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Transforming IT GRC with AI and Machine Learning

Transforming IT GRC with AI and Machine Learning

The Overview of IT GRC Improvement

Several drivers have accelerated the digital shift in IT GRC in recent years. While the pandemic enhanced the pace of digital transformation in most companies significantly, it also exposes the gaps in GRC processes. A joint study by Deloitte & Touche and Compliance Week found that despite an increasingly complex environment, 40% of companies do not complete a yearly compliance risk assessment at all, which makes companies vulnerable to compliance risks, as well as legal, audit, financial, and operational risks. This triggers uncertainty in companies, where employees were working in disrupted areas and became overwhelmed by unpredicted challenges and changes. Fortunately, Artificial Intelligence (AI) and machine learning become the solutions for GRC improvements. AI and machine learning has the ability to revolutionize the scope and functionality of risk management systems, revolutionizing the GRC environment. Furthermore, the survey results underlined, that while the GRC function has become increasingly important in this new uncertain world, systems, and processes used to manage risk and compliance require further development.

The Overview of AI and Machine Learning in IT GRC

In an increasingly digital world, data is fast becoming the lifeblood of a company, and automation tools such as AI and machine learning can help companies better anticipate and manage emerging risks. To conclude, companies are seeking greater visibility, assurance, and confidence from AI and machine learning solutions. AI defines as a computerized technology that enhances its system by self-learning from human behavior over time in order to simulate human intelligence and mimic its behavior. In this way, AI can adapt human behavior in processing information and making decisions. Machine learning allows software applications to achieve accurate decisions at predicting outcomes without explicit programming. In its implementation to technology advancement, the objective of AI is to enable intelligent machines to think and act like humans, on the other hand, machine learning's objective is to get systems to learn from data, identify patterns, and make decisions without human assistance without needing to be programmed. In conclusion, machine learning searches for patterns using its experiences in order to learn from them. AI learns from its experiences and gains skills and knowledge on how to use that knowledge in other situations. GRC solutions that combine AI its application with machine learning will offer immersive benefits for companies to strengthen their business value in the future.

Artificial Intelligence for IT GRC

In the midst of interconnecting and evolving technology, AI helps companies to ease the process by acting as a catalyst for forced integration between contrasting structures. AI has become a major need in GRC as companies expand their digital footprints and increase their cybersecurity due to potential risks and vulnerabilities that occur from the huge amount of data being produced. AI has arrived and is already having an impact across a range of operations, including governance, risk management, and compliance programs. However, despite of potential imposed by AI in industry, the technology itself is not well-implemented in many companies. According to a survey by IBM, only 21% have employed advanced technology in AI for their business environment, mainly being used for reporting analytics, enhanced risk identification and monitoring, and conducting workflow automation. The AI-powered data analytics engine can learn based on benchmarking across many customers' data, which later helps companies to gain more accurate insights from much larger pools of anonymous data pools. Several cloud-based AI platforms like AWS SageMaker, Azure ML, or Google VertexAI can help accelerate the AI development journey from experimenting to deploying products catering to various aspects of GRC.

Machine Learning for IT GRC

By applying machine learning, GRC solutions can learn from human analysis and continuously monitor for the emergence of high-risk vulnerabilities, thus catching them and through cognitive computing, orchestrating action that can prevent major incidents from failure. Machine learning can enhance GRC advancement by managing risks and opportunities based on more advanced factors than risk appetite thresholds, estimations, and responses. In data management for GRC solutions, machine learning helps identify fraud and waste patterns, mine financial data, and use predictive techniques to develop more effective controls. For minimizing vulnerabilities in cyber threats, machine learning helps companies understand and prevent cyber threats by analyzing data and automatically learning from successful attacks. In advancing automation in GRC solutions, machine learning makes better use of complementary technologies like robotic process automation to improve processes, refine computer-based decisions, and improve algorithms. Robotic process automation can be an important tool to build more robust and effective compliance programs that will support continuous control monitoring, as well as fill sample-auditing, making it easier to detect anomalies.

Future Challenges

AI and machine learning may offer the most inventive solutions when it comes to GRC solutions, however, the applications itself is not without difficulties. Training the technology requires more knowledge and resources. Employers should provide comprehensive training to members to enhance the experience of developing and using AI and machine learning in GRC solutions, which ultimately lead to a true test of innovation and originality for GRC specialists. Further investment in GRC technology such as new automation techniques, is also needed to elevate the role of risk and compliance beyond conventional auditing responsibilities and compliance box-ticking.


The idea of AI and machine learning is not new. It took a lot of time, work, and research to develop and build, and throughout time, it was modified to make it ready for the modern digital environment. It improves current systems and processes. Work is streamlined by automation, which also optimizes resource allocation and frees up workers to concentrate on tasks that really call for human involvement. With the new tools and inventions emerging in the near future, there will be a lot of experimentation in optimizing AI and machine learning in GRC solutions.

Eckerson. W. W. (2007). Predictive Analytics: Extending the Value of Your Data Warehousing Investment. TDWI Best Practices Report. Canada.
Jeble. S.; Patil. Y. (2016). Role of big data and predictive analytics. International Journal of Automation and Logistics. DOI: 10.1504/IJAL.2016.10001272

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