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Antivirus software is evolving from relying on static databases of known malware signatures to employing behavioral monitoring and machine learning for threat detection. Traditional antivirus solutions focused on recognizing known threats through unique signatures, but this approach has become inadequate due to the rapid evolution of malware, including polymorphic and metamorphic types. Modern antivirus systems now monitor program behavior, looking for suspicious activities such as unexpected file encryption or unusual network communication. Machine learning models analyze large datasets to identify patterns associated with malware, allowing for the classification of files as safe, potentially unwanted, or malicious. Techniques like sandboxing and dynamic analysis are used to preemptively neutralize threats. However, advancements in AI also present challenges, as cybercriminals can exploit these technologies to create sophisticated malware that evades detection. Despite improvements in antivirus effectiveness, modern cyberattacks increasingly target individuals through methods like phishing and social engineering, necessitating a combination of robust antivirus solutions and good cybersecurity practices.