{"id":3405,"date":"2017-10-09T00:00:00","date_gmt":"2017-10-09T07:00:00","guid":{"rendered":""},"modified":"2025-06-26T00:44:58","modified_gmt":"2025-06-26T07:44:58","slug":"how-azure-security-center-uses-machine-learning-to-enable-adaptive-application-control","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/how-azure-security-center-uses-machine-learning-to-enable-adaptive-application-control\/","title":{"rendered":"How Azure Security Center uses machine learning to enable adaptive application control"},"content":{"rendered":"\n

While the threat landscape has changed dramatically over the last several years, malware detection continues to be one of the biggest issues. There is an endless race – attackers develop new malware, security vendors create new signatures to detect it, a new variant of the malware is created to avoid detection, and the cycle continues. Malware is not the only type of application that can expose a server to risk. Unauthorized software can introduce vulnerabilities that are exploited by attackers. Most organizations lack the necessary application tracking and controls, leaving them blind to these risks.<\/p>\n\n\n\n

Application controls, such as whitelisting, can help limit exposure to malicious and vulnerable applications. Instead of trying to keep pace with rapidly evolving malware and new exploits, application whitelisting simply blocks all but known good applications. For purpose-built servers that typically run a fixed set of applications, whitelisting can offer significant added protection. You can use application controls to:<\/p>\n\n\n\n