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5 Ways Cybersecurity Can Benefit from AI

 

Image credit: Unsplash

Businesses have numerous weak points. A minor neglection may cause serious problems when an unauthorized user penetrates the system and steals sensitive data. And the attack surface is just getting bigger.

How do you predict and prevent risks? You need to process billions of time-varying signals, which is hard to accomplish on a human scale. That's where artificial intelligence has evolved to assist information security teams in their tasks, such as reducing breaches or improving their security posture.

AI bears a limitless potential for various spheres. For example, it enhances eCommerce onsite search, producing more relevant results according to search history. It leverages client preferences to provide personalized recommendations and to improve customer experience. But most importantly, AI can protect IT infrastructure.

According to a recent Capgemini study, two-thirds of organizations can't imagine recognizing and fighting critical cybersecurity risks without AI. The same report indicates that nearly three-quarters of businesses have already implemented AI for this purpose.

capgemini-study 

The best part is that AI solutions improve with time, taking into consideration previous scenarios. Let's dwell on the AI benefits for cybersecurity.

5 Ways AI Can Help Cybersecurity

1. Handling Massive Data Sets

Human abilities are limited. We can't compete with intelligent systems in analyzing large data arrays, and we need to accept it. So why not leave the complex tasks to AI?

AI cybersecurity can benefit nearly all businesses in its ability to process massive amounts of data with assembly-line efficiency. It automatically creates algorithms to detect security threats across a wide range of elements in the IT network, such as:

  • emails;
  • websites visited;
  • third-party software;
  • shared files;
  • activity patterns indicative of network hacking attempts.

Note that present-day AI can't self-adapt to the full degree. It requires human control and new programming. As a result, it doesn't threaten to replace IT professionals.

Network analysts implement AI to distinguish between hazardous and benign network activity swiftly. It lets them focus on potentially dangerous patterns and program AI to block such incidents in the future.

So it's a win-win. Humans educate AI to become more intelligent, and vice versa.

One of the helpful cybersecurity tools is IBM QRadar Advisor, which utilizes IBM Watson technology to defend against malicious attempts. It combines:

  • automatic incident investigation to collect a local context based on the data from the incident;
  • high priority risk identification, where it handles critical insights to determine which threats you should prioritize.

For example, IBM QRadar Advisor serves eCommerce companies to grow their business and keep the security level high. It provides a centralized dashboard to monitor the whole infrastructure with enormous data.

Cybersecurity-Knowledge-Graph 

Image credit: IBM

2. Self-Learning to Analyze Suspicious Patterns

Another helpful AI faculty is machine learning. It implies self-improvement over time. ML considers routine IT network activity and remembers that as legitimate events.

Suppose something abnormal happens. AI recognizes it as a potential threat because it's not used to such scenarios. It stops dangers in their infancy before they transmit a virus, record keyboard actions, or let hackers browse the infrastructure.

AI analyzes user account, endpoint, and server behavior to identify not only familiar hazards but also zero-day unknown attacks. These are threats that you may not even be aware of.

Hackers devise new ways to access networks unnoticed and catch firms off guard. AI can proactively protect organizations before they find or have to patch such vulnerabilities, decreasing the consequences of a novel hacking strategy. Even if the cybersecurity annals have yet to identify and explain it.

3. Non-Stop Protection

Hackers don't work during typical business hours. Even if they do, they may reside in any part of the world. How do you protect the network round the clock? Here's where artificial intelligence and cybersecurity are a perfect match.

Don't wait until the next working day. You'll need to monitor your IT system 24/7 to learn about malicious things and attempted network breaches before they destroy your company.

You may want to hire more IT workers or make them work in shifts. But it's neither cost-effective nor efficient compared to deploying an AI cybersecurity solution. A third-party cybersecurity program will cost you less than an employee.

Moreover, people may make mistakes, get sick, or fall prey to social engineering: psychological tricks when a cybercriminal manipulates emotions to convince a person to expose information. Social engineering happens only in human interactions.

Generally, AI tools come at an affordable price. The app will diagnose the infrastructure more extensively and effectively than a human. In the long run, merging AI and cybersecurity allows businesses to leverage their human resources better.

Let's take Darktrace Antigena as an example. It relies on basic ML technology to detect suspicious actions and respond in real-time (just in 10 seconds), depending on the danger degree.

Antigena doesn't require human intervention to carry out the needed procedures. Neither does it need prior knowledge of attacks, regulations, or signatures.

The tool lets organizations respond to threats swiftly, without disturbing regular business workflow. Darktrace Antigena covers all digital areas, as the company claims.

screenshot-darktrace 

Screenshot taken on the official Darktrace Antigena website

4. Preventing Unwanted Users from Accessing Resources

Most websites require visitors to log in or create an account to utilize services or make purchases. Some accounts bear personal information, such as:

  • address;

  • full name;

  • credit card number.

How do you keep your prospects safe as they browse your network? In this case, you'll need an extra security layer. Such a step will protect personal data and sensitive information.

Let's assume hackers try to gain access to enterprise networks. They may resort to credential stuffing and brute force attacks. They insert as many variants as possible, aiming to guess the right pair.

Once an attacker enters a user account, your entire network may be compromised. AI can also help at this stage. When visitors wish to log into their account, AI employs a variety of verification and authentication techniques:

  • facial recognition;

  • CAPTCHA;

  • fingerprint scanners;

  • among others.

These factors help determine whether a log-in attempt is authentic or not.

5. Scanning the System for Possible Weaknesses

Such cases differ slightly from detecting abnormal behavior. The emphasis here is on AI identifying potential faults, bugs, and security issues. Possible examples may be:

  • Catching when an application sends untrusted data.

  • SQL injection vulnerabilities, meddling with a program's database queries. These are some of the most prevalent weaknesses malware and viruses exploit to steal data and gain access to computers.

  • Buffer overflow. It happens when an application puts more data in a buffer than typical.

  • Human error, which is among the most common causes of data breaches, and AI can detect them early enough to avoid damage.

Final Word

Why do companies adopt AI and ML? Such technologies can quickly analyze millions of events and identify many threats. These may include malware exploiting zero-day vulnerabilities or risky behavior leading to phishing attacks or malicious code downloads.

The battle between attackers and defenders will never finish. Fraudsters develop new scenarios and look for loopholes to steal data. In return, they may ask for ransom or use financial information directly to their advantage.

Cybersecurity teams try to prevent or deal with the ongoing damage. But obviously, it will require more human resources to analyze the growing amount of data. That's why AI and machine learning are the best steps forward.

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ABOUT THE AUTHOR

Alex Husar

Alex-Husar 

Alex Husar, chief technology officer at Onilab. For over eight years, he's been working on Magento migration and development projects as well as building progressive web apps (PWAs). Alex is an expert in full-stack development who shares his expertise and in-depth knowledge on modern technologies and Computer Software Engineering.

Alex's Linkedin profile

Published Thursday, June 30, 2022 7:33 AM by David Marshall
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