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    How AI and Risk Management Can Work Together

    For decades, thanks to popular sci-fi movies and books, the collective imagination has been frequently struck with the idea of intelligent computers outsmarting and replacing humans. Fortunately, that imaginary scenario hasn’t been brought into reality yet.

    But something else has happened: the emergence of artificial intelligence (AI), particularly cognitive computing. AI has been turning into a significant part of our daily lives. The digital personal assistants, smartphones, self-driving cars, music and movie applications, online shopping sites, and every application that can learn and adapt to human preferences are powered by AI technology.

    The cognitive capabilities of an AI model include data mining, machine learning (ML), and natural language processing (NLP). These advanced concepts can be used to teach computers to recognize and identify risks and address complex situations. With the aid of traditional analytics and human thought processes, the cognitive ability of AI has begun to assist business decisions and boost business performance. Since typical enterprise risks often consist of unlikely disastrous events, the area of risk management lends itself to the cognitive capabilities of AI.

    Artificial Intelligence and Risk Management Solutions

    Computers always have the superior ability to perform calculations faster and more accurately than humans. With the advent of AI, computers acquired the ability to learn and analyze more quickly than humans.

    AI is helpful in risk management because it can easily handle and evaluate unstructured data—the information that doesn’t fit into structured rows and columns. Cognitive technologies of AI, such as NLP, use advanced algorithms to analyze unstructured data to derive insights from them. 

    A 2015 study by International Data Group estimates that nearly 90% of data generated is unstructured, and the implementation of cognitive analytics can place businesses in a better position than their competitors. Business leaders who utilize AI to proactively manage IT security risk can gain a competitive advantage and boost the performance of their enterprises. Deloitte estimates that the global market revenue for cognitive solutions will surpass $60 billion by 2025.

    Financial technology (FinTech) companies, banks, and insurance companies implement risk management solutions using AI to facilitate decision-making processes, reduce credit risks, and provide personalized financial services. The ML algorithms of AI can analyze large amounts of data relevant for IT security management, risk assessment, and making accurate business decisions.

    Digital application platforms such as Trello have already begun utilizing AI to analyze user behaviors and predict the recurring online activities of their users. QuillBot, a content creation and rephrasing application, runs its business model on AI technologies such as NLP. These AI technologies can also be efficiently used for enterprise risk management, simplifying business processes, and using IT resources productively.

    Also read: The Pros and Cons of Enlisting AI for Cybersecurity

    Key Aspects of AI-enabled Risk Management System

    Some of the key aspects of AI-enabled risk management systems are as follows:

    IT security threat analysis and management

    ML algorithms can analyze large amounts of data from different sources. The real-time prediction models developed from this data help risk managers and security teams address IT security risks quickly. In addition, these prediction models work as the foundation for early warning systems that ensure uninterrupted enterprise operations while enhancing data privacy and protection.

    Enterprise risk reduction

    AI enables an enterprise to thoroughly evaluate the unstructured data about risky behaviors or activities in its operations. ML algorithms can identify past risky behavioral patterns and transpose them as prediction models.

    Fraud identification

    Traditionally, the identification of fraud or theft requires intense analysis processes, particularly for financial institutions and insurance companies. AI-enabled risk management systems can substantially bring down the workload of these fraud identification processes. In addition, as ML algorithms focus on social media analysis, text mining, and database searches, they can significantly reduce IT security threats.

    Classification of data

    Data comes in all sizes and forms. AI technology can efficiently process and classify all available data in accordance with predefined patterns and classifications. It can also constantly monitor access to these data sets.

    Also read: 10 Ways Companies Screw Up Their Cyber Investigations

    The Implementation of AI in Your Enterprise Risk Management Plans

    Benefits always come with risks and challenges. The case is the same with AI-enabled risk management systems. While implementing AI technology, an enterprise must pay special attention to the risk associated with it, such as data protection, data privacy, and the costs of implementation.

    You can use the following procedure to reduce the risk of implementing AI in your enterprise risk management plans.

    Ideation

    Before implementing an AI-enabled risk management system, an enterprise should identify the risks associated with its reputation along with industry and legal regulations. A thorough risk assessment of current enterprise frameworks and values will do the job. It can also be used to choose the data collection and analyzing process that suits your needs.

    Data sourcing

    Based on your previous risk assessments, you can define which data sets are suitable for AI-enabled risk management systems and which ones aren’t. Therefore, care should be taken when it comes to the type and the source of data. The right data set can significantly influence the quality of the operational results. Thus, data sourcing is a crucial step in the implementation of an AI-enabled IT security ecosystem.

    Model development

    Once you possess valuable data, use it to build a useful AI model. Always be sure about the level of transparency you need in AI operations because some AI tools aren’t recommended for high-risk operations. You should spend a quality amount of time studying the industry standards and regulations. This will help you understand how specific AI technologies can be used for certain business processes and how AI can fructify your business goals.

    Monitoring

    Every risk management tool requires constant evaluation and adaptation, including AI risk management systems. Therefore, it is always crucial to consider the ever-changing enterprise needs and the imminent drawbacks the technology may bring.

    AI May Go Rogue

    It is more apt to say that AI will be a game-changer in the risk management sphere, but it changes the game by taking one step at a time.

    Owing to its autonomous nature, implementing the proper IT security controls on AI is also a form of risk management. According to McKinsey, AI technology requires a little more attention than standard IT. This is because AI brings several unknown risks in terms of ethics, compliance, legal, operations, and regulations.

    Another IT security concern is the rapid decentralization of AI across various enterprises that makes constant tracking and monitoring difficult.

    According to PwC, the best way to manage all these IT security risks is to put proper IT security measures in place before AI becomes an indispensable part of an enterprise.

    Ultimately, AI technology management is a matter of trust. An enterprise should be wise enough to consider AI just like any other employee. First, assign it with limited responsibilities, and track its performance. Then, promote AI to higher ranks of the corporate ladder only after it proves to be both capable and worthy.

    Amid unpredictable risks, odds always favor the fact that AI and humans will find a way to work together. Since the strengths of both bits of intelligence compensate for each other’s weaknesses, it would be more likely to be a mutually beneficial relationship than a hostile one.

    Read next: Top Risk Management Tools & Software

    Kashyap Vyas
    Kashyap Vyas is a science and technology writer with 9+ years of experience writing about SaaS, cloud communications, data analytics, IT security, and STEM topics. In addition to IT Business Edge, he's been a contributor to publications including Interesting Engineering, Machine Design, Design World, and several other peer-reviewed journals. Kashyap is also a digital marketing enthusiast and runs his own small consulting agency.

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