performed consistently. A file integrity monitoring tool, such as Fortra’s Tripwire File Integrity Manager comes in handy here, helping to ensure that relevant data remain intact and reliable for auditing.

For instance, the integration of Natural Language Processing (NLP) into auditing systems enables informed analysis of unstructured data, which are often rich sources of information for actual and potential compliance breaches.

Risk Assessment and Management

The best way to provide a dynamic and holistic view of an organization’s risk standing is to employ automated risk assessment tools. Platform integrations provide the needed data, while smart algorithms can reconcile what’s happening across your digital footprint with the regulatory standards that apply to your company.

Machine learning algorithms can analyze vast amounts of structured and unstructured data from multiple sources, while natural language chatbots can help with executing changes to mitigate risks. Once this is automated, businesses can receive continuous feedback that enables them to adjust their risk management strategies swiftly.

Ongoing intelligent gap analysis enables organizations to monitor and address security gaps while assigning urgency scores to various components of regulatory frameworks that deserve attention. This capability is crucial for devising effective mitigation plans. AI can likewise assist with quantifying risks.

“Once risks have been identified, the next crucial step in your compliance risk management plan is to conduct a comprehensive analysis, measuring, assessment, or scoring of each of the identified risks,” explains Yahav Peri, the CTO and VP of R&D at Cypago. “This involves giving meaning to each risk, taking into account factors such as the likelihood and impact of the risk, the expected loss in the event of the risk happening, and the probability of the risk.”

Anomaly Detection

AI systems excel at detecting anomalies that may escape the notice of human auditors. For instance, while a human auditor might overlook slight inconsistencies in user behavior across digital environments, AI can pinpoint these variations – especially if they form an unusual pattern – with remarkable accuracy.

In recent years, regulations have introduced increasingly strict breach disclosure requirements, so having the ability to stop and report incidents quickly goes a long way towards compliance.

The true power of AI in anomaly detection lies in its ability to analyze complex patterns and relationships. Malicious activities often involve intricate schemes designed to appear normal at first glance, but an AI system trained for this purpose can automatically trace these threads.

More so, by continuously learning and adapting to new attack patterns, AI helps your business to remain one step ahead of criminals and ensures that defense mechanisms are up-to-date.

Regulatory Intelligence

Regulatory intelligence is an emerging field, given how quickly regulatory frameworks are enacted and updated.

Consider the field of data protection, for example. Just a few years ago, no country had an overarching data protection law that captured modern technological realities. However, since the European Union’s GDPR came into effect, several countries have come on board with new laws regarding data privacy, each with its own requirements.

To be able to navigate such a complex terrain, organizations need to track regulatory changes across different jurisdictions and stay up-to-date with the latest legal requirements. For regulatory intelligence, AI-driven automated tools help track changes, analyze the impacts of those changes, and help businesses avoid sanctions and other risks.

Customer Due Diligence

In the financial world, customer due diligence is the foundation for compliance with Know Your Customer (KYC) regulations and AI and automation have a crucial role in transforming this process. Algorithmic models can be trained to identify high-risk individuals and entities with greater precision than traditional methods.

This includes cross-referencing customer information against databases of known criminals or politically exposed persons in a fraction of the time it previously took, highlighting the efficiency of automated identity verification and background checks.

Various data points can be collected to automatically generate a comprehensive risk profile for customers, ensuring that an organization remains vigilant against new methods of money laundering and fraud. In addition to assessing static risk factors, AI systems can analyze customer behavior patterns to detect suspicious activities.

Aligning Cybersecurity Risk Management with Broader Objectives

The state of cybersecurity in 2024 stands at a critical juncture. The last few years have seen a huge increase in ransomware attacks and other sophisticated cyber threats. This has certainly increased scrutiny by governments, particularly over organizations that hold a large amount of Personally Identifiable Information (PII) belonging to customers.

Based on this, it is imperative for business leaders to integrate cybersecurity into their core business strategies in order to create a sustainable future for their organizations. The following are some of the ways leaders can integrate cybersecurity with broader organizational risk management.

• Common Risk Language: To foster collaboration and avoid departments talking over each other, they need to agree on a common risk terminology.
• Integrated Risk Assessment: Risk assessments should be comprehensive and holistic and should aim to identify interdependencies between cybersecurity risks and other kinds of risks (operational, financial, legal, reputation, and strategic).
• Risk Quantification: When you think about risks, don’t just think about what could go wrong on an abstract level. Think about losing money, customers, investors, and partners. Being able to quantify risks in this way helps to justify investments in risk mitigation measures.
• Risk Appetite and Tolerance: Your organization’s risk appetite should be clearly defined, especially when it comes to cybersecurity risks in the context of overall business risk.
• Incident Response and Business Continuity: Integrate cybersecurity incident response plans into the overall business continuity and disaster recovery plans.

Conclusion

As cybersecurity threats grow in complexity, embracing AI and automation tools to enhance GRC operations will be increasingly paramount for business sustainability. This not only strengthens compliance resilience but also fosters a culture of adaptability, which is crucial for long-term success.

Editor’s Note: The opinions expressed in this and other guest author articles are solely those of the contributor and do not necessarily reflect those of Tripwire.