By Devin Partida, Editor-in-Chief, ReHack.com
Artificial intelligence (AI) has permeated modern life. In late 2022, OpenAI unveiled ChatGPT, introducing many people to natural language processing (NLP). Now, people are using generative AI for tasks like writing emails, gathering information and making predictions. Some professionals are using generative AI for cybersecurity.
Generative AI is an influential tool cybersecurity experts can use for their businesses. However, there are also drawbacks associated with it. Here are the pros and cons of using generative AI for cybersecurity.
What Are the Pros of Using Generative AI in Cybersecurity?
Cybersecurity technology is evolving. Experts are constantly searching for ways to stay on top of the industry. Generative AI can help companies with these advantages.
Some cybersecurity employees have long days at the office but clock out eventually. Cyberthreats can happen overnight, and somebody or something needs to watch while the workplace is empty. Countries like China and Russia often pose cyber risks to the U.S., so protection is critical.
Generative AI allows businesses to have around-the-clock protection. Cybersecurity teams use it to rely on third-party vendors to fight cyberthreats and provide peace of mind after hours. The software will diagnose threats and use strategies to fight them — often better than humans can.
Rapid Threat Detection
Rapid threat detection is another benefit of generative AI. NLPs are advantageous because they can process high volumes of information simultaneously. Generative AI can quickly react to data because it can identify dangers faster than humans. It gives cybersecurity teams a leg up in threat detection.
Generative AI protects systems by detecting patterns from security threats and anomalies in typical behavior. This software monitors network traffic and user behavior to ensure everything runs smoothly with internal users and external forces. Generative AI also uses predictive analytics to detect threats before they can do any damage.
Cybercrime happens regularly, so it’s critical to be ready when it does. Ransomware attacks have significantly increased since the pandemic. Research shows ransomware attacks surged by 40% in 2020 and malware in the Internet of Things (IoT) increased by 30%. One way generative AI prepares is by simulating attacks.
Simulated attacks are critical for cybersecurity infrastructure because they identify vulnerabilities and allow for correction before a genuine threat arises. Many cybersecurity procedures are reactive — but generative AI reverses course and turns IT departments into proactive teams. Ransomware attacks can be costly, so it’s crucial to prepare.
The rise in cybersecurity threats has made IT employees work harder. The Bureau of Labor Statistics (BLS) projects 35% growth for information security analysts between 2021 and 2031 — a rate it says is much faster than most other fields. The hours can be long, but cybersecurity experts can make the job easier by streamlining their daily tasks.
Generative AI makes life easier — not harder — for IT employees. AI allows employees to increase productivity and efficiency by minimizing their routine tasks at work. For example, an IT department can use generative AI to perform system updates automatically and monitor the network.
What Are the Cons of Using Generative AI for Cybersecurity?
This technology is advantageous for cybersecurity professionals. Still, there are downsides. These disadvantages demonstrate why some experts are waiting to use generative AI for cybersecurity.
Generative AI in cybersecurity is still a relatively new topic. There’s room for potential, but companies still face vulnerabilities with generative AI before it can become a completely reliable service. Cybersecurity experts must train it to manage particular threats. Many professionals face issues where their AI is biased or limited to specific data.
Generative AI is susceptible to hackers who try to ruin these NLPs. For example, bad-faith users could fool generative AI into misclassifying data, leading to inaccurate predictions. They can also poison information to destroy generative AI models in training sets. In this case, generative AI becomes a liability.
New technology is terrific for those who can afford it. People can use ChatGPT for free online, but leveraging generative AI for cybersecurity is expensive. For example, Latitude says it spent over $200,000 monthly on generative AI software and Amazon Web Services in 2021. Small businesses and startups may need help with these steep costs.
More products will enter the market and bring the price down. However, there are other financial obstacles. Organizations must hire highly trained employees with experience in generative AI to construct and maintain the software. The cybersecurity industry needs more skilled workers to fill the gap. Generative AI could divide the industry based on who is and isn’t ready for the financial costs.
In any field, especially cybersecurity, there are questions about ethics. Despite the potential efficiency boosts, the usage of AI can still have ethical implications. People with ill intentions can negatively leverage technology to harm or take advantage of people. These fears remain with generative AI. Though the technology is young, there are ways it can be exploited.
Bias is one of the most significant problems with generative AI. Bad-faith actors can train algorithms to have gender or racial prejudices — therefore, the programs will be discriminatory and misidentify particular groups as security threats.
Another ethical concern for generative AI is third-party data. Many organizations use it for cybersecurity, meaning more companies can access sensitive information. Laws and policies are in place to safeguard data, but it only takes one slip-up to cause chaos.
Generative AI for Cybersecurity
Generative AI is becoming popular among professionals and average users. This innovative technology has a place in cybersecurity because of its 24/7 protection and rapid threat detection. However, ethical issues and high price points can leave many out of the movement. The best thing companies can do is weigh the pros and cons before deciding if generative AI is right for them.
Devin Partida is an industrial tech writer and the Editor-in-Chief of ReHack.com, a digital magazine for all things technology, big data, cryptocurrency, and more. To read more from Devin, please check out the site.