By Caique Barqueta: Artificial Intelligence (AI) is an area of computer science dedicated to creating systems that can perform tasks that normally require some kind of human intelligence. This includes skills such as learning, reasoning, problem solving, perception, language and pattern recognition.

There are a number of approaches and subfields within Artificial Intelligence, such as:

• Machine Learning: a technique that allows computers to learn from data, identifying patterns and making decisions with minimal human intervention;
• Natural Language Processing (NLP): aimed at machine understanding and interaction in human language;
• Computer Vision: ultimately involves processing and interpreting visual images, allowing machines to "see" and respond to the visual world;
• Robotics: Applies artificial intelligence to create machines or robots capable of performing complex physical tasks;
• Recommendation Systems: Algorithms that suggest products, services or information based on the user's preferences and previous behavior.

 It's worth noting that AI can range from basic systems, such as email spam filters, to more complex technologies, such as virtual assistants, medical diagnostics and autonomous vehicles.

To help you understand even more, we're going to cover some of the topics mentioned above, starting with Machine Learning.

Machine Learning

It uses algorithms trained on large amounts of data to answer questions and solve problems. Common algorithms include: "decision trees", "K-means" and "deep learning". The latter is notable for its ability to perform supervised, unsupervised and reinforcement learning tasks.

Deep learningAn advanced form of machine learning that performs more complex tasks, it is based on neural networks, inspired by the functioning of the human brain, with many artificial neurons in several layers, which explains its "deep" nature.

Supervised learning: The most widely used technique today, employing human-labeled data for training and testing, ensuring the accuracy of the algorithm's responses. It is useful for predictions, probability analysis and classification of images and other data.

Unsupervised learning: Works with unlabeled data to explore potential results, looking for unknown information. This includes clustering (by customer profile, behavior, etc.) and data associations, helping to identify patterns and problems.

Reinforcement learning: It uses positive and negative reinforcement, learning from mistakes and successes to make decisions or choose the best paths. It is applied, for example, to content and advertising recommendations, adjusting to customer preferences based on their consumption.

Natural Language Processing (NLP)

It is the field dedicated to linguistic processing, recognizing meanings in text and speech, which allows communication with humans. This technique is widely used in chatbots, translators and virtual assistants such as Siri, Alexa and Google Home.

It involves interpreting and generating human language in a way that computers can understand, process and respond to. NLP makes it possible for machines to read text, listen to speech, interpret it, measure feelings and determine which parts are important. It is therefore essential to create interfaces that allow natural and intuitive interaction between humans and machines.

Chatbots are algorithms programmed to allow machines to interact and converse with humans. They use and learn from previous conversations to improve their responses over time.

Computer Vision

This is the area that seeks to give machines the ability to "see", allowing them to visually identify and interpret the world around them. It is used to recognize images and videos and extract similar data, patterns and information from them.

A practical and widely used example is facial recognition, which allows machines to identify and authenticate individuals based on their facial features.

This field combines machine learning methods, image processing and patterns and is crucial in many applications, from security to medical analysis and augmented reality interactions.

Robotic Process Automation (RPA)

It refers to the automation of specific tasks and processes, which can involve different files and even systems.

For example, it can include filling in documents, reports and spreadsheet data; copying data between and within forms; detecting payment for issuing invoices and so on.

RPA is useful for mechanical and repetitive processes that are traditionally carried out manually and in which there is little or no variation. It ultimately simplifies routine tasks and can significantly increase operational efficiency, reducing errors and freeing up human resources for more complex and creative tasks.

Recommendation Algorithm

It's an algorithm that makes recommendations for content, products and services based on user profiles. To work effectively, the algorithm needs to be fed data, which is categorized to identify patterns, taking into account aspects such as gender, age and social class.

Based on this, it analyzes, depending on the case, consumption habits, content consumed, among others, to make new recommendations.

To keep it up to date, it needs a considerable amount of data storage and processing capacity.

Examples include movie, series and music streaming apps, social networks or stores that suggest products that might interest the user.

A company's use of the algorithm allows it to reach its target audience more precisely.

Here we have the risk that the use of insistent, poorly targeted contact or without clear consent in the collection of data could cause a certain amount of discomfort for the organization.

How can AI help cyber security?

Artificial Intelligence (AI) can play a crucial role in improving cyber security, offering advanced and efficient solutions to tackle digital threats. As an example, we present some of the ways in which AI contributes to cyber security:

• Threat detection and behavior analysis: AI can analyze network traffic patterns and identify suspicious activity that indicates the presence of malware, phishing attacks or other cyber threats.
• Automated Incident Response: In the event of a threat being detected, AI-based systems can respond automatically to contain or mitigate the attack. This can include disabling compromised accounts or isolating parts of the infected network, speeding up the response and reducing the impact of attacks.
• Vulnerability Analysis: AI can analyze large amounts of code to identify security vulnerabilities in software and systems. This helps organizations correct flaws before they are exploited by hackers.
• Authentication and Access Control: AI-based systems can improve user authentication through biometric recognition, such as facial or voice recognition, offering an additional layer of security over and above traditional passwords.
• Predicting Future Threats: With the ability to analyze trends and data on a large scale, AI can predict emerging threats, allowing organizations to prepare and strengthen their defenses against future attacks.
• Security in IoT and Smart Devices: As more devices connect to the internet, AI can help monitor and protect these devices from vulnerabilities and cyber attacks.
• Training and Security Awareness: AI can be used to develop customized training programs for employees, focusing on the areas of greatest risk and ensuring that teams are prepared to face cyber threats.

Good safety practices for the use of AI

To ensure effective cybersecurity when using Artificial Intelligence, it is essential to adopt good practices that address both the security of AI systems and protection against threats that can be amplified or exploited through AI.

• Data Protection and Privacy: ensure that the data used and generated by AI systems is protected, as well as that imputed to AI tools by checking the platform's privacy policies and verifying compliance with data protection standards. Develop an AI-focused culture covering issues such as data protection and cybersecurity;
• Verification before implementing AI: it is necessary to analyze what the intended objectives are, what strategies will be adopted to achieve them, what tools and infrastructure are available, what the level of qualification and training of the team that is already part of the company is and, above all, what problems are faced throughout the processes;
• Access Management: it is highly recommended to restrict access to AI systems to authorized users only, implementing robust authentication and authorization controls to prevent unauthorized access.
• Continuous Monitoring: continuously monitor AI systems to detect suspicious or abnormal activity, such as unexpected changes in model behavior or unauthorized access attempts.
• Regular testing and validation: carry out regular security tests, such as penetration tests and vulnerability assessments, to identify and correct security flaws in AI systems.
• Training and Awareness: it is necessary to educate team members about the security risks associated with AI and provide regular training on how to identify and respond to threats.
• Application of ethical and governance principles, where ethical and governance principles are applied and implemented to ensure that AI is used responsibly and safely, respecting individual rights and avoiding undue bias.

How can AI be used for cybercrime?

Artificial Intelligence (AI), while having many beneficial applications, can also be used for malicious purposes, as AI has a great capacity to process large volumes of data, learn and adapt quickly, and automate complex tasks making it a powerful tool that can be exploited in harmful ways.

• Cyber attacks: AI can be used to develop more sophisticated malware, carry out customized phishing attacks, or automate the discovery of vulnerabilities in security systems;
• Disinformation and Fake News: AI algorithms, such as adversarial generative networks (GANs), can create extremely convincing deepfake videos and audio, which can be used to spread disinformation or defame individuals;
• Invasive Surveillance: AI can be employed in surveillance systems to monitor, identify and track individuals in an invasive manner, raising serious concerns about privacy and human rights.
• Market Manipulation and Financial Fraud: AI can be used to analyze and predict market movements in ways that can be exploited for market manipulation, insider trading or to carry out complex financial fraud.

As an example, we had cases in which tools were identified that were created using Artificial Intelligence, such as WormGPT and Fraud Bot using ChatGPT.

Artificial intelligence (AI) is a driving force behind profound changes in contemporary society, with implications that range from improving quality of life to optimizing industrial and administrative processes. Its use has enabled advances in critical areas such as health, where it helps in the early diagnosis of diseases, education through adaptive learning platforms, and in the service sector with the personalization of consumer experiences.

AI's ability to analyze large volumes of data at high speed allows for more informed and efficient decision-making, which can result in smarter systems and safer, more sustainable urban environments.

However, along with the benefits come significant risks, since AI that can be used to protect information systems could also be employed by cybercriminals to develop more sophisticated attacks, such as malware that learns and adapts to circumvent cyber defenses.

There is also the danger that for algorithmic bias, AI systems can perpetuate and amplify human prejudices if they are not properly supervised. Privacy is also another concern, as AI ends up relying on a set of data, often personal, which can lead to abuse without proper legal and ethical safeguards.

While AI has the potential to make significant progress, it is also necessary to implement robust cybersecurity policies, as well as strict ethical considerations, to ensure that its use is beneficial and safe for everyone.

References

• Heimdall by ISH Technology