In today’s business world, ever-evolving and ever-changing technology is presented to us after long research, studying and testing. While the digitization of business processes and database-driven operations increase the efficiency of organizations, they also introduce new and diverse risks. Information technology (IT) risk management is a strategy implemented by organizations to identify, analyze and manage these risks in advance. In today’s world, where innovation, technology, products and research are increasing and diversifying, the risks that enter our lives along with the innovations are naturally increasing. Because of this, the role of risk management in information technology, and the need for proactive risk management, is increasing.
Companies are increasingly forced to store, process and transmit large amounts of data. To do this, they must increase their IT investments and capabilities every day. The security of sensitive information, such as customer data, financial data and trade secrets, can affect a company’s reputation and business continuity. IT risk management is a critical control point to ensure that this sensitive data is protected from unauthorized access, data leakage or malicious attacks. It aims to identify and control potential threats in these areas through risk analysis. IT risk assessment is not limited to information security—risk analysis can be applied to almost any area you can think of.
Reducing business continuity risks is another area of increasing criticality. Information technologies are vital to the proper execution of business processes and services. Unexpected events such as technological failures, human errors, cyberattacks or natural disasters are among the factors that increase business continuity risks. IT risk management is one of the most important tools for organizations to identify the necessary measures and plans to minimize such risks.
New technologies bring new risks
New technologies, products, customizations and systems come with many unknowns. Some of these may also pose risks and threats to organizations. It is not up to organizations to manage all of these on their own—regulators are trying to control them as well.
Information technology is an area in which organizations must comply with certain regulations and industry standards. Data protection laws, customer privacy requirements and industry standards can both constrain and control an organization’s operations. IT risk management helps organizations ensure compliance and take the necessary steps to avoid potential legal sanctions.
The AI factor in risk management
Artificial intelligence (AI), one of the most popular technologies in today’s business landscape, has started to be used extensively in the field of risk management. IT risk management is an essential tool for organizations to ensure data security, business continuity, compliance, competitive advantage and positive reputation. As technology advances and digital threats increase, it is critical for organizations to effectively manage these risks to ensure long-term success. Even though regulators are trying to account for the risks related to emerging technologies, the main responsibility falls on the institutions, which have to manage the risks from new technologies themselves. Performing a large number of risk analyses for so many needed areas, especially one by one for each system or structure within an area, places a very heavy operational burden on expert teams. At this point, especially in areas that can be considered more ordinary and routine, leaving the work entirely to AI and leveraging it for complex issues can both speed things up and ensure a more effective execution.
The more data used in risk analysis, the more accurate the analysis performed by the machine learning method of AI. In other words, the more historical information, risks, risk action plans and related information, the more consistent and sound the results will be. AI offers unique opportunities for analysis, especially for routine tasks such as risk analysis of many suppliers. For example, if the regulation requires risk analysis for all critical suppliers individually and the risk methodology is the same for all suppliers, it would be demotivating to have people do this work. However, if AI were to be applied here, it would enable this routine work to be automated to a great extent with AI support. Removing the relevant experts from such routine tasks and directing them to review the results produced by AI will increase job satisfaction and increase the efficiency and quality of the results.
Although the use of AI in IT risk analysis is still very new today, as the use cases and methodologies increase, the results will far exceed expectations.
