Hi everyone, thank you for joining me. This is section seven for ISO 270001 Lead Auditor. This is the impact of trends in technology and auditing, and we're going to talk about big data The use of big data and audits, artificial intelligence, machine learning, and cloud computing, the use of artificial intelligence and audits. The impact of cloud computing and audits and auditing outsourced operations So the impact of technology and auditing, uh, technology makes auditing a whole lot simpler than it than it uh could be. The advances in technology are changing on a daily basis. In relation to operations of organizations around the globe. Companies that are wanting to make more money, be more efficient, they're all obviously always trying to figure out how they can add more technology to do so. Technology has also been an impact on the way audits are being conducted and data is being analyzed. The use of new technologies like audit tools and software contributes to the increase of the audit quality by providing tools that can help deal with large amounts of data. Tools that can increase the audit efficiency, tools that allocate more time to the audit analysis instead of more time to conduct some sort of technical tests because the tools take care of that. Tools help minimize costs and they can also help provide transparency. However, the use of new technology tools Can also present certain challenges. Data security and quality control are two important factors that have to be considered by organizations to ensure that their data used is reliable, complete, and accurate. Not only that, but also has to be safe, has to be protected. So we have the concept of big data. So the dictionary defines big data as an accumulation of data that's too large and complex for processing by traditional database management tools. If you're interested in artificial intelligence, this is going to be covered in the 42001 course, which is also hosted by PECB. And I'll talk about that later. Big data includes a large number of structured and unstructured data. So structured data is organized, easily reachable. Unstructured data cannot be organized in relational databases and are not easily reachable. So structured data can have a defined data model and are based on relational databases. So example of structured data could be like SQL databases, Microsoft Excel files. And so on. Unstructured data would be an example would be MongoDB, Mongo database. So remember the two differences, especially from the vendor standpoint. Structured data would be like SQL SQL, SQL Server, or Microsoft Excel, but I don't really view Excel as a database, but SQL databases. And then MongoDB would be unstructured data. The use of big data in audits, so technology tools for big data processing can be beneficial in that auditors Can easily identify data that is available and use it easily. Retrieve data that may be considered sensitive, design their program to manage big data better. Collect more qualitative audit evidence, gather data from different systems, and combine structured and unstructured data Big data technology is often used for data analytics as it provides a scalable infrastructure for storing, processing, and analyzing large and complex data sets. Data analytics involves analyzing and examining data to obtain valuable outputs that support organizations in making strategic decisions. Big data analytics enable the integration of different data types quickly and efficiently. So there's a variety of different things we can do in relation to big data, but If we want to optimize the value of data so we can implement various technologies, and some of those could be uh data management, so referring to the activities that are conducted to ensure the accessibility, reliability, and timeliness of information, including the gathering of that information, the structuring structuring of the information, verifying, securing, and so on of the information. Auditors can rely on data management technologies to gather and organize data from various sources to support the audit process. We can also have data mining and that refers to the process of analyzing data to identify patterns and relationships between events. In auditing, data mining technologies are used to identify anomalies and unusual patterns in financial transactions or other data. So in relation to data mining, there may or may not be that much involvement with uh 27001, depending on the type of client you're interacting with. Auditors, however, can use data mining to identify potential areas of risk, such as fraudulent activities or financial reporting. We can also have predictive analytics. Basically, we take the data, we look at it, we start to see trends, and we start to anticipate what could happen. So, the use of big data and audits. So, Microsoft Power BI is an example of a tool. So, Microsoft Power BI is a powerful tool that can be used by auditors to analyze data and identify patterns, trends. and anomalies. Auditors can use Power BI for the following aspects during an audit. So data analysis, visualization, data cleansing, collaboration, and monitoring. And we'll talk about those. But again, from the standpoint of big data and audits, and as well as structured and unstructured. So again, structured data would be like Microsoft SQL Server, unstructured data would be MongoDB. But from the standpoint of Microsoft, think Power BI. Now, artificial intelligence. So the dictionary defines artificial intelligence as the theory and development of computer systems Able to perform tasks usually requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages So the interconnectivity fast data transfers that are now possible used to not be so possible through the usage of 5G and there's other technologies besides just 5G. Is expected to allow AI applications to become integral parts of our lives. AI is increasingly ubiquitous across industries. Some common applications of AI are banking, marketing, healthcare. autonomous vehicles and so on. So as it relates to AI, it's becoming more and more prevalent across uh various technologies. I just got a new iPhone 16. It's new to me, maybe not so new to everybody else. But one of the options when I set it up was include artificial intelligence uh uh upgrading some of the uh older iPhones to a current uh iPhone tech iPhone operating system that now allows for AI. There's numerous areas of AI that's now being included across the board. But it is important to understand that artificial intelligence solutions don't necessarily make the right decision the first time. They have to be trained. They have to understand. They have to learn. And so on. So there could be, for example, I saw a video Where a guy had a bunch of balloons on the wall and he said, track all the yellow balloons. And the camera moved all around, but it didn't really know what to do other than track the yellow balloons. And it but it knew what the color yellow was. So it was able to visually perceive the color yellow. It was visually or it was capable of understanding its directive, of tracking the yellow balloon But it didn't know what to do after that. There that had to be another command uh with a decision made by uh a human being I also don't want people to get the impression I I think most people across the planet have seen the Matrix movies or the Terminator movies or so on and so forth. Uh don't want people to think that it that uh artificial intelligence is to that level. It's it's it's not. So the use of artificial intelligence and audits, artificial intelligence is mostly applicable in the domain of information collection or collecting, which refers to data extraction, comparison, and validation processes. By leveraging AI-enabled technology, auditors can extract information from documents and use it more efficiently AI-based systems such as expert systems and neural networks can help auditors in their decision-making processes by addressing potential biases and omissions that could arise. In a manual decision process. The aim is to improve the overall quality of auditor judgment. So there's various technologies that can be used in conjunction with AI technologies To help collect and analyze data that could be used to facilitate the audit process. So, for example, we have the inductive language programming. It could be used in auditing to show patterns and anomalies in large data sets. as well as prepare a hypothesis based on data itself. We can have robotic process automation, which can be used to help automate repetitive tasks Mimicking iterative auditing tasks by identifying inconsistencies and outliers. Expert systems can be used to imitate the auditor's way of thinking to solve problems. Be very cautious about saying the word expert systems because again, artificial intelligence has to learn. You're not gonna flip a switch and they're gonna be as smart as a human being the very second it's turned on. Decision networks can help auditors make more informed decisions based on incomplete or uncertain data because we can predict what the data could be. And then artificial neural networks analyzing large data sets and identify patterns and relationships between different variables without any sort of other prior instruction. But again, that implies learning. AI applications for auditing, risk assessment of individual transactions, augmented audit interviews, and then augmented analysis. So we can perform risk assessments in relation to individual transactions by automatically assessing the accuracy and legitimacy of the individual transactions Without AI or without technology, this could be a labor-intensive process that's could be prone to human error, particularly for small businesses or small entities in general By using AI, the process can be improved, resulting in cost savings, not only to the company, but also increased profit because that reduces human effort, which means they can do other things. To generate revenue, as well as increase the quality of the auditing process itself. The AI-based risk assessment involves automatically detecting and flagging abnormal transactions. between accounts that are unrelated, infrequently used, or do not match the transaction detail. So this helps connect things that did not appear to be obvious independently Could help identify transactions that appear suspicious or fraudulent by looking at outliers as well as evaluating authorizations for transactions. In relation to the cybersecurity space for SIMS, security information event monitoring, a lot of the SIM solutions are actually implementing artificial intelligence to help identify suspicious or fraudulent activities. As a part of the outliers. We can also use AI to augment audit interviews, capture and analyze clients' verbal interactions, so basically their voice speech patterns and uh understand what they're saying, why they're saying, or maybe possibly pick up uh some other things, which could lead into an augmented analysis So if we're if we're able to augment the audit interviews and able to determine speech patterns and uh and so on in relation to the truthfulness or body language if there's visual recognition, then we might possibly be able to use artificial intelligence as a uh ability to analyze that and and augment it a little bit more We can also do an automated confirmation letter, automated data validation, and augmented physical observations. So automated confirmation letter to validate transactions and account statuses Auditors often need to automate or to communicate with external parties during the audit, such as creditors, debtors, customers, regulatory bodies. and so on, or law enforcement, or maybe shareholders. These validations commonly involve repetitive tasks such as selecting the accounts that should be verified, composing and sending the confirmation requests receiving and reading the response, updating the confirmation status, and so on. So all of that could be automated to save a lot of time for the audit team uh itself. By automating uh such processes, auditors can save time and resources. Nonetheless, there are some challenges associated with the use of this technology, such as the financial cost to even make it happen. If it's obviously if it's a smaller entity and the audit the confirmation letter is only gonna be to ten different entities, probably don't want to do that, but if you're looking at a hundred thousand, a million confirmation letters or whatever, you're probably much better off to figure out some way of automating all of it. Automated data validation prior to publishing the annual report of a of an entity, we could validate the accuracy and completeness of the numerical data in that report for that. uh entity that uh ought a T. And then also we can augment physical observations based on uh what uh artificial intelligence, how it's set up and how you're if if they even have a need for a physical observation of the oddity.