Customer Support Manager at a tech vendor with 1-10 employees
Real User
Top 20
Oct 7, 2026
Our main use case for UnderDefense Agentic AI SOC is to watch how accounts actually use our platform. Our product is a large database of creator data that customers search and export, so the biggest risk we carry is someone turning a legitimate account into a pipeline for bulk extraction. We rely on it most for catching that early: a sign-in pattern that does not match how the account normally works, an export volume that jumps past what a real campaign would need, or usage that ramps in a way no genuine customer does. That is the part we count on day to day, and it protects the data set itself, which is what customers pay for. An example of how it catches something suspicious is when it flags behavior that breaks from an account's own baseline. For us, that looks like an export volume several times higher than the account's normal range in a single session, or a sign-in from a new location right before a large batch pull of creator profiles. When that happens, the alert already lays out which signals triggered it and why it reads as different from normal agency activity. Our team can look at the account and decide whether it is a big campaign push or a compromised login, usually the same day. The alerts land in the channel our engineers already watch, so there is no separate tool to check. Most days there is nothing, and when something does come through, it is usually one of us on the account team who looks first, since we know which clients run big legitimate batches around launches. If it still looks off, we loop in engineering. It has become part of how we trust the exports feature enough to keep it open for agencies.
Find out what your peers are saying about UnderDefense, Fidelis Security, Red Canary, a Zscaler company and others in Endpoint Detection and Response (EDR). Updated: October 2026.
EDR solutions provide advanced capabilities for detecting and responding to threats on endpoints. These tools enhance security teams' ability to detect, investigate, and remediate incidents.EDR solutions offer enhanced endpoint protection by integrating real-time monitoring and advanced analytics. Designed for modern security teams, they facilitate immediate threat detection and remediation, effectively mitigating risks. User feedback highlights their capability to analyze extensive data...
Our main use case for UnderDefense Agentic AI SOC is to watch how accounts actually use our platform. Our product is a large database of creator data that customers search and export, so the biggest risk we carry is someone turning a legitimate account into a pipeline for bulk extraction. We rely on it most for catching that early: a sign-in pattern that does not match how the account normally works, an export volume that jumps past what a real campaign would need, or usage that ramps in a way no genuine customer does. That is the part we count on day to day, and it protects the data set itself, which is what customers pay for. An example of how it catches something suspicious is when it flags behavior that breaks from an account's own baseline. For us, that looks like an export volume several times higher than the account's normal range in a single session, or a sign-in from a new location right before a large batch pull of creator profiles. When that happens, the alert already lays out which signals triggered it and why it reads as different from normal agency activity. Our team can look at the account and decide whether it is a big campaign push or a compromised login, usually the same day. The alerts land in the channel our engineers already watch, so there is no separate tool to check. Most days there is nothing, and when something does come through, it is usually one of us on the account team who looks first, since we know which clients run big legitimate batches around launches. If it still looks off, we loop in engineering. It has become part of how we trust the exports feature enough to keep it open for agencies.