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DFLabs Unveils Machine Learning Powered First Responder Automation for Security Operations

By   /  July 25, 2017  /  No Comments

by Angela Guess

According to a new press release, “DFLabs, the leader in Security Automation and Orchestration Technology, announced today the release of its new “Playbook Recommendation and Intelligent Selection Mechanism” (DF-PRISM), enhancing DFLabs’ security automation and orchestration (SAO) platform with incorporated proprietary machine learning. The system uses patent pending advanced methods and algorithms to ingest operational intelligence such as security incident and resolution data to recommend playbooks and actions based on historical incident response activities. This approach minimizes the resources and time required to successfully analyze and respond to ongoing incidents, while maximizing the effectiveness and efficiency of security teams.”

The release goes on, “At its core, DFLabs’ SAO enables security organizations to take a gradual “crawl, walk, run” path to developing efficient processes for successfully responding to and managing threats as well as hardening security controls. Beginning with “Human Guided Learning” and evolving to “Human Supervised Learning,” users can create and apply simple, linear or conditional playbooks that combine manual, semi-automated and automated actions. Decision-making and conditional responses can be made manually by humans, automatically by machine, or a hybrid of the two – depending on the needs, requirements and maturity of the organization. Mature organizations can leverage DF-PRISM’s advanced ‘Runbooks.’ These support complex and stateful logical decision making to enable an advanced and adaptive threat management program. Runbooks can be used to fully automate the triage, hunting and investigation and containment of incidents using conditional responses that allow users to pursue a variety of alternative responses.”

Read more at Business Wire.

Photo credit: DFLabs

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