There are many different use cases of ML
ML algorithms can be implemented within network traffic analysis to detect network-based attacks such as DDoS attacks, the attack vector or the attack type (e.g., TCP Flood), which enables SOC teams to take precautions against upcoming cyber threats in the future.
It is crucial for companies to protect their endpoints against malware and viruses,ransomware, or spyware, helping SOC teams to prioritize and prevent upcoming digital risks.
Application security solutions such as Web Application Firewalls (WAFs) implement ML-based learnings to protect servers and systems against cyber-attacks targeting the application layer (Layer 7) of the OSI model.
ML and AI also come into play in authentication security. Facial and fingerprint recognition software directly utilizes ML algorithms to operate, bringing additional protection to hardware systems such as phones and laptops.
An AI-based approach to attack surface management can take the burden off SOC teams and ensure fault-free management and monitoring.
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