Experts believe that Artificial Intelligence (AI) and Machine Learning (ML) have both negative and positive effects on cybersecurity. AI algorithms use training data to learn how to respond to different situations. They learn by copying and adding additional information as they go along. This article reviews the positive and the negative impacts of AI on cybersecurity.
Attacks are becoming more and more dangerous despite the advancements in cybersecurity. The main challenges of cybersecurity include:
Geographically-distant IT systems—geographical distance makes manual tracking of incidents more difficult. Cybersecurity experts need to overcome differences in infrastructure to successfully monitor incidents across regions.
Manual threat hunting—can be expensive and time-consuming, resulting in more unnoticed attacks.
Reactive nature of cybersecurity—companies can resolve problems only after they have already happened. Predicting threats before they occur is a great challenge for security experts.
Hackers often hide and change their IP addresses—hackers use different programs like Virtual Private Networks (VPN), Proxy servers, Tor browsers, and more. These programs help hackers stay anonymous and undetected.
Cybersecurity is one of the multiple uses of artificial intelligence. A report by Norton showed that the global cost of typical data breach recovery is $3.86 million. The report also indicates that companies need 196 days on average to recover from any data breach. For this reason, organizations should invest more in AI to avoid waste of time and financial losses and.
AI, machine learning, and threat intelligence can recognize patterns in data to enable security systems learn from past experience. In addition, AI and machine learning enable companies to reduce incident response times and comply with security best practices.