Automation index
How much of a SOC analyst’s job can AI already do?
An alert and a playbook come earlier. Calling it an incident and cutting it off in time do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
SOC analyst
28%
AI can already do 28% of this job.
#80 / 623
View rankingSOC analyst
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Triage, SIEM and the alert playbook.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
The false positive and the one that is ransomware.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
IOCs, correlation and the ticket.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
24x7 shift, handoff and the note to the CISO.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Contain, isolate and not killing the business.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Cut it off in time, and not ignoring the awkward alert.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- alert triage
- SIEM
- SOC playbooks
- IOCs
- contain incidents
- 24x7 shift
- ransomware
- event correlation
Sources for this job· O*NET 15-1212.00· ESCO/ISCO 2529
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511