Automated anomaly detection: Using machine learning to rapidly identify known bad behaviors is a great use case for security. After first profiling devices and understanding regular activities ...

Contributor Content In 2025, integrating artificial intelligence (AI) and machine learning (ML) into cybersecurity is no longer a futuristic ideal but a functional reality. As cyberattacks grow more ...

Approximately 51% of businesses use it for threat detection, 34% for predicting potential security ... As cyber threats become more complex, reliance on AI and machine learning is expected to ...

Saryu Nayyar is CEO of Gurucul, a provider of behavioral security analytics technology and a recognized expert in cyber risk management. The cyber threat landscape is continuously evolving, and it ...

Thankfully, artificial intelligence (AI) and machine learning ... future attacks. In addition to detecting threats, AI and ML are also instrumental in automating defensive responses. Malware ...

Additionally, advanced monitoring tools are essential for detecting and responding to cyber threats in real time. Security ... machine learning: Leveraging AI can significantly improve threat ...

In today’s digital age where cyber security threats loom around ... mitigating evolving threats. Meanwhile, machine learning algorithms can predict future threats by analysing past attack ...

As AI grows, two areas stand out as essential for the future ... Examples of machine learning in action include: Recommendation systems (like those on Netflix and Amazon) Fraud detection in ...

The key to enhancing cybersecurity lies in leveraging AI and machine learning ... including cyber threat intelligence, network detection and intrusion prevention, security incident event ...