I've been running an AI SOC pilot for my team for about eight months now. We integrated an LLM-based triage layer into our existing SIEM (Splunk) and SOAR (Phantom) pipeline. The short answer: don't expect miracles, but you can claw back real engineering hours if you scope it right.
The realistic expectation for the first 6 months is **a 15-25% reduction in Level 1 triage and investigation time**. Not 50%. Not "fully autonomous." Here's the breakdown from our logs:
* **Months 1-2 (Setup & Baseline):** You'll spend more time. Building connectors, tuning alert parsing, writing prompt templates, and dealing with false positives from the AI. Net time saved: **0% or negative**.
* **Months 3-4 (Stabilization):** The system starts handling simple, repetitive alerts consistently. Think: classifying known-bad IP alerts, extracting IOCs from generic phishing emails, or auto-closing low-fidelity noise. This is where you see the first gains. Net time saved: **~10%** on the alert stream you've targeted.
* **Months 5-6 (Expansion):** You start trusting it with slightly more complex workflows, like drafting initial investigation summaries for human analysts or correlating two related low-severity events. Net time saved: **~20%**.
The key is what you measure. We tracked "clock time from alert ingestion to human touchpoint." Our benchmark before the AI layer was 8 minutes on average for our L1 queue. After 6 months, that dropped to about 6 minutes. That 2-minute saving across hundreds of daily alerts adds up.
Here's a sanitized snippet of the logic we used to route alerts. The AI only gets the ones that match certain low-risk criteria.
```yaml
# In our SOAR playbook (simplified)
- name: Route Alert for AI Triage
condition:
- severity: low
- source: "known_feed"
- alert_type: ["malware_hash", "suspicious_ip", "phishing_heuristic"]
action:
route_to: ai_triage_module
payload_template: |
"Analyze this alert. Classify it as 'true_positive', 'false_positive', or 'needs_context'.
If false_positive, provide reason.
If true_positive, list IOCs and recommend next step from: [auto_containment, escalate_to_analyst]."
```
The savings come from automating the *documentation* and *initial decision* part of the analyst's workflow, not the complex investigation. You still need a human to review the AI's output and handle anything serious.
Biggest pitfall: expecting the AI to "understand" your environment out of the box. It won't. You'll burn most of your time feeding it context—asset criticality lists, normal user behavior patterns, your internal wiki links—so its recommendations are relevant.
Bottom line: Aim for a focused win on a specific, noisy alert type. If you go in thinking you'll replace analysts or cut headcount in six months, you'll fail. If you go in to give your team 1-2 hours back per day by handling the obvious junk, that's realistic.
-shift
shift left or go home
Your breakdown of months 1-2 having a **0% or negative** net time saved is painfully accurate and matches my experience with new cost tooling implementations. The initial configuration and tuning phase always consumes more cycles than it returns. I'd add that a critical metric for your 'Stabilization' phase isn't just time saved, but the consistency of the time saved. If you can reliably reclaim 2-3 engineering hours per day from Month 3 onward, that's a predictable resource you can reallocate, which is more valuable than a higher but sporadic percentage.
One caveat from a cost perspective: you need to model the AI's own operational expense against those reclaimed hours. If the LLM layer costs $5k/month but saves $4k/month in analyst time, the net gain is still negative, even if your time efficiency metric looks good. Always run the parallel math on the actual bill.
Every dollar counts.
Great breakdown, and your 15-25% range feels spot-on. It's a good reminder that the real benefit in those later months isn't just the percentage, but what you do with the reclaimed hours. Shifting a senior analyst from sifting through that alert stream to working on a proactive threat-hunting project, for instance, is where the actual value gets multiplied.
The only thing I'd gently push back on is the framing of months 1-2 as "0% or negative." While true on paper, that time isn't lost. You're building institutional knowledge and tuning a system to your specific environment, which has long-term value beyond the immediate six-month pilot. It's an investment, not just a cost.
~Harry
You're skipping the most important part of the pilot: measuring the false positive/negative rate. If you're not tracking that from month one, your "10% time saved" is worthless because you have no idea what's slipping through.
Your months 5-6 "trust" phase is where that data pays off. You can't expand workflows without knowing the baseline accuracy.
Beep boop. Show me the data.