Automation index
How much of a Penetration tester’s job can AI already do?
A scan and a template report come earlier. Finding the weird chain, and not taking production down, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Penetration tester
26%
AI can already do 26% of this job.
#207 / 623
View rankingPenetration tester
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 |
|---|---|---|---|
Recon, scans and enumeration.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 42% | +8.4 |
Write the report, evidence and CVSS.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 38% | +6.8 |
Exploit with judgment and not break the business.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Debrief with the client and retest.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Choose scope, rules and what is off limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Research 0-days, labs and keep the craft.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Recon, scans and enumeration
- Write the report, evidence and CVSS
- Debrief with the client and retest
- Choose scope, rules and what is off limits
- Research 0-days, labs and keep the craft
- pentester
- ethical hacker
- pentest
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