Artificial intelligence (AI) is critical to the future of cybersecurity. It multiplies forces, increasing the power of security systems. Large datasets can be quickly analyzed by AI, which can also spot trends and abnormalities that could be signs of impending danger. A subset of artificial intelligence called machine learning algorithms can adapt and learn from new data, which enhances their capacity to identify and reduce potential dangers. AI also helps automate repetitive processes, freeing cybersecurity experts to concentrate on more difficult problems.

The cyberattack surface in current enterprise setups is large and growing at a rapid pace. This indicates that human engagement alone is insufficient to analyze and improve an organization’s cybersecurity posture. Information security is increasingly dependent on artificial intelligence (AI) and machine learning (ML), as these technologies can quickly analyze millions of data sets and identify a wide range of cyber threats, from malware threats to suspicious behavior that could lead to a phishing attempt.

John McCarthy first used the phrase “artificial intelligence” (AI) in 1956 at an event on the topic. The field of computer science known as artificial intelligence (AI) focuses on creating intelligent computer systems that resemble human intellect. Intelligent systems can now handle new tasks because of machines’ capacity for natural language processing, learning, and planning.

The primary goal of AI is to emulate human cognitive function and do tasks that a human would normally perform. AI is a separate, autonomous electronic entity that performs tasks similar to those of a human healthcare professional. Artificial intelligence (AI) is being used in many aspects of our daily lives today, including computer games, automated public transit, computer assistants, driverless cars, companion robots, facial recognition at passport checks, and computer games. AI systems are becoming increasingly adept at data analysis.

AI refers to a collection of computing models and algorithms rather than a single technology. Expert systems, fuzzy logic, artificial neural networks (ANNs), machine learning, deep learning, natural language processing, computer vision, and robotics are some of the key fields in artificial intelligence.

The following are some of the different computer-based instruments or technologies that have been employed to fulfill AI’s objectives:

Expert Systems :  An expert system (ES) (also known as a knowledge-based system) allows computers to make judgments by understanding data and comparing alternatives in the same way that a human expert would. It makes use of a method called rule-based inference, which processes data according to rules.

Neural Networks : After being trained, these computer programs can identify objects or patterns. Artificial Neural Networks (ANNs) are distributed systems that operate in parallel and are composed of neurons, which are processing units that perform certain mathematical functions. Nonlinear relationships that are immediately learned from the data being modeled are represented by the ANN model. Applications of neural networks to healthcare are being investigated, including risk analysis, lifestyle management and monitoring, virtual health support, imaging and diagnosis, and health information management.

Natural Language Processors : Computer programs that translate or interpret spoken language in everyday situations. To enhance and improve structured medical data, natural language processing (NLP) approaches to extract information from unstructured data, such as clinical notes. Applications of NLP include text analysis, speech recognition, translation, and other language-related objectives. Semantic and statistical approaches are the two fundamental methods in NLP. The largest industry using NLP techniques is healthcare.

Robots : Computer-programmable devices with physical manipulators and sensors. The deployment of intelligent robots in the healthcare industry improves hospital operating efficiency, diagnosis accuracy, and patient satisfaction. Medical robots can support a variety of tasks, including assisted living, social contact, surgery, and rehabilitation. In spine surgery, robotic guiding is becoming more prevalent.

Fuzzy Logic : Reasoning based on inaccurate or partial information using a range of values rather than point estimations. Fuzzy logic addresses knowledge uncertainty by simulating human reasoning with imprecise or ambiguous facts. The fuzzy model can withstand impressions and is resistant to parameter changes.

Machine Learning : Reasoning using a range of values rather than point figures when dealing with confusing or incomplete information. Fuzzy logic addresses knowledge uncertainty by simulating human reasoning with incorrect or confusing facts. The fuzzy model is capable of handling observations and is stable to parameter changes.

Deep Learning  : A branch of machine learning that is constructed using a deep architecture of layers, each layer handling a separate aspect of a challenging issue. By including many processing layers, it seeks to increase the ability of supervised and unsupervised learning algorithms to solve challenging real-world situations. Figure 1 shows an example of deep learning with two hidden layers. The connection between deep learning, machine learning, and artificial intelligence.

DataMining: This is the process of gleaning fresh information and patterns from massive databases. Numerous algorithmic tools, including statistics, neural networks, fuzzy sets, regression models, and evolutionary models, are displayed in data mining. Every AI tool offers benefits of its own. It is advised to use a mix of these models as opposed to just one. AI technologies are having a significant impact on consumer experience and the retail sector. Because of the speed at which threats are emerging, both the public and private sectors are paying more attention to the use of AI technology for cybersecurity activities. 

1.2.1 The Role of AI in Cyber Security:

Does AI represent cybersecurity’s future?

Businesses in the public and private sectors have already embraced AI initiatives, and as the White House points out, numerous federal agencies also make use of the technology. Why? Why? AI is capable of saving a lot of time and resources by skimming through standardized data and thoroughly examining unstructured data, numbers, voice patterns, and words. AI has the potential to protect national secrets as well as tax cash. There are also gaps. Hackers are attempting to discover ways to get inside the devices by finding security gaps that we were unaware of. Before a business discovers a data leak, years have already passed.

By then, the hacker has vanished, along with all of the sensitive data. Conversely, AI has to wait till a hacker gets dirty and just gathers data. AI looks for a variety of behavioural irregularities that hackers might exhibit, such as when a user logs in or when a password is written. Artificial intelligence (AI) can identify subtle indicators that a hacker group would have missed otherwise and halt them in their tracks. As Varughese pointed out, anything may be misused. In the ongoing cybersecurity chess game, human hackers will always probe the weak points in any system, including artificial intelligence. Because artificial intelligence is controlled by humans, it can still be defeated.

AI can only work the way it was intended to, despite its amazing ability to link and process data. Programmers will need to implement new defences as hackers adapt to Artificial Intelligence systems. The cat and mouse game will continue, but artificial intelligence is a useful ally in the struggle to protect data. For Tensor Flow machine learning, Google unveiled a graphical data learning approach. Search results for March 9, 2019 implemented Neural Structured Learning (NSL), an open-source framework for training data sets and data structures in neural nets using the Neural Graph Learning technique. NSL is intended for qualified machine learning specialists in addition to those who lack expertise, and it integrates with the Tensor Flow stage of machine learning. In addition to performing NLP and projecting data from interactive databases like medical reports or information graphs, NSL can also render machine vision models.

Speech recognition apps (e.g. Siri), Google’s search app, and Facebook’s facial recognition technologies are all examples of artificial intelligence. AI is frequently used by payment card manufacturers to help investment banks thwart reported fraud worth trillions of dollars. However, how is their information security being applied? Does artificial intelligence present an advantage or a problem for corporate digital security? On the one hand, contemporary information management architecture is useful since it makes it easier for safety practitioners to assess, investigate, and comprehend cybercrime. It fortifies the digital management techniques that businesses employ to combat cybercrime and aid in maintaining the security of their clients and operations.

On the other side, artificial intelligence might require a lot of resources. That may not be achievable in any scenario. In reality, it might also be a potent tool in the toolbox of cybercriminals who use technology to enhance and magnify their cyberattacks. Regarding information security, the discussion surrounding artificial intelligence was not very noteworthy. After all, the core of current developments in cyber safety is information. But how better to evaluate the data than using computers that can process information in nanoseconds and do jobs that would take humans far longer? AI is becoming a rapidly-evolving area of focus for the computer safety community.

We will examine the developments in AI security technologies and how the technology affects consumers, businesses, and cybercriminals. Let’s resolve everything. Why does internet security increase more with automated information protection protocols? You have a range of security layers in place, including border, network, edge, device, and computer storage, if you’re like many growing enterprises. For example, you may have hardware or software firewall restrictions in addition to network security solutions that monitor and identify which connected devices are allowed while avoiding others. The antivirus and malicious solutions will be up to the hackers if they manage to get past these security measures. They might then encounter IDS/IPS systems, etc.

What will happen, though, when cybercrime surpasses some defences? You are in danger when the only means of knowledge security are human-based surveillance capabilities. After that, cybercrime shouldn’t match your vulnerability to cyber protection nor should it follow a set schedule. You must be able to recognize, locate, and act upon threats in real time, every day of the year. IT organizations should be capable and prepared to respond quickly, regardless of vacations, work hours, or even when employees are just not accessible. Information protection technologies driven by artificial intelligence were built to defend you continuously. Artificial Intelligence can react to cyber threats in microseconds when humans might need minutes, hours, days, months, or even years to identify them.

What do AI executives believe about the use of AI in information security?

Aside from their research paper “Reinventing Cyber Protection with AI,” which demonstrates the significance of using AI to establish cybersecurity defenses for businesses, the Capgemini Research Institute looked at the state of information protection. Part of the reason for this is that 850 leaders in data security, IT information management, and IT operations from ten different countries who participated in the poll believe AI-enabled solutions are crucial because criminals are already using them to launch cyberattacks. The following are some of the report’s other principal points: According to 75% of study participants, artificial intelligence (AI) makes it possible for their company to react to violations faster.

According to 69% of firms, artificial intelligence is necessary. According to three out of five businesses, employing AI improves the accuracy and productivity of cyber analysts. Artificial intelligence may even be able to improve the viewpoints of cybersecurity solutions that already exist and pave the path for the development of new ones. AI will significantly strengthen the enterprise’s security defenses as networks get larger and more complex. Simply put, human capabilities are limited by the ever-increasing sophistication of these networks. It’s okay to acknowledge that; there’s no reason to be fearful. But it raises an important question: What steps do you take to guarantee the security of sensitive information and customer knowledge about your business?

How might artificial intelligence technologies strengthen your defense?

Nothing that successfully integrates artificial intelligence technology with the existing information defense networks can be accomplished quickly. As one might anticipate, time is needed for planning, training, and setup in order to make sure that the personnel and programs make the most of it. In a Forbes article, Naveen Joshi, the founder and CEO of Allerin, explains how AI systems can guarantee the sustainability of cybersecurity operations in a variety of ways. Among those characteristics are:

• Creating an accurate biometric password-based log-in method or methods
• Risk and suspicious activity detection with predictive analysis
• Better comprehension and reasoning with natural voice recognition establishing a necessity to secure connection and identity

After you’ve integrated AI into your information defense systems, your information intelligence experts and other IT management personnel will need to understand how to use it effectively. which necessitates preparation and time. Take care to ensure that the human element of the organization is not overlooked. If you look around the business, a lot of major players now include AI into their offerings. The use of artificial intelligence in cybersecurity is being used by a number of well-known industry leaders, including Palo Alto Networks, Crowd Strike, Check Point, Fortinet, Log Rhythm, Fire Eye, Sophos Symantec, and others. Artificial intelligence has many benefits for knowledge security, but there are also hazards to consider. Applying AI to information defense tends to require more time and money than conventional, non-AI computer protection methods, which is one of the major challenges.

Taking care of the weaknesses that AI cybersecurity tools create

Physical protection is facing additional difficulties as a result of the use of AI in information defense. While it’s crucial to deploy AI technology to identify and neutralize malware threats, hackers may also employ these tools to launch progressive behavior attacks. In part, this is due to the fact that, as the costs of developing and implementing these advancements decrease, access to sophisticated AI technologies beyond machine learning methodologies is increasing. This guarantees that hackers can create increasingly complex and effective malicious programs faster and for less money. The combination of factors makes one vulnerable to misuse by cybercriminals.

Adversarial AI: How cybercriminals might abuse AI to target different companies

Artificial intelligence (AI) poses a threat to information security when it comes to hostile AI, a term that is used maliciously to describe the expansion of AI use. Adversarial artificial intelligence, according to Accenture, is what “causes machine learning algorithms to misunderstand inputs into the framework and respond in a way beneficial to the intruder.” In essence, that happens when intentionally altered inputs trick neural networks within an AI computer into misidentifying or inaccurately portraying items. The use of cyber security may be almost limitless if the proper safeguards or precautions are not in place. Thankfully, cybersecurity experts are aware of the dangers posed by aggressive AI. As stated in a post on IBM’s research blog for Security Intelligence, they are “building protections and making pre-emptive assault models test AI weaknesses.” In addition, IBM’s Dublin labs are actively involved in the project and have contributed to the ill-disposed AI index of the IBM Adversarial Robustness Toolbox (ART).

https://iopscience.iop.org/article/10.1088/1742-6596/1964/4/042072/pdf

1.2.2 AI’s benefits for cyber security

Through the use of sophisticated algorithms, AI systems are being trained to recognize patterns, identify malware, and identify ransomware attacks or malware even before they infect a system. Artificial intelligence (AI) can offer more predictive intelligence by using natural language processing to automatically select content by scanning through news, articles, and studies on cyber dangers. According to Tech Republic, a mid-sized company receives notifications for almost 200,000 cyber events every day. This volume of attacks would be too much for the security personnel of any regular corporation. Consequently, some of these threats will cause serious network damage without being detected. Security professionals need a lot of assistance from smart equipment and cutting-edge technology like artificial intelligence (AI) in order to function efficiently and defend their organizations from cyber-attacks.

 Artificial Intelligence Can Manage Massive Data Sets:

There is a lot of activity on a company network. A typical mid-sized business has a lot of traffic. That suggests that a lot of data is shared on a daily basis between the organization and its customers.  This data needs to be protected from malicious individuals and software.  Cyber security professionals can’t, however, scan every piece of data for dangers. Artificial intelligence is the most suitable option for detecting threats that seem as routine tasks. Because it is automated, it can sift through a large volume of data and traffic. You can transport data with the aid of AI-powered solutions, such as a tailored proxy. In addition, it is capable of spotting and identifying any dangers that could be present among the confusion.

Repetitive Procedures Diminish:

As mentioned earlier, attackers often change their techniques. Conversely, the core security procedures remain unchanged. If you hire someone to perform these tasks, they can grow bored and put your network at risk.  Artificial intelligence handles repetitive cyber security tasks that could tire out your cyber security personnel while mimicking the best aspects of human nature and omitting its shortcomings. It regularly assists in the identification and mitigation of core security threats. Additionally, it thoroughly examines your network to see if there are any security holes that could endanger it.

Detection and response times are improved:

The first step in safeguarding your company’s network is identifying hazards. If you could identify problems like unreliable data right away, that would be great.  It will shield your network from damage that won’t go away. The best way to identify and stop assaults in real time is to combine AI with cyber security.  Artificial intelligence (AI) looks for threats throughout your entire system. Unlike humans, artificial intelligence (AI) will simplify your security operations and detect dangers early.

Protection of Authenticity:

Most websites include a user account feature that enables visitors to log in and use services or make transactions.  Certain websites need users to fill out contact forms with their personal details. As a business, you will require an additional level of protection because such a site contains sensitive material and private information. The increased security layer will ensure your visitors are safe when using your network. When a user tries to connect to their account, AI secures authentication. AI employs a range of methods for identification, including fingerprint recognition, face recognition, and CAPTCHA. You can use the data these features provide to evaluate if a log-in attempt is legitimate. Hackers use brute force attacks and credential stuffing to get into business networks.