Imagine yourself as a SOC analyst and a new alert pops to the top of the queue. You open the alert and all of the data you need to make a decision is present. In just a few seconds you’ve notified the customer of a potential security incident. After completing the investigation, you find yourself waiting in vain for another alert to appear in the queue (hey, we’re dreaming here, right?!). An alert finally pops up but it’s immediately triaged by the system; a combination of AI and automation classified the alert as benign, leaving you time to finish your research into the latest MFA bypass techniques.
Now, imagine you lead the teams that enabled both of these scenarios. Your teams created the detection logic that produced the alert, built the response actions that enriched the alert, and automated the SOC playbook that made the decision. You prioritized this work with the data-driven detection program you’ve established.
But you can’t stop at the prioritization and creation of content. Your team is also a core driver of the continuous evolution of the platform. You have a running list of the features you need built to mitigate the running-even-faster list of emerging TTPs, and it's all backed up with research. The research spikes you prioritized are informed by the continuous detection gap analyses your teams perform of the attacks we see every day. This is why you worked so closely with the Data Science team to build the ML model that now safely auto-closes false positive alerts.
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