The frequency of cyber-attacks continues to be prevalent – 66% of businesses experienced a cyber-attack in 2021 according to Forbes. As cyber threats and attacks grow more sophisticated, so does the technology that prevents them. Many businesses are turning to AI to build up their defenses against the crimes that their industries face. While there are numerous use cases and benefits for implementing artificial intelligence and machine learning technology for cybersecurity, the very same technologies can also be leveraged by criminals for their own gain.
AI is powerful but can be used for wrongful actions. It presently assists governments in developing innovative methods of censoring online content. Artificial intelligence can also secretly collect data and obtain access to the personal information of individuals all around the world. This is when Virtual Private Networks continue to be necessary.
Let’s dive into what role artificial intelligence plays in cybersecurity, its benefits, and drawbacks, how cybercriminals also utilize AI, and how our most basic cybersecurity precautions are still our greatest tools.
The first cyber threat was not actually a threat at all. Bob Thomas, a technology engineer, designed the first computer virus in early 1970 that could travel between computers. The only threat it posed was this message: “I’m the creeper: catch me if you can!”. In response to this, Thomas’ friend and coworker, Ray Tomlinson, who went on to create the first email program, produced additional code that could not only move from computer to computer but could also duplicate itself as it moved. This effectively removed the ‘Creeper’ and the new code, and the first ever antivirus software was named the ‘Reaper.’
More malicious viruses appeared in the late 1990s and early 2000s. The ILOVEYOU and Melissa viruses infected tens of millions of devices worldwide, disrupting email systems. Unfortunately, the vast majority of hacked emails were of unwitting victims with weak security. These exploits, which were primarily intended for financial gain or strategic goals, generated headlines as they took center stage in the world of cyberattacks.
There has been a tremendous evolution and growth in cybersecurity during the last few decades. The global threat landscape has continued to shift, giving us new battles to face, and internet users today confront a variety of risks. On one hand, huge and mostly automated botnets attack consumer devices. On the other hand, social engineering (or phishing) assaults attempt to dupe individuals into handing over their money and personal data.
As a result, cybersecurity has evolved in terms of both threat and response. Cybercriminals now have more creative ways to carry out more coordinated attacks. Despite methods to evade being tracked online, these last few decades have forced businesses to reconsider their cybersecurity practices. Cyber attackers are now infiltrating more networks as a result of the expansion of cloud and IoT devices and they continue to use this new technology to make more and more sophisticated attacks.
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As mentioned, and up until recently, security solutions were primarily reactive: a new malware would be discovered, analyzed, and added to malware databases by experts. The industry continues to take this strategy, but it is becoming more proactive, particularly in the face of social engineering risks.
In this transformation, machine learning or AI algorithms are critical. While they are not a panacea for all cybersecurity challenges, such as how to secure your business after a data breach, they are incredibly useful for fast streamlining decision-making processes and inferring patterns from incomplete or manipulated data. These algorithms learn from real-world data such as current security dangers and false positives, as well as the most recent threats found by researchers worldwide.
The number of malware attacks worldwide surpassed 2.8 billion in the first half of 2022. In 2021, 5.4 billion malware attacks were detected. In recent years, the most malware attacks were found in 2018, when 10.5 billion such attacks were recorded globally.
AI and machine learning-powered systems may analyze malware based on inherent properties rather than signatures. For example, if a piece of software is built to encrypt multiple files quickly, this is suspicious behavior. Another clue that the software isn’t real is if it takes measures to conceal itself. An AI-based program can consider these and other factors to determine the danger of a new, previously unknown piece of software. The ultimate result could be a significant increase in endpoint security.
AI can also help identify and prioritize threats. Analysts in security operations centers are bombarded with security alerts every day, many of which are false positives. They can end up spending too much time on these rudimentary tasks and not enough time looking into real threats or they can completely miss advanced attacks. According to Verizon’s data breach investigation report, 20% of breaches took months or longer for businesses to notice there was a problem.
Lastly, artificial intelligence and machine learning can be used to streamline and automate operations including responding to large numbers of low-risk warnings. These are warnings where a quick response is required yet the risks of making a mistake are low and the system is certain about the threat. For example, if a known sample of ransomware appears on an end user’s device, promptly disconnecting its network connectivity can prevent the rest of the business from becoming infected.
A drawback of the role of artificial intelligence in cybersecurity is that hackers may benefit the most. Hackers can exploit AI advances to launch cyberattacks such as DDoS attacks, MITM attacks, and DNS tunneling. They are also using artificial intelligence to hack passwords faster.
Incorporating machine learning into your threat detection approach is a vital aspect of any cybersecurity strategy today. As mentioned previously, AI-powered technologies alleviate some of the stress on security personnel who are subjected to thousands of alerts every day.
However, hackers may take advantage of these analytics by flooding the systems with too many alerts. Too many false positives can overwhelm even the greatest machine-learning systems and security specialists. The attacker can overwhelm the system and generate a large number of false positives, and then launch a real attack while the system is adjusting to filter out the false threats.
When it comes to malware, the effectiveness of ransomware is determined by how quickly it spreads throughout a network system. AI is already being used by cybercriminals for this aim. For example, they use artificial intelligence to monitor the reactions of firewalls and identify access points that the security staff has overlooked.
Considering their evolving sophistication, other ransom attacks are AI-powered. AI is included in exploit kits that are sold on the dark market. It’s a tremendously profitable tactic for cybercriminals, and ransomware SDKs are packed with AI technology.
“Although artificial intelligence and machine learning have enormous potential, they are not quick fixes. Despite the benefits, AI is not ideal for detecting cyber threats. It suffers with quick change, such as the unexpected COVID-19 outbreak, which radically altered employees’ work behavior.” – as explained by Urban VPN in one of their recent blog posts.
The usage of AI may also jeopardize data confidentiality. AI algorithms are associated with large-volume data analysis, which is required for the developed algorithms to produce accurate results. A company’s data contains traffic linked to daily transactions and online activities, as well as sensitive information about its clients, such as personal information. What actually happens to our data when it is transferred to an AI agent, however, remains a mystery. With personal data breaches consistently making headlines, the safety of consumer data should not be compromised for the sake of advanced technology.
The basics of cybersecurity should continue to be observed regardless of new technology advancements. Protect your internet connection by installing a firewall and encrypting data. As convenient as connecting to a free Wi-Fi network may be, it might be damaging to the safety of your business. Connecting to an unsecured network allows hackers access to your computers. Invest in a VPN to ensure the privacy of your network. IP addresses can also be used for data protection to protect cybercriminals from accessing sensitive data.
Implementing passwords to safeguard computer networks is obvious but if you want to get the most out of your password protection, you must pay attention to more than just the odd number and letter sequences. Consider requiring a multi-factor authentication mechanism, which involves more information than a single password to obtain access.
Furthermore, to save time and money on checking cyber interactions, create regulations that outline how employees should protect identifying information and other sensitive data. This includes restricting access to specific information and using layered security measures such as supplementary encryption, security questions, and so on. Make certain that your staff understands the penalties of violating your company’s cybersecurity standards.
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Artificial intelligence is no longer a thing of science fiction, but a very real element in the modern development of cybersecurity. However, despite its appeal, we should not dismiss other cybersecurity safeguards that continue to protect businesses and users alike, nor the possibility of artificial intelligence being weaponized to do harm rather than good.