Automation index
How much of a CISO’s job can AI already do?
A risk dashboard comes earlier. Saying no to the business and riding out the incident do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
CISO
25%
AI can already do 25% of this job.
#217 / 623
View rankingCISO
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 |
|---|---|---|---|
Risks, policies and the control map.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
The incident, the board and talk to press or not.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Budget, vendors and cyber insurance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Audit, ISO and briefing the board.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business that wants to ship now, and the no.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Ride out the incident, and not lying to the board.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- risk map
- security policies
- handle incidents
- ISO 27001
- cyber insurance
- brief the board
- say no to the business
- security controls
Sources for this job· O*NET 11-3021.00· ESCO/ISCO 1330
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