Seventy percent of SOC analysts with five years or less of experience leave within three years. The typical explanation is burnout from an overwhelming threat landscape. The less comfortable explanation is that the tools meant to help analysts are making their jobs worse. Fragmented workflows, constant context-switching across disconnected platforms, and thousands of daily alerts with no actionable context are turning what should be a high-impact career into a repetitive grind. When analysts spend more time wrangling dashboards than investigating threats, the best ones leave.

The retention problem is not just a staffing issue. It is an operational risk. Every departure takes institutional knowledge with it, increases the load on remaining team members, and widens the window for missed detections. Organizations that want to keep experienced analysts need to redesign how SOC work gets done, starting with how detection, investigation, automation, and analyst experience are delivered across the stack.

Addressing this challenge requires coordination across SIEM, XDR, SOAR, MDR, and security analytics platforms to reduce friction, improve context, and make investigations more actionable.

Topics include:

• How fragmented tooling and manual workflows contribute to analyst turnover
• Reducing cognitive load through unified investigation and automated triage
• Building a SOC environment that retains talent by making the work sustainable

Join us to explore how rethinking SOC tooling and workflows can address the retention crisis at its source.

Every security vendor now claims AI capabilities. For SOC teams already processing thousands of alerts per day, the promise is appealing: automated triage, intelligent prioritization, faster investigations. The reality is more complicated. Poorly implemented AI can generate its own layer of noise, create false confidence in automated decisions, and introduce opaque reasoning that analysts cannot validate or trust.

The SOC teams seeing real results from AI are the ones asking the right questions before deploying it. They are auditing data quality first, defining what “automated” should and should not mean for their environment, and measuring whether AI is reducing time-to-resolution or just shifting where analysts spend their time.

Getting this right requires alignment across detection, triage, investigation, and automation layers of the SOC – from SIEM and XDR to SOAR, MDR, and AI-driven analytics platforms.

Topics include:

• Evaluating AI-driven SOC tools based on measurable outcomes, not vendor claims
• Addressing data quality and pipeline readiness before deploying AI-powered detection
• Defining the right division of labor between automated triage and human investigation

Join us for an honest look at where AI is delivering real value in security operations and where it is falling short.