AI and Machine Learning in Cybersecurity

Most people know not to fall for suspicious emails sent by those claiming to be Nigerian princes or long-lost relatives. But as technology advances, cyber threats have grown increasingly prevalent and sophisticated. Many individuals and organizations fall victim to advanced cybercrimes like cryptocrime and ransomware attacks. According to a 2022 study by the research firm Cybersecurity Ventures, these attacks will cause $8 trillion in damages globally in 2023.¹

On the bright side, artificial intelligence (AI) and machine learning offer new ways to fight cybercrime. These innovative and progressive technologies allow experts to identify and respond to threats faster and more efficiently. AI- and machine learning-powered security tools can also help on the front end by locking down data and detecting new threats.

Find more details as this article examines cutting-edge developments in AI and machine learning in cybersecurity.

Uses for AI and Machine Learning in Cybersecurity

AI and machine learning are two of the hottest trends in the cybersecurity industry. According to a 2023 survey by the Canadian software firm BlackBerry Limited, 82% of IT decision-makers in North America, the United Kingdom and Australia plan to invest in AI-driven cybersecurity by 2025.²

Traditionally, cybersecurity experts recognized new threats manually and developed methods to stop them. For example, organizations would train employees to identify phishing emails and implement multifactor authentication to prevent hacking.³ However, cybercrimes evolve rapidly, so human cybersecurity specialists often remain one step behind cybercriminals.

Now, experts can leverage AI systems to expand their capability and combat complex cyberattacks. Hybrid cybersecurity combines human intelligence with AI and machine learning technologies to recognize and prevent emerging threats.⁴ Hybrid approaches can pair AI with security data and automation to accomplish these tasks.

Processing Security Data With AI and Machine Learning

Cybersecurity experts frequently need to collect and decipher vast amounts of data to identify threats. Corporations can receive thousands of security alerts daily, and computer networks have many activities and users. Rather than having someone spend hours combing through that large volume themselves, AI sifts through this mountain of data much faster than humans and flags abnormal activities that may indicate a cyber threat, like an unknown user accessing files. Humans can then use this information to respond to cyberattacks.⁵

Working in tandem with AI, machine learning algorithms enable AI to learn from the data it collects. As a result, AI becomes increasingly adept at recognizing cyberattacks over time and less likely to produce false positives.⁶

Automating Cybersecurity Tasks

Humans can also implement AI and machine learning to automate routine or time-consuming cybersecurity processes. A recent survey by IBM’s Institute for Business Value found that 64% of cybersecurity executives use AI and automation as part of their organization’s security lifecycle.⁶

For example, AI can patch vulnerabilities in software systems automatically. This technology can also automatically prevent unauthorized access to confidential data, monitor user behavior and triage security alerts. Automating these processes reduces response times and allows human experts to focus on the most serious threats.⁷

The Benefits of AI and Machine Learning in Cybersecurity

AI and machine learning tools have many advantages for cybersecurity experts and organizations. Let’s explore a few of them in greater depth.

Keep Up With the Latest Cyber Threats

AI uses predictive analytics to identify new malware and other types of cyberattacks. Machine learning algorithms allow AI to learn from previous threats and detect the earliest symptoms of emerging threats. Furthermore, AI uses natural language processing to collect and learn from news and research on emerging threats.⁸

Enable Real-Time Response

AI works around the clock to predict, detect, and halt cyberattacks. For instance, this technology provides real-time mapping of cybercrimes like hacking and fraud. Based on this data, AI can alert cybersecurity professionals and automatically implement defensive measures. A real-time response can immediately stop cybercrimes before they cause serious damage, protecting the organization’s financial health and reputation.⁹

Catch Internal Threats

Unfortunately, some employees take advantage of their access and/or responsibilities and intentionally participate in cybercrimes by leaking private information or destroying data. For example, in 2021, a credit union employee pleaded guilty to deleting thousands of files from a company server after the company terminated her.¹⁰ In other cases, members of an organization unknowingly aid cybercriminals: in 2016, the social media company Snapchat accidentally released employee data after a human resources worker fell for a phishing scam.¹¹

AI can quickly identify those staff who may pose security threats. By collecting and analyzing data on user behavior, it can spot and warn against potential bad actors. AI can also detect employees who access files without authorization or frequently open spam emails.⁶ Once AI has detected these users, the cybersecurity team can investigate further and provide necessary training.

Security Tools Powered by AI and Machine Learning

Companies can implement AI- and machine learning-powered security tools to enhance their cybersecurity and save time for employees. Examples of this technology include:

AI Chatbots

AI-powered chatbots like ChatGPT use natural language processing to understand human language. Cybersecurity experts can use these tools to research cyberattacks and develop solutions to combat complex threats. Chatbots can also test systems for vulnerabilities and write incident reports.¹²

Next-Generation Firewalls

Next-generation firewalls (NGFWs) prevent unauthorized access to devices and networks by combining individual security controls in one gateway. For instance, NGFWs can incorporate content filtering gateways, intrusion prevention systems and virtual private network (VPN) gateways. Several NGFWs also use AI and machine learning to continuously learn about emerging cyber threats and implement new security measures.¹³

Behavioral Analytics

Cybersecurity professionals use AI and machine learning algorithms to create custom behavioral profiles for all users. Based on this data, endpoint management tools can detect device cloning, fraudulent transactions, user impersonation and other abnormal behavior that could signal a cyberattack.¹⁴

Become a Cybersecurity Leader With AI and Machine Learning

AI and machine learning are reshaping the cybersecurity industry by making it easier and faster to combat cyber threats. And to become a leader in this rapidly changing field, you need to have the theoretical and technical expertise to wield these exciting tools.

An online MS in cybersecurity from the Yeshiva University Katz School of Science Health can help you develop the expertise and technical skills necessary for these technological advancements. As you take classes online, whenever and wherever you prefer, you’ll learn about the latest cybersecurity approaches and frameworks from expert faculty. You’ll also gain hands-on experience and networking skills as you participate in internships and research.

If you’re interested, set up a call with an Admissions Outreach Advisor to learn how you can prepare for a leadership role.