AI SOC in Production: Expectations vs. Reality

This post isn’t about the AI hype. It’s a practical view of AI SOC implementation and lessons learned deploying AiStrike with real customers.

1. Fit Seamlessly into Analyst Workflows:

AI should enhance, not disrupt, how analysts work today. Customers don’t want a complete process overhaul; they want AI to slot in seamlessly while improving outcomes.
Example: At one customer, Tier 1 analysts created tickets with detailed investigation summaries for Tier 3 analysts. Our AI SOC needed to produce the same outputs in the same ticketing format, ensuring analysts had what they needed without changing how they worked.
If your AI solution adds friction or increases workload, even the smartest AI won’t earn trust.

2. Automate the Entire Workflow, Not Just Parts:

A beautiful dashboard or summary isn’t enough if your AI SOC doesn’t replace the end-to-end steps analysts take to investigate and respond to threats.
An effective AI SOC must fully operationalize workflows end-to-end, not just partially support them.
Example: At one customer, Tier 1 analysts pulled context from multiple tools outside of the SOC to complete their investigation. Our AI SOC had to automate this exact process, building new connectors and integrations as needed.

3. AI SOC: Knowing Where it Adds Value:

AI SOC isn’t a silver bullet for every SOC problem. Challenges like legacy SIEM limitations, incomplete telemetry, or low-quality threat intel are data and tool gaps that AI can’t fill.
We help customers:

• Map current pain points.
• Define where AiStrike fits best and where you need other tooling or architecture and process changes.

Being clear about where AI SOC drives value helps ensure measurable outcomes while building a realistic improvement plan.

4. Prove Value with Tangible Metrics:

Deploying AI SOC isn’t enough. You need to prove it delivers better results. Metrics that resonate:

• Qualitative: Comparing AI-led investigations with human investigations. Are the conclusions different? Better? What additional context did AI uncover?
• Mean Time to Investigate (MTTI): Is AI actually reducing investigation time compared to analysts?
• False Positive Reduction: Are escalations decreasing, reducing analyst workload?
• False Negative Improvement: Is AI surfacing true positives that would otherwise be missed?

If you can’t measure it, you can’t prove it—and customers won’t see value.

Final Thoughts:

AI SOC can transform security operations, but success depends on seamless fit, end-to-end operationalization, and measurable outcomes.
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Start small, prove value, then expand.
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At AiStrike, we’re learning these lessons daily to ensure AI delivers real value—not just another single pane of glass.
Exploring AI SOC? Let’s connect. Always happy to share what we’re seeing in the field.

#AISOC, #SecOps
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