Despite huge investments of time, money, and tooling, modern security teams still struggle to create effective security operations (SecOps) programs capable of providing accurate results and keeping up with the volume and complexity of incidents they receive. This is due to an acute talent shortage that prevents the hiring, training, and retaining of experienced staff; the excessive costs associated with building an in-house SOC or outsourcing operations to a service provider like an MDR, and the growing amount of work to be done. In the face of these challenges, firms that build in-house SOCs or outsource to MDRs often face incomplete results, low efficacy, and inconsistent quality.

Machine learning and AI now offer an autonomous way to perform SecOps. This greatly increases capacity while also reducing security spend. Autonomous MDR software combines machine learning with deep security expertise to effectively tackle the time-consuming and repetitive tasks of alert triage, investigation, containment and remediation, in an infinitely scalable way. An autonomous, software-based approach to SecOps ensures that 100% of alerts are addressed, in a consistent manner, at the quality of a top-tier analyst possessing a high-degree of familiarity with your environment. A typical result of this approach is a 95% reduction in analyst workload for less than 50% of the cost of traditional approaches like an MDR or in-house SOC.