Prior to using ExtraHop, the organization relied on a legacy NDR vendor that suffered from lost packets. This meant the SOC was unable to fully trust the information provided, relegating the solution to a secondary checking role that was insufficient for the scale of the business.

The manufacturer struggled with detections that failed to provide the detail as to "why" a threat occurred. Without this level of information, the team could not effectively track lateral movement, which was a major concern for the organization.

With manufacturing operations spread across three major regions, the customer needed a solution that could deploy globally and handle the challenge of encrypted traffic without introducing performance risk.

The automotive leader secured the required forensic depth and network control when it deployed ExtraHop, which analyzes 100 Gbps of east-west traffic and uses high-speed decryption to immediately find threats previously hidden within encrypted flows.

The cloud-scale machine learning built into the ExtraHop platform reduced the SOC's operational burden by providing high-fidelity, low-noise detections. This shift allowed analysts to move their focus from low-value false positives to highly reliable network activity, signaling true post-compromise threats and endpoint detection and response (EDR) evasion tactics.

The security team achieved comprehensive insight by using identity-based investigation, which links malicious network activity directly to user and service accounts, finally enabling the detection of all missed AD and lateral movement attacks.

ExtraHop fundamentally simplified incident response workflows because it established itself as the definitive source of network truth, automatically feeding high-value contextual data to the customer’s existing SIEM and EDR platforms.

The organization gained efficiency and reduced complexity by consolidating NDR, NPM, and IDS capabilities into one unified, integrated solution for comprehensive network security and observability.

The automotive manufacturing leader mitigated major risk by gaining deep fluency (parsing over 90 protocols) that allowed for accurate decoding of all traffic, including sensitive database communications, without introducing performance risk. This was critical for detecting hidden AD attacks and lateral movement.