How artificial intelligence is transforming cybersecurity in 2024

Cybersecurity began decades ago, in the 1960s, as defenders sought to fight against hackers attacking phone systems. Soon, in the 1970s, when ARPANET, an early version of the internet, was created, attackers started experimenting with penetrating the systems connected to it. By the 1980s, government networks were being attacked, and security officials have been targeting cybercriminals, their tactics and technologies with a vengeance ever since. 

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Fast-forward to 2024, and both criminals and cyber defenders have a new tool: artificial intelligence. AI has risen to transform experiences across the board: Smartphones, vehicles, workout routines and entertainment all benefit from the AI movement. For IT security teams, AI is revolutionizing cybersecurity by enhancing threat detection, response, prevention and the speed at which professionals can learn. 

The current landscape of AI in cybersecurity 

Artificial intelligence involves computers using algorithms to imitate human thinking and decision-making. Machine learning (ML), a subset of AI, enables computers to learn and adapt on their own, without needing instructions from a human. In cybersecurity, machine learning and AI play a prominent role, especially when it comes to detecting and mitigating attacks. 

For example, next-generation firewalls (NGFWs) use AI to analyze the behaviors of files to see if their movement patterns indicate they present a threat. In this way, the system doesn't have to rely on the content of the file itself; it can simply judge its intent based on its behavior. For attackers, cybersecurity AI trends are bad news, especially because they make it easier to detect and respond to threats. 

AI-driven threat detection and response 

AI improves threat detection over traditional methods because it automates several of the functions cybersecurity teams need to defend systems, making it one of the hottest cybersecurity trends. In this way, it saves organizations money, preventing them from exhausting human resources as they try to identify and mitigate threats. AI also makes it easier to scale your cybersecurity operations, covering more devices and networks without having to invest in more hardware or personnel. 

One of the most significant roles AI has been playing is in developing pride active models that preempt cyber threats. For instance, you can catch malware with artificial intelligence. By analyzing current and historical data, you can predict the likelihood of a cyberattack based on region, technology and even the business sector you operate in. 

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The double-edged sword: AI as a tool and a threat 

Even though AI is a powerful anti-threat tool, attackers also wield it as a weapon. They use the same automation principles that power cyber defense to develop self-adaptive malware. For example, attackers can build malware that changes how it behaves or its code to evade antivirus software. 

Deepfake attacks have also become more common. These involve threat actors using AI to impersonate someone's likeness or voice, fooling victims into thinking whatever the "person" says or does is real. Threat actors can target everyone from HR to the C-suite and everyone in between, using deception to steal money and damage reputations. 

Attackers armed with AI have also uncovered ways to turn artificial intelligence against the defense teams that use it. For example, they use data poisoning and adversarial attacks, which involve inserting fraudulent data into a machine learning system's training datasets. In this way, the ML ends up missing attacks because its algorithms are using a false pretext when looking for threats. 

To protect your organization against AI-powered threats centers, you can: 

Generative AI and cybersecurity 

Generative AI (GenAI) has emerged as another way to automate some of the more challenging elements of cybersecurity, particularly because it can analyze data automatically and detect patterns. Generative AI is a subset of machine learning and uses pattern recognition to produce information. 

Here's how generative AI works: 

Some of the most common examples of generative AI include Bard, ChatGPT and other content-producing systems. They use GenAI to analyze huge volumes of text and then identify patterns. Using these patterns, these programs can imitate human writing, creating content that, although dry, predictable and sometimes inaccurate, makes sense. 

Similarly, generative AI can become one of the top cybersecurity AI best practices because it can identify threat behaviors and trends. Because cybersecurity teams can use GenAI to predict threats and their behavior, they can leverage this information to detect attacks before they happen. 

Also, because GenAI can study the data from many different attacks, it can understand how cybercriminals try to crack systems. This can help those in security operation centers and incident response teams focus their energy where the attackers are most likely trying to gain access. They could also use it to architect security systems that would thwart the most insipid attack vectors. 

In addition, security teams could also use GenAI to create cyberattacks and malware, and then use these in defense exercises. This would be similar to having the ability to create a droid version of an enemy soldier that behaves just like your adversary—then training your army to defeat them. 

While this can be effective, it's important to be careful—anytime you're creating cyber threats. An attack could either spread to one of your systems or get out in the wild, endangering other businesses and their networks. 

Another factor to keep in mind is how generative AI can be used by bad actors. In addition to writing realistic phishing emails, attackers can use it to create threats that look and act just like others that have been successful. They can also use it to identify patterns in the most effective security systems and then design threats that can circumvent these defenses. 

It's best to keep these tactics in mind as you design security solutions and adjust accordingly. For instance, instead of simply copying a security solution that has worked for another organization, you can tweak and customize it, making it different from what cyber attackers have fed into their malware training systems. 

How AI will impact cybersecurity education 

AI is also poised to shift the way students and cybersecurity professionals learn. For example, the Infosec Skills course Leveraging ChatGPT for security operations center (SOC) analyst skills shows how someone with minimal technical skills can learn to use a tool like Wireshark — using AI almost like a mentor to guide and teach them along the way — to perform incident response in less than two hours. 

AI tools also make the technical syntax that seems daunting for some cybersecurity career paths less of a barrier. AI allows learners to focus on the "why" of a process like incident response versus memorizing the "how" of different commands and tools. That means newcomers can dive into strategic and tactical processes quicker, learn faster, and become more useful to an organization.  

AI is going to rapidly change the way that everyone learns — and where they need to focus their skillsets — as AI tools like ChatGPT become more integrated into both our education and cybersecurity workflows. However, it's important to make sure those workflows remain secure as AI gets used. 

Mitigating AI risks in cybersecurity 

One of the most effective ways to mitigate the risks posed by AI is to systematically audit and secure AI systems. During the audit process, you want to look for vulnerabilities, such as corrupted training data, that could weaken your system. 

In addition, it's best to take preemptive measures to boost data security concerning your AI-powered defenses. For instance, you may have to use encryption to secure the data that goes into your training system. This can make it far more difficult for someone to intercept and read. Only those with authorized access can see the training data sets and make changes.