Prior to using ExtraHop, the organization lacked automated forensic packet capture. This forced highly skilled engineers to engage in manual data collection during outages or performance degradations, leading to high labor costs and extended mean time to resolution (MTTR).

The manufacturer struggled with siloed telemetry data that prevented a holistic view of launchpad performance. Without real-time insights, the team could not proactively identify bottlenecks in mission-critical systems, creating risk during high-stakes satellite deployments.

Troubleshooting efforts were often hindered by fragmented data sources, forcing different teams to function in isolation. This lack of integration made it difficult to establish a single source of truth for global operations, particularly during mission control scenarios where every second is vital.

With expanding operations and future Mars-related programs on the horizon, the manufacturer required a scalable architecture. The existing "blind spots" in launchpad performance were incompatible with the organization’s long-term goals for interplanetary mission success.

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

The semiconductor 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.