Prior to using ExtraHop, the organization lacked a centralized system for automated network data analysis. This created "blind spots" where active ransomware and data exfiltration could remain undetected until after an impact occurred.

The organization relied on manual, post-incident data gathering that slowed issue resolution. Without the ability to perform PCAP replay, engineers could not effectively reconstruct past network events, leading to extended mean time to resolution (MTTR) and increased operational overhead.

Technical teams functioned in isolation due to siloed data sources. This lack of integration made it difficult to establish a single source of truth for global operations, particularly during high-traffic shopping periods where transaction stability is vital.

The retail 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 retail 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.