Generative AI in Cybersecurity: Definition and Applications

As a creative sensation, ChatGPT, Copilot, and Stable Diffusion applications have taken the internet by storm and made generative AI one of the fastest-growing and richest directions of artificial intelligence, yielding over $36 billion per year (as of 2024, according to Statista).

However, the nature of generative AI is much more ingenious than we might have assumed.

Defined as a type of artificial intelligence capable of creating new illustrations, reorganizing text, writing code, or even composing soundtracks, it quickly penetrated multiple industries, including more complex and dynamic cybersecurity.

The brightest minds promise that thanks to machine learning models, generative AI will be able to forewarn threats, design advanced defense mechanisms, and simulate myriad scenarios to better equip systems for real-world crimes.

But how true is this? How can generative AI be used in cybersecurity? And could it be that it will actually become an even greater threat if it falls into the hands of attackers? Let’s figure it out.

Integrating Generative AI into Cybersecurity

In simpler terms, cybersecurity refers to the practice of protecting confidential records, systems, and networks from digital attacks.

The capability of generative AI in cyber defense manifests itself in two ways: more traditional when security teams use pre-set rules and documented data to catch and tilt against anomalies or intrusions; and more farseeing focused on preventing and diminishing the danger of cybercrime.

By incorporating generative AI into cybersecurity, organizations can move from reactive to proactive protection. Intelligent models can imitate probable episodes and allow security teams to locate flaws before fraudsters can fully exploit them.

For instance, generative AI can examine all current phishing tactics and make up convincing emails that would test workforce readiness and resilience against social engineering attacks.

Major Advantages of Generative AI for Cyber Defense

The most consequential talent of AI, unlike older systems that look for familiar patterns, is the ability to investigate massive layers of data and pick up on unusual behavior that might signal a security problem.

But no less important is its ability to quickly react to dangers. AI can respond to threats much faster than a human. And as we know, the faster the reaction to the problem, the less chance that everything gets worse.

In June 2016, a cyber attack on Ethereum resulted in the theft of around $50 million in just a few minutes. This incident caused many traders to worry about using the ether blockchain.

Perhaps if generative AI had been invented and introduced to the public earlier, Ethereum would have been able to avoid this.

Finally, generative AI can visibly shrink budgets normally spent on human agents. By automating monitoring networks and checking logs, for example, it can free up security engineers to focus on bigger issues and cut down on manual work.

AI and Cybersecurity: Use Cases

AI cybersecurity tools are already being used in some ways. However, the more the technology works and learns, the more threats it can prevent and mitigate.

Phishing Detection

Phishing attacks try to fool users into giving away sensitive information, and they’re getting more sophisticated all the time. Generative AI can be trained to spot these patterns, flagging suspicious emails or messages so that users can review them before getting caught in a scam.

Malware Creation and Defense

It might seem strange, but cybersecurity specialists often use AI models to develop new types of malware, not for breaking the law, but for testing. Emulating helps find weaknesses in security systems and build better shields against future risks.

Monitoring Network Activity

It can track normal behavior on a network and specify any uncommon activity that might suggest an intrusion. By comparing real-time actions to established patterns, the AI can flag anything that looks out of the ordinary.

Password and Identity Management

Password and identity management create stronger security processes, such as generating one-time passwords and examining login patterns to spot potential identity theft. As it learns from user behavior, it continually improves the security of these systems.

Risks of Generative AI for Cybersecurity

Whatever the impact of AI on all areas of activity, its widespread adoption is limited by some imperfections.

One concern is AI-powered attacks. Artificial intelligence is a technology devoid of moral qualities. Therefore, it can both protect data and be used to design more advanced crime schemes.

For example, generative AI could be used to develop new types of malware or phishing tricks that are tougher to spot and harder to resist.

Another drawback is false positives. Sometimes, artificial intelligence might flag normal activities as insecurities, which can overload systems with unnecessary alerts and cause confusion.

The next thing is that all AI models suffer from—biases. Intelligent algorithms rely purely on the data they have been trained on, so if it’s insufficient or prejudiced, it can lead to security measures that miss certain vulnerabilities.

Finally, embedding generative AI into existing infrastructure can become resource-heavy and time-consuming. For those reluctant to bother with the AI development process, implementation, modernization, and control of security systems, there’s an option to seek external help.

For example, the SCAND team has over 20 years of experience in custom software development services. We can assist in developing and integrating generative AI into your current setup and make sure your software stays safe and resilient.

Predictions and Assumptions

As artificial intelligence continues to unwind, so too do cybersecurity risks.

Soon, we can expect smart models and human experts to work as a single unit, with AI taking large amounts of data and spotting threats, while humans focus more on overall strategy and decision-making.

Generative AI promises to become more personalized. It’ll be possible to tweak security solutions so they fit the specific industry, enterprise, and even division.

However, as AI technology improves on both sides—defenders and attackers—the battle between security experts and cybercriminals will heat up. Companies will need to leave their comfort zone and continue investing and researching to