The Application of Artificial Intelligence in the Field of Cybersecurity

Artificial intelligence is increasingly being used in almost every sector of business and industry globally. The adoption of artificial intelligence in the cybersecurity sector has also been influenced by this rise. The cybersecurity landscape has seen a tremendous shift as a result of AI. In today’s business contexts, there is a significant and quickly expanding surface for cyberattacks. This indicates that more than just human interaction is required for cybersecurity posture analysis and improvement within a company. Since these technologies can quickly analyze millions of data sets and find a wide range of cyber threats, Artificial Intelligence and Machine Learning are now becoming crucial to information security. Nowadays, AI is being included into a wide range of products and applications that are employed in effective threat identification and cyberattack prevention. This article discusses the foundational ideas of artificial intelligence, its function and applications in the field of cybersecurity, and how AI can be applied to enhance an organization’s overall security posture.

What is Artificial intelligence?

Artificial intelligence is the capability of a computing system to mimic the functions of human intelligence in order to carry out tasks that call for logical reasoning or the application of knowledge. Traditionally computers were designed to carry out instructions provided to them by people through the use of computer programs. Making computing devices capable of making decisions and carrying out various tasks without human intervention is the main goal of artificial intelligence. The development of artificial intelligence involves examining the operations and researching the patterns of the human brain. These evaluations result in the creation of intelligent systems, software, or AI-powered solutions.

Digital marketing, autonomous vehicles, home automation systems, virtual assistants, face recognition software, modern healthcare systems, and many other areas are some of the most prevalent applications of artificial intelligence in our daily lives. By automating the majority of jobs that a human being can complete manually, artificial intelligence is assisting companies all over the world in becoming more productive. Artificial Intelligence is also incredibly helpful to businesses because it can analyze vast volumes of data, look for trends, and extract valuable information. Due to these factors, companies all over the world are implementing AI to automate crucial business processes and spur corporate growth.

Important Terms Related to Artificial Intelligence

Artificial Intelligence is an umbrella term used for any branch of computer science that tends to create computer programs or devices that can collect and correlate data in order to learn and make decisions using logical reasoning. But there are other terminologies related to artificial intelligence that could seem to be interchangeable. As a result, it is critical to comprehend what machine leraning, deep learning, and artificial neural networks entail when discussing artificial intelligence. The specifics of each of these concepts are covered in this section.

Machine Learning

Machine learning is a subspeciality of the science of artificial intelligence. The focus of machine learning is to improve the performance of the computer program by continuously incorporating and learning from the data and using this data to improve its accuracy. Machine learning works in a similar fashion to how a human being gains knowledge over time and uses that knowledge to make improved decisions. The relationship between various entities is determined by the input data, which is also used to initiate the learning process in machine learning. Observations or data, such as examples, first-hand experience, or instructions, serve as the basis for machine learning. It searches for patterns in the data so that it can later draw conclusions from the presented examples. The main goal of ML is to make it possible for computers to learn on their own, without aid from humans, and to adjust their behavior accordingly.

Machine learning is a critical component of the study of data science. Data science is a field of study that works with enormous amounts of data utilizing contemporary technologies and methodologies to uncover hidden patterns, obtain valuable information, and make business decisions. Data science creates statistical models using sophisticated machine learning algorithms. Machine learning can be incorporated into data mining operations to assist organizations in identifying important trends or making valuable predictions as organizations all over the world gather, store, and process massive volumes of data. The decisions made as a result of these insights affect key growth indicators in the organization and are then used to drive businesses.

Deep Learning

Despite being two distinct concepts, machine learning and deep learning are occasionally used interchangeably. Both deep learning and machine learning are branches of artificial intelligence. Deep learning is a group of machine learning techniques that are used to represent high-level abstractions in data using model architectures. It is a member of a large family of machine learning techniques based on discovering representations for various kinds of data. Deep learning is a more sophisticated form of machine learning that mixes different algorithms and integrates the results to help the software continue to learn. Deep learning models can attain cutting-edge accuracy, occasionally outperforming human ability. A massive collection of labeled data and multi-layered neural network architectures are used to train models in the deep learning approach.

The primary distinction between machine learning and deep learning is how the underlying algorithm is applied to the learning process. A computer model learns to carry out categorization tasks directly from images, text, or sound using deep learning. It is not necessary to explicitly label the datasets while using deep learning. Deep learning, on the other hand, can make use of datasets in an unstructured manner and use its complex algorithm to automatically discern between various sets of data and classify them appropriately. In contrast to traditional learning methods that require human supervision for the learning process, deep learning consequently only needs minimal human involvement.

Artificial Neural Networks

Artificial neural networks are a collection of computer programs and algorithms that can replicate the biological neural networks seen in humans. Input and output are used by the computer to learn as data travels across these networks. The Artificial Neural Networks were built on the principles of the composition and function of biological neurons. Biological brains are able to deal with complex problems, yet each neuron can only manage a small piece of the problem. In a similar fashion, Neural Networks are made up of nodes (small distinct alogorithms), just like neurons in a human brain, that work together to produce the desired results. Each node in case of neural networks is responsible for handling a small fraction of the problem. In order to function well, a neural network may require millions or billions of computations, necessitating a much larger infrastructure. The layers of a neural network indicate the depth of the underlying computational network leveraged in a deep learning approach. Deep learning and artificial neural networks are credited with rapid development in fields including speech recognition, computer vision, and natural language processing.

The Role of Artificial Intelligence in CyberSecurity

In the guise of cutting-edge technology, artificial intelligence is significantly advancing the field of cybersecurity. The application of artificial intelligence in security solutions can assist companies in staying one step ahead of cybercriminals and enhancing their overall security posture in face of the sophistication of cyberattacks that are increasingly being directed at them. It is therefore imperative for security professionals to gain an understanding of how products utilizing AI work so they can use them to their maximum capacity in order to safeguard their organizations from different cyber attacks. This section goes over the role of Artificial intelligence in today’s cybersecurity landscape.

Task Automation

An organization can use artificial intelligence to automate complex security-related operations that would be difficult for human analysts to complete, giving them the opportunity to concentrate on other crucial projects. Some of the tasks that can be automated using AI include managing computing devices on the corporate network, performing asset inventory in an organization, responding to the high volumes of low-risk alerts and so much more. Security task automation carried out using artificial intelligence can also help organizations deal with the shortage of skilled security professionals in an effective way.

Learning from the Network/endpoint data

As was previously mentioned, security products utilizing artificial intelligence and machine learning technology learn from the organization’s network or endpoint data that is collected over time. In order to create a profile of typical business operations and activities over a specific time period, these security tools makes use of deep learning capabilities. These products then make use of this information to look for any irregularities or deviations from the normal that would indicate that a security incident is unfolding…

Detecting Zero-Day Threats

In order to circumvent or bypass the standard signature-based detection mechanisms employed by many enterprises, attackers are continually producing new malware. However, the sheer volume of routinely published zero-day malware cannot be effectively protected against by this kind of technology. Zero-day threats are unidentified threats that have the potential to seriously harm a network and an organization before they are discovered, recognized, and stopped. Artificial intelligence-based systems use complex algorithms to identify patterns of anomalous activity, detect malware, or spot even the smallest indications of ransomware or cyberattacks that could cause serious harm to the company even before they have a chance to infiltrate its systems. Instead of using signatures, systems driven by AI and ML may assess malware based on its inherent traits. For instance, if a piece of software is made to quickly encrypt a large number of files at once, it is strange behavior and may indicate a ransomware attack. Organizations such as FireEye, Google, and Microsoft are increasingly developing AI-based innovative solutions to carry out advanced malware detection.

Handling large volumes of data

An organization, even a small one, generates a lot of data every day in the form of system logs, network activity logs, operational logs and so much more. Sometimes, this data contains critical information that needs to be safeguarded from being intercepted, altered, or stolen by nefarious adversaries. Artificial intelligence-based systems may sift through vast amounts of data to find indications of harmful activity, assisting the company in identifying and thwarting potential threats.

Identification of threats

An organization’s SOC (security operations center) can be overwhelmed by the multitude of security alerts that are generated that end up being false positives. This can result in the time and efforts of the security analysts going to waste and prevents them from focusing on more complex tasks that require their attention. Artificial Intelligence-based security tools can help organizations by correlating disparate security events and allow for the identification of actual security threats in the organization. This enables the organization to have an efficient and timely response to security incidents.

The Current Use of AI in CyberSecurity Landscape

Artificial intelligence is being incorporated into different security tools to improve the threat detection process and provide superior protection to an organization’s network as well as endpoints. These tools can leverage deep learning and neural networks to make smarter decisions in order to effectively defend against different attack vectors. This section goes over some of the tools that incorporate artificial intelligence and are being adopted by a large number of organizations worldwide to avert security threats and strengthen their security posture. Some of these tools are given below:

UEBA (User and Entity Behavior Analytics)

A cybersecurity tool called User and Entity Behavior Analytics uses artificial intelligence to identify security problems and thwart online attacks. User and entity behavior analytics, or UEBA, is a type of cyber security solution that creates a baseline of users’ regular or expected behavior by observing a network over a certain period of time. These technologies then keep an eye out for any questionable user activity and instances where these normal patterns are violated while monitoring the business environment. By using executable signatures that have never been seen before but can be abnormal, so-called zero-day attacks can be defended against with the use of this security tool. Thus AI can prevent this because it is abnormal user behavior, or at the very least, it can notify someone about it.

IPS (Intrusion Prevention Systems)

An intrusion prevention system (IPS) is a network security tool that continuously monitors the organization’s network for malicious activity. It has the added capability of taking action to prevent, report, or block any malicious activity that deviates from the normal. Intrusion prevention systems make dynamic decisions based on network traffic or host-based behavior. In order to decide whether to allow some network traffic inside the corporate network, a network-based IPS must be able to read all network traffic. In comparison to a conventional firewall, which employs static rules, this approach is quite unique. Using information about traffic and threats, this security tool makes decisions in real-time making it very efficient in defending against cyberattacks.

SIEM (Security Information and Event Management)

Security information and event management (SIEM) technology aid in threat detection, compliance, and security incident management by collecting and analyzing security events as well as a wide range of other event and contextual data sources. This tool then correlates the data collected from these various sources to make decisions if these events add upto a security incident that requires immediate action.

Conclusion

Despite being incredibly effective at detecting and mitigating cyber attacks, AI-based security tools can have certain drawbacks. The deployment of these AI-based tools in an organization demand significant financial resources as well as efforts to streamline these technologies to meet the requirements of the company. In the absence of vast volumes of data and events, AI powered systems may provide erroneous conclusions and/or false positives. Therefore, it is crucial to be aware of these drawbacks so that these tools can be effectively utilized to give the best possible results. In spite of this, AI-based security products are very helpful for preventing cyberattacks and enhancing the organization’s overall detection and response process.

See also

Do you want to get practical skills to work in cybersecurity or advance your career? Enrol in MCSI Bootcamps