The Respond Analyst™ Extends Its Automation Capabilities to Give Security Teams More Visibility and Better Results
Extended vendor coverage and a new deductive processing feature enhance the product’s automated judgement skills to augment security teams in their efforts to defend their enterprise.
MOUNTAIN VIEW, Calif.—March 26, 2019— Respond Software, innovators in Robotic Decision Automation (RDA) software for security operations, today announced new enhancements to its flagship product, the Respond Analyst™, with expanded analysis of vendor data sources and the introduction of a new feature called Inferred Context. With the added capabilities, Respond’s product increases its visibility into customers’ existing security telemetries and uses first-of-its-kind automated inferential information to increase the accuracy of its results, saving customers significant monitoring and analysis time and effort.
“The top frustrations for me as a SOC director was realizing how much time my analysts spent chasing false positives or hunting down the source of a threat,” said Chris Calvert, Founder and Vice President of Product Strategy, Respond Software. “We believe that we need to fundamentally change frontline security operations with machine power—Inferred Context takes a big step towards that goal.”
To make an informed decision, front-line security analysts need accurate information about the IT environment; however, critical asset lists are often inaccurate or incomplete. This lack of reliability makes it difficult for security analysts to identify and resolve security incidents and is compounded by the fact that missing, incomplete or inaccurate asset data and host context often increase the risk of false positives.
Inferred Context connects the dots for security teams for faster, more accurate decisions
The Respond Analyst improves its ability to determine the severity of a security incident and, ultimately, whether or not it is worthy of escalation, through automated asset classification and host attribution – a new feature called Inferred Context. Inferred Context simulates human reasoning to infer specific asset classifications—based on observations/information found in vulnerability scans (e.g. open ports) or Endpoint Detection and Response (EDR) solutions, such as Tanium. The Respond Analyst categorizes assets by varying degrees of criticality to use in its decision engine, PGO®.
Expanded analysis of data sources offers more robust security vendor coverage
The Respond Analyst seamlessly integrates with many leading security systems. Respond Software expands this support to give security teams more visibility into their existing alerting telemetries for the following systems:
- Endpoint Protection Platforms, including Trend Micro Deep Security, Trend Micro Officescan, and Palo Alto TRAPs;
- Web proxy/URL filtering, such as McAfee Secure Web Gateway; and
- Network IDS/IPS such as Checkpoint.
To learn more about Respond Software’s real-time security analysis solutions visit: https://respond-software.com/.
- Introducing “Inferred Context” or How to Enjoy a Spring Day
- Video: A breakdown of Inferred Context by Chris Calvert
- 2018: The Year We Introduced Robotic Decision Automation to the World
About Respond Software
Respond Software delivers near-instant return on investment to organizations in their battle against cyber-crime. As a leader in the emerging class of automated software known as Robotic Decision Automation (RDA), Respond Software is working to address the critical shortage of skilled security analysts impacting security teams of all sizes. Its patented intelligent decision engine, PGO®, uniquely combines human expert judgement with the scale and consistency of software to dramatically increase capacity and improve monitoring and triage capabilities at a fraction of the cost of in-house or outsourced personnel. Respond Software was founded in 2016 by security and software industry veterans and services customers across critical infrastructure sectors such as banking, energy, and retail. https://respond-software.com/