Artificial intelligence (AI) and machine learning rank among the latest technological advancements to see an increased role in cybersecurity. Living in the digital age, the cultural and business landscapes sometimes feel like they are moving under our very feet. For example, the notion of a thought-generating machine was a sci-fi fantasy popularized in the original Terminator movie in 1984. It may come as something of a surprise, but the majority of Americans didn’t own a smartphone until about 2013. Less than a decade later, we’re discussing the best ways to deploy AI in the global fight against cybercriminals.
It’s true that many have reservations about integrating AI and machine learning into their business networks. After all, no one wants an Arnold Schwarzenegger cyborg telling them, “I’ll be back.” By that same token, the strategic use of AI and machine learning can exponentially enhance business cybersecurity. The question industry leaders may want to consider is an “either/or.” Either work with an expert to harden your defenses using AI and machine learning, or remain vulnerable to hackers putting the same technologies to work against you.
What are Artificial Intelligence and Machine Learning?
It’s essential to understand that AI is not necessarily a thing. It’s more akin to a method of using a variety of advancing technologies. AI-driven systems typically perform tasks that were previously reserved for human thinking. By exploring patterns, AI has the capacity to interpret speech, make predictions, produce art, play games, and even make investments. Wall Street firms have already embraced AI, and experts estimate it could increase value by upwards of $340 billion.
The success of AI stems from its ability to crunch massive amounts of data and identify connections and trends even skilled data analysts could not. It then uses this information to predict next steps, which is why Wall Street has not been shy about using it to support stock purchases. The thing that prevents AI from becoming a sci-fi nightmare is the fact that human decision-makers establish and supervise its parameters.
That being said, machine learning is not necessarily a different technology or approach. It’s a type or subset of AI that carries out specific processes and functions based on programmable and refined algorithms. Commonly used to identify relationships between items and trends in raw data, businesses are using machine learning to generate predictive analysis, classify digital assets, and even put forward creative content.
Some of the popular applications spawned by machine learning include ChatGPT and GitHub, among others. By that same token, machine learning remains a critical component in self-driving cars, fraud detection, and identifying malware threats. When AI and machine learning processes are implemented by a managed IT firm with cybersecurity expertise, the combination can significantly reduce the danger of ransomware attacks and other threats facing business leaders.
Major Cybersecurity Threats Facing Businesses
Despite widespread awareness, companies continue to underinvest in cybersecurity measures. A 2023 poll of chief information security officers (CISO) indicated that 75 percent of enterprises were “at risk of a material cyberattack.” The average financial hit continues to rise, with U.S. companies expected to lose more than $452 billion by year’s end. These rank among the greatest threats to businesses in 2024.
- Ransomware Attacks: Even garden variety hackers can gain access to the tools and technology required to launch ransomware attacks. Sophisticated cybercriminals no longer dirty their digital hands by taking over business networks and negotiating a crypto payoff. Instead, they are leasing out their criminal toolkit in what is being called ransomware as a service (RaaS). The proliferation of pre-developed ransomware tools puts every organization at risk. The average U.S. payout peaked at more than $870,000 in 2023.
- Endpoint Attacks: The pandemic fast-tracked the work-from-anywhere culture. Companies are still dealing with the security vulnerabilities created when personal devices are used to log into business networks. Exacerbating the problem, remote workers continue to utilize sometimes unprotected public Wi-Fi and unvetted endpoint devices. Curing this illness calls for advanced technologies that can identify endpoint devices and decide whether to allow or deny network access based on risk.
- Phishing Schemes: In 2023, more than 90 percent of firms were the target of email phishing attacks, making such schemes a constant threat. A reported 58 percent suffered some level of data breach. What’s remarkable about this pervasive business threat is that professionals are generally aware hackers push email schemes. Still, workers without proper cybersecurity awareness training click on malicious links, download malware-laced files, or unwittingly provide usernames and passwords to hackers.
- Supply Chain Attacks: Sometimes called “third-party attacks,” cybercriminals typically plant malicious code where they anticipate it will spread. The 2020 SolarWinds attack has become a model for understanding supply chain attacks. Hackers, using an intern’s login credentials, infiltrated the software company. They inserted strands of code into an application update that went undetected. Once the update was distributed to thousands of businesses, as well as government agencies, wide-reaching networks were compromised. Only advanced cybersecurity measures are likely to detect and deter high-level supply chain attacks.
- Zero Day Attacks: The term “zero day” refers to the fact a given software has gone unpatched or updated. It has effectively run out of days before the update could reverse its susceptibility. Savvy hackers search for emerging vulnerabilities and level attacks on outfits that fail to patch applications in a timely fashion. For instance, North Korean hackers took advantage of a Google Chrome vulnerability in 2022. Using phishing emails to reroute users to a fake Chrome site, they orchestrated a massive zero-day exploit that installed spyware.
- Weak Password Protections: Organizations that allow employees to choose their own passwords without direction create an inherent cybersecurity weakness. Everyday people use a variety of online platforms that require usernames and passwords. It’s not uncommon to select simple and easy-to-remember passwords and use them on multiple sites. Along with tasking staff members to employ strong passwords that include capital letters, a symbol, and numbers, it’s also critical to leverage multifactor authentication. This process sends a security code to a secondary device that legitimate network users must type in before access is granted. Easy-to-remember passwords are merely easy-to-guess keys to your digital assets.
Perhaps the greatest cybersecurity threat facing companies involves the growing talent shortage. A recent report from the World Economic Forum points to a cybersecurity expertise workforce shortage that could exceed 85 million by 2030. If there’s a silver lining, it’s that advanced technologies such as AI and machine learning can help compensate.
Cybersecurity Benefits of AI and Machine Learning
Because AI and machine learning can crunch enormous amounts of data from wide-reaching sources in real time, they play a crucial role in anticipating and detecting vulnerabilities. The technology also helps alert cybersecurity professionals to imminent threats. Automation and refined algorithms allow machine learning to deploy predetermined threat responses and suggest remediation measures. This list of AI and machine learning cybersecurity benefits highlights why they play a critical role in protecting sensitive and valuable information.
- Improved Threat Detection: Traditional threat analysis methods operate at a snail’s pace compared to AI and machine learning. Once-vaunted security measures detected fewer advanced persistent threats than these strategies, leaving companies more vulnerable than necessary. Employing AI and machine learning allows organizations to identify wide-reaching threats across their attack surface. This results in staving off supply chain attacks and malware-tainted emails, among many other dangers.
- Prompt Threat Response: The minutes it takes to send an alert to a security official, assess the risk, and take action too often give hackers all the time they need. Cybersecurity experts can use AI and machine learning to automate specific cybersecurity responses. Rather than allow ransomware attackers to get a foothold inside a network, advanced technologies can immediately launch a counterstrike to expel an incursion.
- Proactive Cybersecurity: The so-called break-and-fix approach to network security was abandoned a long time ago. Today, cybersecurity professionals prefer to ramp up defenses by employing threat hunting to play offense. Adding AI and machine learning expands the number of targets, allowing organizations to expel dangers before a full-scale hack gets underway.
- Adaptive Learning: The algorithms employed in machine learning processes continue to get refined over time. With the skilled hand of a cybersecurity expert driving the adaptive learning process, business networks enjoy a more determined cybersecurity posture. Hackers can up the ante, but AI and machine learning catch on too quickly for an enterprise to get outwitted.
- Endpoint Detection: Business leaders can breathe a sigh of relief knowing AI and machine learning can help protect against unauthorized remote access to their networks. These technologies can be used to check endpoint devices that attempt to log in. Should an employee try to use an unapproved device or unsecured Wi-Fi, access can be denied.
- Insider Threats: Although the vast majority of staff members are generally viewed as hard-working people with a sound ethical foundation, exceptions exist. A disgruntled worker may make an uncharacteristic decision to pilfer off digital assets. In other cases, moles from rogue nations attempt corporate espionage. Trade secrets and research prove valuable to private companies based in countries that do not honor American patents and honest practices. Machine learning and AI capabilities relentlessly search for anomalies, including the files and applications commonly leveraged by network users. When someone attempts to access files and applications outside those needed to accomplish tasks, alerts are automatically triggered.
Industry leaders who opt into AI and machine learning cybersecurity defenses also solve the biggest problem facing companies — the talent shortage. These advanced technologies far outpace the efficiency of cybersecurity experts in terms of detecting and deterring malware, ransomware, and various cyberattack methods. In the managed IT cybersecurity sector, they are not replacing human beings. Instead, AI and machine learning are helping to fill an expertise void that could otherwise tip the advantage to online criminals.
What Does the Future Hold for AI and Machine Learning Cybersecurity?
The emergence of AI and machine learning in cybersecurity could not arrive at a better time. Managed IT firms with cybersecurity experts on staff can now deploy these strategies to oversee and help protect the digital assets of multiple organizations. That, in turn, makes the service scalable. Real-time threat detection, predictive analysis, and anomaly alerts harden cybersecurity defenses to such an extent they discourage most cybercriminals. By integrating AI and machine learning, organizations are no longer the low-hanging fruit hackers pluck.
Author Bio
John Funk is a Creative Consultant at SevenAtoms. A lifelong writer and storyteller, he has a passion for tech and cybersecurity. When he’s not found enjoying craft beer or playing Dungeons & Dragons, John can be often found spending time with his cats.
