AI has recently dominated every field of life, especially after COVID-19. Artificial intelligence impacts every sector, like cybersecurity, education, information technology, and more, without discrimination. Many sectors benefit from AI, but it also has some negative impacts on them. As per reports, cybersecurity adopts AI faster than other sectors, enhancing security threats. On the other hand, the ratio of cyberattacks and phishing is increasing dramatically. In this scenario, AI hits the world’s cybersecurity badly and urges us to get more sophisticated systems to prevent these threats. Let’s have a look at the drawbacks of Artificial intelligence on cybersecurity.
Dual Use of AI
AI has a dual role in cybersecurity. First, it works as a cybersecurity solution; second, hackers use it to breach security.
Cybersecurity sectors get help from AI to avoid attacks. No doubt, it creates multilayered security around the systems. However, the same technology can also be used for various phishing attacks. In other words, AI is dual, and it is neither good nor bad. If you run a casino site, the top payid casinos for Aussies, you should take some safety measures before installing AI into your system. However, the wise and limited use of AI can reduce security threats to some extent but can never eliminate them.
AI More Prone to Cyberattacks
Hackers develop more sophisticated attacks with the help of AI that can’t be imagined. They create different phishing techniques to penetrate security infrastructure. As you know, AI automates things, and hackers launch automated attacks. For instance, they develop bots that harm the security system secretly, and even AI cannot detect them on the first attempt. To avoid AI-generated attacks, you should learn them in depth.
AI Can Be Deceived
Hackers can easily fool any AI system with adversarial attacks. But what are hostile or adversarial attacks? In such attacks, hackers try to fool a machine with data. They know machine learning algorithms work, and they trick them with their codes. For instance, they developed a spammy website that looks legitimate. However, the machine learning system doesn’t understand what is happening and becomes the victim of cyberattacks. So, if you rely solely on AI models, you should rethink your decision and perception. Remember one thing: There is no substitution for human effort. However, many organizations only trust the systems, which is not a good practice.
AI Is Not Errorless
If you consider the AI system perfect, you’re wrong. Some AI models are highly trained and advanced, but it doesn’t mean they can never make a mistake. In other words, AI-based systems create false positives when the system incorrectly flags a friendly activity as spam. Sometimes, things work in your favor, but AI doesn’t take them seriously. But there are many reasons behind it why AI does it sometimes. First, systems cannot process data, and AI detects it as a malicious activity. Overall, these errors reduce the productivity of the systems and humans. Remember one thing: AI also minimizes false positives when something goes wrong. But with all these benefits, you can’t say AI is errorless.
AI Relys on Structured Data
AI could be biased and work only according to the data structure. It means that it will give the results according to the data training. For example, if you put biased trained data in an AI system, you will show bias in the results. This increases cybersecurity threats as it creates false negatives when the system fails to flag malicious activity as such good. So, if you want to improve the cybersecurity of your systems, you need to manage the AI according to its nature. It may harm your overall systems and security if you don’t do it.
Final Word
If truth be told, AI is both a blessing and a curse simultaneously. It is neither good nor bad but neutral. Undoubtedly, it has provided a bundle of benefits to organizations and cybersecurity departments, but it is a culprit of security, too. But rational and limited use of AI will keep you safe, so you must take precautionary measures before welcoming AI into your systems.
