Additional Info

Company size (employees)500 to 999
Headquarters RegionNorth America


ExtraHop Reveal(x) provides cloud-native network detection and response for the hybrid enterprise. The breakthrough approach analyzes all network interactions in real time and applies advanced machine learning to help security operations practitioners investigate threats, ensure the delivery of critical applications, and protect investment in the cloud. Reveal(x) delivers real-time threat detection and response from the closest source of ground truth in the enterprise: the network.

ExtraHop is the first to combine automated discovery and asset classification of every asset and transaction on the network with cloud-based machine learning, anomaly detection and critical asset focus for high-fidelity, low volume alerts and zero false positives. Reveal(x) goes beyond detection to support incident response with contextual visualization, one-click access to forensic evidence, and simple integrations to automate and orchestrate rapid response.

At the core of Reveal(x) is analytics and machine learning technology that’s been tested in the most demanding enterprises to deliver complete situational intelligence and automated threat detection and investigation. Regardless of environment — from the data center, to cloud infrastructure and SaaS, to remote sites and device edges — all business transacts on the network, which is why ExtraHop leverages the network as the ultimate source of truth.

Machine learning technology detects anomalous behavior to discover threats as they’re happening, and analysts receive a triaged list that can be explored immediately through real-time access to forensic-quality data with analytics surveying the most critical assets first. Reveal(x) cuts investigation time from days to seconds, freeing up time for security analysts to focus on the threats that matter.

ExtraHop was founded with a bold vision: help enterprises rise above the noise of alerts, organizational silos, and runaway technology by giving security and IT teams the clarity, confidence, and agility they need to embrace the cloud-first future.

How we are different

ExtraHop is at the forefront of helping customers build security the way they build the business: cloud first. The company built Reveal(x) with the input of its customers, who include Fortune 500 and Global 2000 companies, and continues to innovate in partnership with Microsoft, Amazon, and Google. The reality of cloud requires a new approach to security. Reveal(x) delivers real-time threat detection and response from the closest source of ground truth in the enterprise: the network. Reveal(x) uses stream processing to auto-discover and classify every transaction, flow, session, device, and asset in your enterprise — including data centers, cloud-hosted applications, remote branches, and IoT — at up to 100 Gbps and 1 Pb/day.

Organizations are faced with severely understaffed and overworked security teams. Reveal(x) improves security analyst productivity through contextual discovery of the enterprise attack surface, full-spectrum detection, and one-click guided investigations for incident response. Advanced detections incorporate device and user context to identify known and unknown threats using an array of machine learning, rule-based, and custom techniques.

A new crop of security start-ups are trying to fill the void left by the security skills gap – but not on the backs of strong products or battle-tested algorithms, but through hype and fear. And while these new-age security companies may offer machine learning and AI capabilities, not all ML is created equal. An ML platform that analyzes bad or incomplete data results in a sea of alerts filled with false positives and poor insights that will be of little service to in-demand security teams. By decoding 70+ enterprise protocols and decrypting SSL/TLS traffic, Reveal(x) delivers unmatched breadth, accuracy, and focus into all network transactions and enterprise operations. In real-time, Reveal(x) extracts 5000+ metrics from this data to power precise, machine learning-driven behavioral analysis.