Every aspect of our daily lives has been influenced by artificial intelligence. Thousands of tech companies have now developed cutting-edge AI-powered cybersecurity defence solutions that have been specifically designed and programmed by ethical hackers and penetration testers. The artificial intelligence used in such solutions aids in the prevention of cyberattacks by predicting potential risks.
Thousands of tech companies have created cutting-edge AI-powered cybersecurity defence solutions that have been designed and explicitly programmed by ethical hackers and penetration testers. The Artificial Intelligence used in these solutions aids in the prevention of cyber-attacks by anticipating potential threats before they occur. As a result, the use of artificial intelligence in ethical hacking is critical.
We shall now dive into AI and cyber security, and then compare Artificial Intelligence vs Ethical Hacking. Also, they both come into play as a single unit and how ai vs cybersecurity differs. Besides, there are some challenges discussed in these jobs to give you an idea of what to expect from these roles.
What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. The term can also refer to any machine that has human-like characteristics like learning and problem-solving.
AI employs a variety of technologies, including Machine Learning (ML), Deep Learning, Neural Networking, and Natural Language Processing (NLP), to create machines that can learn on their own and mimic human actions to perform various tasks.
Basics of AI
1. Assisted Intelligence
It is the most primitive level of AI-assisted intelligence and is primarily used to automate simple processes and tasks by leveraging the combined power of Big Data, the cloud, and data science to aid in decision-making. Another advantage of assisted intelligence is that it frees people up to perform more in-depth tasks by performing more mundane tasks.
Assisted intelligence only works with clearly defined inputs and outputs because it requires constant human input and intervention. The primary goal of assisted intelligence is to improve what people and organisations are already doing; thus, while AI can alert a human to a situation, the final decision is in the hands of end-users.
2. Augmented Intelligence
This is the next level of AI. It is primarily concerned with the role of technology as an aid. This cognitive technology is intended to supplement, not replace, human intelligence. When discussing AI in general, this "second-tier" AI is frequently mentioned, with machine learning capabilities layered over existing systems to augment human capabilities.
Augmented intelligence enables organisations and individuals to do things they could not do otherwise by assisting human decisions rather than simulating independent intelligence. This category includes models such as machine learning, natural language processing, image recognition, and neural networks.
The primary distinction between augmented and assisted intelligence is that augmented intelligence can combine existing data and information to suggest new solutions rather than simply identifying patterns and applying predetermined solutions.
3. Autonomous Intelligence
Autonomous intelligence is the most advanced form of AI, in which processes are automated to generate intelligence that allows machines, bots, and systems to act autonomously, without human intervention.
Autonomous intelligence, once thought to be the stuff of science fiction, is now a reality. The idea is that, like humans, AI requires autonomy to reach its full potential. While autonomous intelligence applications are growing in popularity, organisations are not yet — and may never be — ready to give machines complete control. With this in mind, AI should be given autonomy only within strict lines of accountability — a belief that stems in part from the aforementioned sci-fi depictions.
Furthermore, autonomous intelligence is not appropriate for all applications, particularly those where the best outcome is difficult to quantify.
Subsets of AI
AI are divided into subsets, each with its own set of responsibilities and capabilities. These subsets collaborate to create an AI-implemented task for you. We will now look at each subset:
1. Machine Learning
Machine learning is the study of algorithms that learn from examples and experiences. Machine learning is based on the idea that some patterns in data were identified and used to make future predictions. The difference between hardcoding rules and machine learning is that the machine learns to find such rules.
2. Deep Learning
Machine learning has a subfield called deep learning. Deep learning does not imply that the machine gains more in-depth knowledge; rather, it employs multiple layers to learn from data. The number of layers in the model represents the model's depth. For example, the Google LeNet image recognition model has 22 layers.
3. Natural Language Processing
A neural network is a collection of interconnected I/O units, each with its weight associated with its computer programs. It aids in the development of predictive models from large databases. The human nervous system serves as the foundation for this model. This model can be used to perform image understanding, human learning, and computer speech, among other tasks.
4. Expert Systems
An expert system is a computer-based decision-making system that is interactive and dependable, and it solves complex decision-making problems using facts and heuristics. It is also regarded as the pinnacle of human intelligence. An expert system's primary goal is to solve the most complex problems in a specific domain.
5. Fuzzy Logic
Fuzzy logic is defined as a many-valued logic form with truth values of variables ranging from 0 to 1. It is the partial truth handling concept. In real life, we may be faced with a situation in which we are unable to determine whether a statement is true or false.
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What is Ethical Hacking?
Ethical hacking is the process of detecting vulnerabilities in an application, system, or organisation's infrastructure that an attacker can exploit. They use this method to prevent cyberattacks and security breaches by lawfully hacking into systems and looking for flaws. To gain authorised access and test the organisation's strategies and network, an ethical hacker follows the steps and thought process of a malicious attacker.
Basics of Ethical Hacking
Ethical hacking is made up of three fundamental concepts that will be discussed below:
It specifies the rules that govern information access. Confidentiality refers to the measures taken to prevent cyber attackers and hackers from accessing sensitive information.
This ensures that the data is consistent, accurate, and reliable over time. It means that data in transit should not be changed, altered, deleted, or accessed illegally.
All necessary components, such as hardware, software, networks, devices, and security equipment, should be maintained and upgraded. This will ensure that data is available and accessible without interruption. Also, sufficient bandwidth is provided to ensure constant communication between the components.
Challenges Faced by Ethical Hacking
Although ethical hacking as a profession has a lot of benefits, it also comes with several challenges which we will discuss below:
1. The field is very active and necessitates ongoing research
To be an effective thing breaker, you must constantly read published work from colleagues and learn new techniques. Sometimes simple reports, such as entering nothing into the username and password fields on Intel's Management Engine, give you full access to a PC. Others, such as RowHammer, are extremely complex. Effective hacking is frequently an art form that involves locating multiple different types of problems and chaining them together to create a full vulnerability that necessitates a patch. There is a lot of reading, learning, adapting, and experimenting.
2. Managers see security as a line item expense
When you work on a security team for a large corporation, security frequently takes a back seat to almost everything else. It is regarded as an expensive form of risk management and public relations. Security teams, in the eyes of (responsible) managers, are costly and slow the development of new products. Neglected budgets frequently result in layoffs, and talented individuals frequently have multiple companies on their resumes/CVs.
You'll find yourself debating the severity of a problem and whether development time should be spent on addressing issues. Conflicts with managers frequently cause unnecessary stress, and the differences can often lead to the need to look for a new job.
3. Disclosure of Vulnerabilities and Response to Hostile Entities
The biggest debate for researchers is vulnerability disclosure to third parties. Depending on the organisation, responses can range from interesting engagement and proactive problem solving to dismissive and uninterested, to actively hostile and ready to pursue legal action.
4. Responsible disclosure
The vast majority of white-hat researchers practice responsible disclosure. This means that you conduct research on a piece of software, discover a problem, and notify the vendor privately. The developers who maintain the software are given a time frame to respond and fix the problem; once patching is completed, the vulnerability is publicised and everyone is advised to update.
5. Everyone benefits
This arrangement works well if the organisation with which you are dealing is not hostile and operates ethically. Often, companies respond to reports extremely slowly, leaving their customers vulnerable to attack. Sometimes the company chooses to do nothing and advises the researcher not to disclose the problem.
Now that we have understood both cyber security and artificial intelligence in detail, we will now compare cybersecurity vs artificial intelligence in the next section.
Difference Between Ethical Hacking and Artificial Intelligence
Deciding which is better between ai vs cybersecurity can be difficult. Before you get started with AI or ethical hacking, it is important to know the difference between the two. As both of these careers are in demand, there is a lot of earning potential in both fields. However, you must consider checking cyber security vs ai salary to know the current market and the payroll.
Check the comparison chart to make a good choice between ethical hacking vs artificial intelligence career:
The goal of ethical hacking is to find system vulnerabilities and report them to the owner.
The goal of AI is to improve people's lives, increase productivity, and relieve people of mundane tasks.
It cannot become an existential risk.
It can become an existential risk if it is realised to its fullest.
It has a lot of data points.
It can be used to manage data points.
Networking is the required field of expertise for it
Coding and algorithms are needed.
Note: There is not much difference in monetary terms in these job profiles. Both the artificial intelligence vs cyber security salary are good and pays well. If you are looking to learn cyber security and get placement in your dream company, enrol in Knowledgehut’s cyber security certification course today!
How is Artificial Intelligence Used in Ethical Hacking?
Despite the differences between cyber security vs artificial intelligence, they combine to eliminate complexities and improve security. The three most significant characteristics of AI are its ability to learn, adapt, and generalize. Because of these characteristics, AI is required in ethical hacking activities. The main reason for this is that black hat hackers' strategies, tactics, and actions are constantly changing. As a result, white hackers must adapt to identify and address issues and vulnerabilities continuously. Here are some examples of how ethical hackers can use artificial intelligence:
1. Analysing user activities
Sending phishing emails to all employees is one of the activities that ethical hackers engage in to test their awareness and attention. Phishing emails are commonly used by hackers to obtain sensitive information or gain unauthorised access to a system. Through promises, threats, or both, a phishing email entices users to click on a link or visit a web page. Following that, either malware is installed and executed, or confidential information is requested.
When a phishing awareness campaign is completed, user responses are analysed, and policies and procedures are revised as needed. Ethical hackers can identify the characteristics of users who are more likely to become victims of phishing and, as a result, pose a high risk to the organisation by using machine learning, specifically decision trees.
2. Identifying new attack patterns
Currently, all computer network management tools include a logging mechanism. Log files are rich data sources that record and display user activities, network access attempts, system, and application issues, and so on. When a successful attack occurs and is later detected, its characteristics, such as origin, time, hardware type, and so on, are recorded in log files. Ethical hackers can identify and address specific vulnerabilities by analysing those attacks using decision trees, a type of machine learning.
3. Increasing cyber defence accuracy
It is common knowledge that no cyber defence is completely effective. There will always be instances where legitimate access is classified as hacking and blocked; so-called false positives or actual intrusions are allowed to pass through – false negatives. These occurrences accumulate over time, and a library of them exists in an organisation. It is possible to build and train a parametric model of an intrusion using neural networks, a type of machine learning. Following that, the neural network will automatically determine whether network access is legitimate or not, improving the overall accuracy of ethical hackers' activities.
Furthermore, a neural network-based system will be highly adaptable. This adaptability will be achieved by incorporating all new errors into a training dataset and retraining the neural network. This approach is valuable because both black hat and ethical hackers are constantly changing their actions to remain effective.
Top Three Use Cases for AI in Cybersecurity
1. Detecting Anomalies
One of the most effective applications of machine learning in cybersecurity is sophisticated pattern detection. Cyber attackers frequently conceal themselves within networks and avoid detection by encrypting their communications, using stolen credentials, and deleting or altering logs. However, a machine learning algorithm designed to detect unusual behaviour can catch them in the act. Because machine learning excels at identifying patterns in data, it can detect activity that traditional approaches miss.
2. Detecting Advanced Malware
Traditionally, malware detection has involved monitoring and searching network traffic for signature matches, or similarities to known indicators of compromise. Deep learning, on the other hand, allows for the analysis of massive amounts of data to make inferences about malware before it is ever opened. Deep learning models can keep up with the rapid evolution of malware.
3. Hunting zero-day exploits
One of the most difficult challenges for the modern cybersecurity function is defending against zero-day exploits. In a zero-day attack, perpetrators introduce malware by exploiting a software vulnerability that a vendor is unaware of (or has yet to patch). Antivirus software and patch management solutions, for example, cannot detect or prevent a zero-day exploit because it is too new for signature-based tools to detect. However, AI may be of assistance.
Deep learning architectures can be used to discover hidden or latent patterns and become more context-aware over time, which are both useful in identifying zero-day vulnerabilities or activities. Natural language processing can parse source code to identify malicious files. "Generative adversarial networks," which can learn to mimic any data distribution, could also be useful in identifying complex vulnerabilities.
The Pros and Cons of Enlisting AI for Cybersecurity
There are several reasons an organization might want to incorporate AI in cybersecurity. Knowledgehut’s CEH course online explains the topic in detail which you must consider checking. Here, we will explore some of the advantages and disadvantages of pairing AI with cybersecurity:
Advantages of AI in Cybersecurity
1. Ability to process large volumes of data
AI in cybersecurity enables organisations to process massive amounts of data with extreme precision and efficiency. AI automates the development of machine learning algorithms capable of detecting a wide range of cybersecurity threats originating in spam emails, malicious websites, third-party applications, or shared files.
2. Greater adaptability
AI-powered programs and applications Deep learning and machine learning algorithms are used to learn. AI can easily understand many IT trends and make changes to its algorithms based on the most recent available data or information using these procedures. Similarly, AI in cybersecurity is familiar with sophisticated data networks that can quickly identify and eliminate security threats with minimal human intervention.
Artificial intelligence in cybersecurity does not replace cybersecurity professionals. Instead, it enables cybersecurity professionals to quickly detect and resolve malicious network behaviours.
3. Early detection of novel cybersecurity risks
Two types of cybersecurity risks can affect an IT network or infrastructure. First, there is a novel, unknown threat, and then there is a known threat that has already breached the network. Hackers specialise in breaching undetected data networks, and AI-powered cybersecurity solutions can eliminate or mitigate these advanced hacking techniques.
Hackers are constantly striving to create new types of cyberattacks that are less detectable.
4. Artificial Intelligence offers real-time cybersecurity solutions
Hackers do not work regular hours and launch cyberattacks from different time zones. To detect malicious cyber threats and data network security breaches, your enterprise's IT infrastructure must be monitored in real-time.
Using an AI-enabled third-party cybersecurity solution, your company can eliminate the extra costs associated with IT security professionals' work shifts. It is also a viable option because these cybersecurity solutions are reasonably priced every month. Furthermore, AI cybersecurity solutions reduce the need for human intervention while providing detailed yet definitive detection of cyber threats as well as enhanced diagnostic capabilities, making them a dependable choice for enterprises. The incorporation of AI in the cybersecurity domain enables businesses to make better use of capital and human resources.
Disadvantages of AI in Cybersecurity
1. AI benefits hackers
AI advancement can also benefit hackers by allowing them to carry out highly sophisticated and large-scale cyberattacks. Furthermore, AI can assist hackers in effectively exploring and exploiting vulnerabilities in a data network or computer system.
2. Breach of privacy
AI-powered devices, such as biometric systems, can endanger our sensitive and confidential data. These devices can send our data to third-party vendors, who can then misuse it, infringing on the privacy of both individuals and businesses.
3. High cost of talents
AI-based technologies are currently in their infancy, so the cost of developing a talent pool is prohibitively expensive. Furthermore, the current stage of AI technology development reduces its dependability.
AI and ethical hacking are two of the most in-demand IT domains in the world, with professionals in these fields being sought after all over the globe. You have learned briefly about the two technologies, the various challenges that Ethical Hacking faces despite the numerous tools and technologies available, and how you can integrate AI to improve Cyber Security and overcome these challenges in this blog. Besides, we encourage you to check the difference between ethical hacking vs machine learning, if working on algorithms excites you.