Demystifying the Role of AI in Cybersecurity

May 14, 2020


There's a lot of anticipation and expectation in business around the role of artificial intelligence (AI) and the cybersecurity benefits we can expect to gain from our diversified integrations.

From website chatbots providing improved customer service, to biometric identification and cutting-edge customer data analysis, AI, and Edge AI is set to transform the world as we know it.The topic is as hotly debated as it is shrouded in mystery, as dreams of AI leading into a brave new future collide with the popular dystopian science fiction fantasies portraying human beings dominated by hyper-intelligent machines.

It might be time for a much-needed dose of reality, especially with regards to the impact of AI on our future information security and its role affecting major industries.

Neither wildest dreams nor worst nightmares are likely to come true anytime soon, but AI already brings significant benefits to applications and has the potential to improve cyber defenses exponentially. And while AI also poses certain data and information threats, it will mostly be applied to our advantage and should become a key component of the defensive arsenal of any organization.

How do artificial intelligence and cybersecurity work together?

Although this digital era's creature comforts and benefits are numerous, it also brings many drawbacks. One of its most noticeable and damaging threats is that our sensitive data and personal information is at risk like never before.

The last decade has seen hundreds of identity fraud incidents, incidents of major financial losses, and of course, massive data breaches. By nature, cyberattacks are widespread, affecting every person, enterprise, and government. We are moving into an age where cyber criminals can achieve their goals anywhere in the world at any time. As such, our need for effective and progressive cybersecurity has never been more important than now.

A typical cyberattack is an attempt by antagonists or cybercriminals to gain access to and modify or otherwise damage their target's computer system or network.They are systematic, planned, and make use of carefully calculated technology to disrupt organizations and any related (often critical) business operations.

While the future looks somber, technological advances with significant cyber security applications have also taken place. One of these key game-changers are the technologies and techniques developed and assisted by artificial intelligence (AI) and machine learning (ML) as a subset of it.

What we are seeing today is that biometric logins are increasingly being used by either scanning fingerprints, retinas or palm prints to establish secure logins. This can be used as a stand-alone safety measure or in combination with a password, and is most commonly found in smartphone technology.

Cybersecurity experts have conclusively demonstrated that passwords are highly vulnerable to cyber threats, compromising personal data, credit card details, and even social security numbers. All of these are reasons why biometric logins contribute positively to cyber security.

AI can be used to identify system or device vulnerabilities and other likely malicious acts. It is a fact that traditional systems can not keep up with the sheer number of malware generated each month, so it has become one of many prospective areas for AI to move in and resolve the issue.

At this moment, cybersecurity companies are teaching AI systems to detect malware and unauthorized logins by adopting complex algorithms. In this manner, AI and machine learning are now being used to secure blockchain-based cryptocurrencies, online banking, sensitive company records, customer data, and so on.

These systems already have the capabilities to distinguish even the simplest patterns in ransomware and malware attacks, effectively stopping them from entering networks or individual systems. They are also making use of predictive functions that go above and beyond the speed of traditional approaches. It’s for this reason that

Systems running on AI unlock natural language processing capabilities that collect information automatically through posts, news, and cyber threat studies. This knowledge will provide insight into trends, cyberattacks and methods for prevention. It further helps information security firms to keep up-to-date on the current threats and time frames, and create proactive strategies to protect organizations.

6 ways AI will improve cybersecurity

We all know that there are several ways AI and ML or machine learning will influence our future. We have highlighted some of the ways these technologies will make a difference by giving cybersecurity the much-needed boost.

1. Machine learning in cyber threat detection

Organizations must be able to detect a cyberattack in advance to be able to counteract whatever adversaries seek to achieve. Machine learning seems to be the aspect of artificial intelligence, which has proven extremely useful in detecting cyber threats based on data analysis and finding a threat before leveraging a flaw in the information systems.

Machine learning helps computers to employ and adjust algorithms based on obtained data, learn from it, and understand the necessary improvements. In a cybersecurity sense, this would mean machine learning allows the computer to detect threats and identify any anomalies even more accurately than any human would.

Traditional technology relies heavily on past results and cannot improvise as AI can. Classic technologies cannot address hackers' latest techniques and tricks as AI can. Additionally, the amount of cyber threats people face everyday is too much for humans and is managed better by AI.

2. AI-powered password protection and authentication

Passwords have always been a very weak security control and they are most often the only link between cybercriminals and our identities. Biometric authentication is being evaluated as an alternative to passwords, but it's not very convenient, and attackers can also easily bypass these controls. Developers are utilizing AI to improve current biometric authentication and eliminate any imperfections to make it a robust application.

One example is Apple's face recognition technology that is currently used on their iPhone X smartphones. Called Face ID, the device detects the user's facial features by built-in infrared sensors and neural engines. AI software produces a sophisticated face model by recognizing key similarities and patterns.

Apple believes that this technology has a one-in-a-million probability of tricking the AI and opening the app with another face. The AI device architecture can also work under various lighting conditions, compensating for changes such as having a new hairstyle, increasing facial hair, wearing a hoodie, etc.

3. AI and ML in phishing detection and prevention control

Phishing is one of the most used cyberattack methods where hackers attempt to deliver their payload using a phishing attack. Phishing emails are extremely common; in fact, one in every 99 emails is an attempted attack. Once opened, the email will contain a link luring the victim to install malware or one of the hacker-favorites, ransomware, onto their device. AI and ML will, fortunately, play a major role in mitigating and thwarting phishing attacks.

AI and ML can identify and track over 10,000 active phishing sources and respond much faster than humans can. AI and ML also work to monitor phishing threats from around the world, and its knowledge of phishing campaigns is not limited to any single geographic region. AI also allows fast distinction between a fake and a valid website.

4. Use of AI and ML in vulnerability management

Nearly every business process includes information technology (IT). Over 2,000 unique vulnerabilities were recorded up to date this year alone. Managing all these with human or conventional technology is incredibly difficult. Yet AI will approach this much easier.

AI and ML-based systems don't allow online threats to exploit a vulnerability. Instead, these AI-based systems efficiently and effectively search for possible flaws in corporate information systems and do so by successfully incorporating various variables such as dark web hacker forums, hacker credibility, trends used, and so on. These systems can analyze such variables and use the knowledge to decide when and how vulnerable targets may be attacked.

5. Network security and AI

AI will make our lives much easier, but also lead to the obsoletion of many technologies we currently use. It may also lead to certain positions or jobs becoming obsolete. Two essential aspects of network security are security policy development and the network topography of an organization.

Usually, both tasks are very time-consuming and take up a lot of human effort and time. We can now use AI to automate these processes by analyzing and studying network traffic dynamics and recommending policies and procedures. This not only saves time, but also a lot of energy and money that we can devote to technical growth and enhancement areas.

6. Behavioral analytics with AI

AI's behavioral analytics ability is another exciting enhancement in our security improvements. What this boils down to is that ML algorithms can learn and build behavior patterns by studying how to use your computer or other smart devices and your favourite online platforms. The details can include everything from your usual login times to your texting and browsing patterns.

If the AI algorithms find irregular behaviors or actions outside your normal patterns at any time, it can identify it as being conducted by a questionable user or block the individual. The behaviors that tick off the AI algorithms can be anything from a massive online shopping sprees, the products of which get shipped to addresses other than yours, a sudden increase in uploads, downloads or the general transference of documents from your archived files, or a sudden shift in your typing pace.

How can AI reduce the cost of a data breach?

With the radical ingenuity of artificial intelligence, the identification of cybersecurity breaches can help consumers to safeguard their private information.

As most websites contain a certain amount of known vulnerabilities, hackers frequently target those with the highest volume of personal data. In most cases, this is done effortlessly, without the active involvement and awareness of the user. Let's explore how AI is used to counter threats to personal information in various industries.


Anomaly detection is a technique using AI to identify unusual activities in a complex world. For example, when a customer unexpectedly makes a suspicious large withdrawal from their bank account. This activity would be beyond this specific customer's "natural behavior" boundaries, and both the customer and bank would be informed of this unusual activity.

Credit card fraud and misuse is one of the banking sector's major problems. AI helps minimize these threats using a misuse identification technique. Here computers identify credit card fraud or misuse based on previous rules built into the system. Every documented invasion has a unique signature. Similar signatures describe invasion characteristics. Often, signatures will have a similar flaw. When the device detects one of these signatures, the bank is alerted.

Another problem for banks is loan application fraud. AI is used to quickly analyze information about an applicant's authenticity and detect unusual behavior or anomalies in the data provided, such as a suspicious residential or business address. By removing fraudulent loan applications earlier than usual in the application process, fraud can be restricted or entirely eradicated and more time can be spent reviewing legitimate applications.


Insurance firms have become a valuable target for hackers because of the large amount of data insurers collect about individuals and businesses. Understandably, the need to remain competitive while reducing security risks have prompted firms to digitize their products and invest in new electronic platforms. This investment, however, sparks other emerging cyber security threats.

When a customer submits their insurance application, there is a presumption that the prospective policyholder will provide accurate details. Nonetheless, a small number of candidates also fabricate data to manipulate the rate they receive from insurance firms.

To address this issue, insurers use AI to analyze online networking profiles of an applicant to claim that the data provided is not fraudulent. For example, AI will inspect the pictures, posts and information of the potential policyholder to validate their submitted details. This technique is successful in detecting fraudulent submissions.

AI can also be used to optimize insurance claim evaluation and filtering based on known fraud trends. Not only does this procedure flag potentially false claims for further investigation, it has the added benefit of automatically detecting legitimate claims and streamlining approval and payment. This lowers insurance companies 'costs and helps reduce customers' prices.


Privacy and protection in healthcare is complicated since thousands can access patient data. It would be impractical to manually evaluate the amount of patient data interactions every day. Moreover, there is a greater chance of privacy and security violations when a patient's data is linked to the Internet.

AI has the ability to scan patient data per second across individual transactions and assess the different factors related to each transaction, such as the area of access, amount of logins, and length of time for each login effort. Should a staff member's account suddenly access the files of 10,000 patients at almost the same moment, AI would detect this unusual behavior and issue an alert.

Medical devices including pacemakers and insulin pumps are commonly used worldwide and give patients significant benefits. Such devices are vulnerable to attacks, however, as many do not have the required operating system version needed to fully exploit the device's protection and privacy. Security researchers examined the vulnerability of medical devices, enabling viruses to be transmitted to a patient's pacemaker device.

The pacemaker was directed to shock the patient. In these circumstances, using anomaly detection (mentioned above), AI is being implemented to detect abnormal instructions being sent to the computer. AI will track the system constantly without having to rely on manufacturers to warn the hospital and patient about the vulnerabilities.

Companies using AI in cybersecurity today

Artificial intelligence, including neural networking, machine learning, analytics, and its associated algorithms for specific tasks, allows systems to learn through experience. For cybersecurity, AI's machine learning sub-set has the most use – at least at our current stage of AI development.

Although there is little use of 'real' cognitive AI, machine learning can also provide a stepping stone from conventional, signature-based antivirus and cybersecurity strategies to a broader method of data collection and analysis.

Microsoft, Google, and a number of other start-ups are shifting away from using rule-based technologies designed to adapt to different types of invasion to deploy machine learning algorithms that analyze large quantities of data to enable authentication to stop hackers from gaining access to user accounts.

Obviously, hackers themselves are incredibly resilient, and they too might use machine learning to create different disruptions to overwhelm new security systems.

They may, for example, find out how businesses train their systems and use said data to circumvent or cheat algorithms. The major cloud services providers are painfully aware that the enemy is a moving target, but claim that the new cloud-based web hosting technologies should help the good guys tip the balance.

Google also searches for violations even after a user has signed in order to find nab hackers. With machine learning capable of analyzing multiple unique sets of data, catching unauthorized logins is no longer about a simple yes or no.Google tracks different behavioral factors during a user session. Someone who initially looks credible but then show signs they're not who they say they are, will be kicked out by Google's software before they can cause any damage

Besides using machine learning to protect their own infrastructure and web services, Amazon and Microsoft give their customers the same technology. Amazon's Macie service uses machine learning to identify confidential data from customers like Netflix and corporate info, and then monitors who is accessing it and when, alerting the organization to suspicious behavior. Amazon's GuardDuty tracks networks for disruptive or illegal behavior. The service also sees workers doing things they shouldn't do, like mining bitcoin at work.


While the idea of allowing AI to take over entirely is very enticing, we must remember that AI consists of a great many things and is therefore very adaptable. While AI is doing cyber security wonders, it is also making its way to hackers for malicious purposes. In the wrong hands, it can cause exponential harm and be an even bigger danger to cybersecurity.

As technology progresses, our global friends and allies are also improving their attack strategies, tools and techniques. There's no doubt that Artificial intelligence is extremely helpful, but it's a bit of a double-edged sword. AI and ML can be used to detect and prevent attacks before they occur. As AI sees more progress, we will indeed be witnessing how far the technology can go and in how many ways it will benefit us and our future generations.

Demystifying the Role of AI in Cybersecurity Cybersecurity is a core component of online safety. Learn how AI, ML, and other high-tech components comprise the world of cybersecurity as we know it.
Dan Fries Dan Fries is an entrepreneur, investor, and writer who shows bootstrapped entrepreneurs and business owners how to prepare for an exit by making better long term financial decisions.

Never miss a post.

Subscribe to keep your fingers on the tech pulse.

By submitting this form, you are agreeing to receive marketing communications from G2.