- Case Studies
Artificial Intelligence has become relevant in daily life and companies' development. This technology simulates human intelligence processes by machines, especially in computer systems. This technology has many application possibilities, including in the business area, because companies perform multiple activities that can be optimized thanks to the efficiency and effectiveness of this technological solution.
The capacity of Artificial Intelligence is enormous, making it capable of analyzing big data relevant to cybersecurity, risk management, risk assessment, and business decision-making. Specifically, in risk management, AI teaches computers to recognize and identify risks and deal with complex situations.
Through traditional analytics and human thought processes, AI has been able to help companies make informed decisions and drive business performance, bypassing the entire manual management system.
Artificial Intelligence is a branch of computer science that involves the simulation of human intelligence through machines and computer systems that can perform multiple tasks that a person would do. To fully accomplish these tasks, AI learns automatically by analyzing large amounts of structured and unstructured data to perform complex actions and make informed decisions.
ANI is a type of Artificial Intelligence characterized by being inflexible because it does not adapt to the requirements of a specific system or machine. The function of this AI is to focus on a single job with high complexity. Therefore, it is of great use to the skilled professional dedicated to performing a single complex function.
The scheduling of actions manifests the functioning of the ANI model. Preparing to act in a single role is necessary during this stage, reducing its performance as much as possible. In this way, the professional guarantees the full implementation of their tasks. Although this is a limitation, it helps to be perceived as an efficient and integral performance.
Concerning its characteristics, ANI is an Artificial Intelligence recognized for having a reactive character and a limited memory. In addition, all other types of AI can be considered ANI. Still, they differ mainly in that most other types of Artificial Intelligence are designed to fulfill different functions and even multi-functionally.
Technical classifications identify ANI as an intelligence incapable of mimicking human behavior by simulation alone. For that reason, they are only oriented to meet specific objectives.
Generally, ANI is applied in functions such as:
AGI is a type of Artificial Intelligence recognized for being solid and deep, as it can mimic human intellect with a great breadth of action. Regarding its behavior, this AI can learn, and based on this, it can replicate attitudes to provide solutions to different issues. For this reason, it is one of the most versatile models available today.
AGI's primary function is to think, which results in a unique and not only robotic understanding. Therefore, the proposed solution is different depending on each scenario it faces. This ability to adapt to different scenarios causes a resolution activity close to the human mind, so it is considered a much deeper intelligence.
AGI is based on a theoretical structure, so it can evaluate and detect different needs, processes, and emotions to implement the appropriate actions. This unique feature differentiates it from other types of Artificial Intelligence.
AGI's learning capacity and cognitive level are very high in its practical application. Therefore, carrying out actions such as molding the company's service according to the most common doubts and needs is possible. This Artificial Intelligence has a system that studies and understands humans and deals with specific interactions and user behaviors.
The name of this Artificial Intelligence lives up to its capabilities, as it is considered the most powerful machine capable of being conscious and autonomous. The reason is that, instead of merely replicating human behavior, ASI surpasses that capability, as it can think better and more skillfully than the human mind. However, this intelligence category continues to develop and improve, although it is already at an advanced stage.
ASI is an artificial intelligence model that has inspired movies to create realities in which robots act of their own free will and manage to dominate the world. However, this technology's real purpose is to be just as intelligent as demonstrated in the movie to facilitate human activities. The idea of ASI's development is focused on making robots better than humans in all tasks. Thus, the goal is for machines to be better athletes, scientists, artists, and even doctors.
Achieving this goal is a real thought because science focuses on creating systems that arouse emotions and personal desires. It is capable of detailed advanced reasoning of the consequences it can cause and works continuously and responsibly.
Developed by the company OpenAI, this is one of the most innovative types of artificial intelligence in recent years. It is a chat system based on the GPT-3 language and trained with a large amount of text, which can obey orders, manage tasks, text translations, or projects by itself and maintain real-time interactions with users.
ChatGPT is a complex system with almost 180 million parameters, allowing it to carry on conversations with anyone; its large text base will enable it to ask questions and provide logical answers within a wide range of commands. The most novel aspect of this AI is its reinforcement learning method, i.e., it provides information when the user uses it. It corrects it, so the tool learns to improve the satisfactory completion of tasks.
One of the significant innovations of this AI is that its algorithms are highly accurate and have a sense of context so that, when processing the user's request or question, it detects synonyms, adjectives, and other variations in the command phrase to provide coherent and complete answers, in natural language. This last feature has made it difficult to distinguish whether it is a text created by a person or an AI.
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Reactive machines are Artificial Intelligence characterized by being the most straightforward and oldest known. Because it is a model of technology with minimal capacity compared to the other models mentioned, the function they perform is quite simple. They only reproduce human behavior when stimulated since they only serve a reactive action.
As they have no memory capacity, reactive machines cannot learn or manage an internal database to execute what they absorb. For this reason, they only have a response role since they are automated to react to a given action. However, this limitation does not prevent them from being useful in several texts, although their use is gradually being phased out.
Undoubtedly, when reference is made to reactive machines, its great moment when it was tested in the 1990s is highlighted. A system with this technology is the one that defeated the chess champion Garry Kasparov in 1997; in this case, the specific machine was created by IBM called Deep Blue.
Memory-limited machines are entirely reactive but have the advantage of having a small amount of memory available. For this reason, they are more advanced since this function allows them to maintain continuous learning of data. Because every time they are exposed to that information, they can generate knowledge based on that content.
Regarding its operation, the machines create small databases based on the history of interactions. From this, they can make decisions to respond to a request or carry out any action. This type of Artificial Intelligence is widely used, but only at its base. The basis of limited memory intelligence is still used to govern the operation of facial recognition systems, virtual assistance, and chatbots, among others.
Theory of mind is a type of Artificial Intelligence developed extensively in recent years. It is possible to make a few references to its application or how far it will go in development. Still, it is believed to be one of the most innovative models because it aims to understand better the interactions to which it is exposed.
The function of the theory of mind is to address the emotions, needs, and thought processes present in the human mind. Despite all the progress made so far, this category still applies in the future. Experts are clear that many advances are still needed in other aspects of Artificial Intelligence.
Therefore, the theory of mind is considered a developing idealization, but it can potentially be one of the most relevant models. Apart from requiring the development of studies, such a process needs understanding factors of the human mind that provoke various feelings and reactions.
Self-awareness is characterized as only an ideal, as it is only a concept guiding the development of Artificial Intelligence. Although there are no concrete creations yet, in the future, machines will be self-aware. This is the highest level of development that Artificial Intelligence can reach, so it requires a lot of time and effort.
This AI aims to understand all emotions, generate feelings and understand in detail what happens to the users who interact with them. Although it looks pretty ambitious, it is undeniable that this is what scientists want to achieve. But, it has yet to be possible to establish how many years the technology can reach this level.
Moreover, there is no doubt that this stage of AI advancement can be dangerous. In general, independent machines will have reasoning that cannot always be stopped. Therefore, even though much remains to be done, self-awareness already has a clear idea among the types of Artificial Intelligence.
Nowadays, technology allows machines to act as human beings, and they are beneficial in providing solutions to various problems in the market. Therefore, this is the right way to allow other types of AI to arrive and impact the world.
Artificial Intelligence can be of great use to risk management processes and practices in multiple ways. Below, we mention the five most common use cases today:
AI in risk management can predict aspects such as sources of attackers, indicators of compromise, behavioral trends related to using cloud accounts, and attacks against cloud services. The data obtained by Artificial Intelligence on threats is aggregated and analyzed at scale with cloud-supported machine learning engines to process it to achieve probability and predictability models.
Due to the increase in cases of account hijacking and ransomware infections, security teams have opted for a faster and more efficient analysis system. This is where Artificial Intelligence can be of great use.
Companies generate large amounts of data and a diversity of events. For that reason, security teams must be able to recognize risk indicators quickly by analyzing patterns of events and detecting events related to the cloud.
Artificial Intelligence can be used to improve mass event information processing technology to develop more effective detection tactics. An example is Microsoft's Azure Sentinel service, which is cloud-based and AI-centric.
Fraud detection requires intense analysis processes by companies, and AI can fulfill this function, thus considerably reducing the workload of these processes. In this way, it minimizes fraud threats through machine learning models focused on text mining, social media analysis, and database searches.
When combined with predictive models at scale, AI in the cloud can be useful for text mining, database searches, social network analysis, and anomaly diversity detection.
Therefore, it can show the main challenges faced by the enterprise with financial and fraud technology, such as lack of analytics knowledge, the urgency of business protection-oriented solutions, lack of understanding of unstructured data, considerably high false positives, slow alert mechanism, lack of omnichannel coverage, and detection based on outdated rules.
Artificial Intelligence (AI) can process and evaluate data related to work operations in risky situations where mishaps are severe or even deadly. Before a problem arises, AI systems can evaluate different behavioral patterns to develop predictions to improve safety processes and prevent accidents.
AI-based cloud analytics engines can classify and sort all uploaded and cloud-generated information based on established regulations and monitor access based on content categories and trends recognized by the system. An example is Amazon's Macie service, which uses Artificial Intelligence.
AI-based risk management solutions can innovate and empower an organization's business activities. They are software tools that drive better performance and productivity while reducing the time and costs associated with the production process. But how do they do this? Integrated AI and ML technologies facilitate the automated capture, handling, and analysis of large amounts of data generated at every stage or activity at high speed.
In addition, the technology allows business organizations to increase their efficiency since its algorithms are based on machine learning from the history of operations to process and analyze the information and make decisions based on facts, significantly reducing the costs of their activities.
Having at hand a solution capable of capturing and accurately examining a large volume of timely data raises the level of competitiveness of the company because it allows the classification and evaluation of the different risk factors (customers, activities, channels of incorporation, areas, etc.) through backtesting, designing informed strategies to mitigate potential threats and financial losses.
The distinctive feature of artificial intelligence risk management tools is that they use automated learning algorithms based on the company's history of operations and risk data, which are compared with external variables (political or economical) to quickly build possible scenarios, detect the possibility of a risk occurring and measure the potential impact it would have on the organization. Unlike traditional methods, which are limited to examining the direct or linear effects of variables on the company, IA improves decision-making for risk mitigation, facilitating the creation of action strategies and, finally, implementing effective control methods according to the type of risk before it occurs.
One of the strengths of AI is that it reduces the time it often takes to manually extract data and variables to make a decision or implement a plan. Organizations that have integrated a platform with machine learning algorithms can process a large amount of data, perform deep analysis, and then classify and extract the different characteristics or variables. Properly defining or differentiating specific features helps to determine risk factors accurately and allows to implementation of a strengthened risk reduction model based on reliable data to reduce errors.
The level of detail and hierarchy that a data structure possesses and how each data field is divided and grouped help companies prepare for changes in the regular course of their business activities. The use of ML algorithms and a no-line perspective based on historical data allows the creation of an accurate model for detecting common trends or patterns in operations to detect unusual ones and anticipate risk.
AI risk mitigation solutions and programs immediately process the data generated in each activity and allow the organization and its members to track each in real-time to have maximum visibility of all systems and thus detect potential risks that may affect the healthy functioning of operations.
Implementing an AI tool within an enterprise requires members of the organization to pay close attention to detail for the safe use of the solution and the adequate protection of their data. To correctly illustrate how the process of adapting ML algorithms in the adoption of a robust risk mitigation model should look like and obtain real advantages, we present one by one the ideal steps to follow:
The first essential step is identifying the operational or reputational risks that could impact the organization. Must analyze the current operations, the framework of variables, and the values that govern the company's operation. It allows for determining the type of data to be captured and how it should be processed by the tool.
From the previous risk assessment results, the business organization can specify the correct category of data to be analyzed in the AI risk management model and which not. In other words, it allows us to discriminate within the massive volume of data those not helpful in the analysis and select those to obtain higher quality analytical results.
After selecting the user data, developing a robust risk management model is next. At this point, the organization must define which operations it wishes to examine and the level of transparency it expects in business activities. It is prudent to review the regulations of the country where the organization operates to see if there are regulatory limits on processing specific data.
Like any other business solution, an AI-based tool must be continuously reviewed and adjusted to ensure its effective operation; for this purpose, they offer performance reports that facilitate this work to make adjustments to the tool, the dynamics of the organization, and the evolving needs must be considered.
ML algorithms can process the large volume of data from each of the operations or business activities to create a predictive model of potential threats so that risk management team members can devise a strategy to prevent their materialization. These provide early warning signals to ensure the proper functioning of all the company's production and commercial operations and the protection of the company and its customers.
The efficient evolution of historical and current data through AI makes it possible to detect usual patterns linked to operations, such as the behavior of employees, partners, suppliers, and customers. AI technology makes it possible to see unusual changes, for example, a large amount in a money transfer, a change in dates, a deposit in a foreign country, etc.
Keep in mind three points before implementing ML tools:
The future outlook based on current trends reveals that business organizations will continue to invest in and adopt AI risk management solutions to streamline their operational processes and enhance the enterprise culture of risk prevention. According to Statista Machine learning market is expected to experiment significant growth from $140 billion to around two trillion U.S. dollars by 2030.
The use of these technologies and the level of trust they offer to both public and private sector organizations are expected to grow significantly. These emerging technologies based on artificial intelligence will become the number one option for combating cyber threats in the next five years.
Risk managers will take advantage of the benefits of deep analytics and automated processing of vast amounts of structured and non-structured data in seconds as AI and historical learning algorithms become more widespread to learn the critical elements in their operations and issue alerts to detect and mitigate risk, ultimately raising safety levels.
Despite the technical challenges of implementing AI technology, creating statistical models through algorithms will allow companies to reduce the manual workload. In addition, it will drastically accelerate compliance with regulations and operating standards and optimal processing and use of sensitive data and enable them to respond nimbly to threats. It will become a best ally and competitive advantage that will help the organization continue to operate in its market.
AI can become a crucial support for business organizations as it allows them to implement an efficient security and risk management system that correctly processes data, and risk indicators, automatically detect vulnerabilities in management processes and potential threats, and indicate the possibility of occurrence and the level of impact.
In Pirani, we provide a technological solution for risk management, which helps the company devise robust security models for its data and activities to optimize its performance towards more efficient processes. The platform centralizes and authenticates information and makes it available to all team members, in addition to allowing a customized configuration that meets the company's regulatory and security objectives.
One of Pirani's new features is that it offers ChatGPT integrations, which makes it a more accessible and intuitive tool as it guides risk management teams through precise interactions. Risk managers can request explanations on any problem, historical event, a concept, create an Excel formula, request to generate performance reports of the security system or devices connected to the platform, and activity summaries (determining the number of words), all from the data collected in the operations activities and previous communications that train it.
Please find out more about our new integration with ChatGPT and the advantages its adoption could bring to consolidate the risk management culture of your organization.
Artificial intelligence tools can become essential allies for business risk control and mitigation. Their incorporation into business processes will allow them to quickly adjust to changes in context or scenario and prepare efficient responses to political, economic, or financial threats. It will enable them to create a more agile decision-making process through the contribution of statistical knowledge with the adoption of an automated learning model, which detects patterns and shows reliable evidence quickly to justify them.
As time goes by, the elements that can represent a potential threat to companies vary and increase, so organizations must make a great effort to keep up to date to deal with new forms of risk: fraud, credit, changes in regulations, etc. AI could be the key to helping decipher and dismantle risk patterns more accurately in time. Despite handling a large scale of data, it can boost the analytical and predictive skills of risk management teams.
Deep analytics solutions enable business organizations and their members to respond proactively based on findings and common elements in their operational records before threats materialize, rather than reacting to an unexpected event for which they are unprepared. Creating efficient processes to reduce business costs and the chances of risk materialization are some of the significant advantages of adopting AI within a company.