Impacts of AI on cybersecurity

Artificial Intelligence and Machine Learning 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.

 
  
According to a report by Juniper Research, the use of AI for fraud detection and prevention is expected to save businesses $11 billion annually by 2023.

 
Key Challenges faced from Cybersecurity today are:
 
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.

 
A report says, Signature-based techniques can detect about 90% of threats. Replacing traditional techniques with AI can increase the detection rates up to 95%, but you will get an explosion of false positives. The best solution would be to combine both traditional methods and AI. This can result in 100% detection rate and minimize false positives.

 
Artificial Intelligence is widely used in web filtering to help organizations and individuals protect against malicious websites and content on the internet - just like SafeDNS does. Its AI algorithms analyze and classify web content in real-time, which allows web filtering software to quickly identify and block websites that contain harmful content, such as malware, phishing scams, and inappropriate material. These algorithms can also learn and adapt to new threats over time, making web filtering more effective and efficient.

 
Artificial intelligence and machine learning can improve security, while at the same time making it easier for cybercriminals to penetrate systems with no human intervention.