Around 2010, traditional antivirus solutions, relying mainly on signature-based detection, started to be considered insufficient as attackers developed methods to run malicious code without installing recognizable malware, bypassing traditional defenses.
There was document-based malware, with harmful scripts embedded in document files (Excel, PDF, Word, PowerPoint, etc.), often delivered through phishing campaigns. Fileless attacks executed processes in memory or exploited trusted system processes, making them invisible to signature-based detection tools. The EternalBlue exploit, used by malware like WannaCry and NotPetya, is probably going to remain forever in the history textbook of cybersecurity. Traditional antiviruses were only effective against known malware, missing a significant portion of new threats. Early EDR software products were complex and could lead to alert overload, requiring significant security expertise and resources to operate effectively.
The term “Endpoint Detection and Response” was officially introduced into mainstream jargon in 2013 by Gartner’s analyst Anton Chuvakin who conceptualized it as a solution to provide more in-depth visibility into system activities and to detect and investigate suspicious activities on hosts and endpoints.
As the field of cybersecurity evolves, so do its tools and one of the main trends noted by specialists is a move towards integration of security platforms. For example, Gartner anticipated in 2019 a convergence of EDR and EPP resources into unified systems managed through a singular interface. These integrated solutions offer faster threat detection and automated responses, marking a significant evolution in endpoint security practices and toolsets.
Another powerful trend is cloud-powered solutions that offer endpoint protection, endpoint detection and response, mobile threat defense, and integrated vulnerability management. Advanced EDR solutions will almost certainly continue to leverage automation, machine learning, and AI to increase efficiency, and tighter incorporation of User and Entity Behavior Analytics (UEBA) to detect anomalies based on user behavior.