Cyber risk specialist
AI Exposure Rank
86/100
Range 85–87/100 across source-weight sensitivity checks
Cyber risk specialist has an AI Exposure Rank of 86/100, meaning its work is more exposed to current AI capabilities than approximately 86% of Singapore occupations. The evidence currently points to demand buffered redesign; this is a relative rank, not a probability of job loss.
Professionals·SGD 10,071/mo (7,579–13,583)·~5.1K workers in SG·Updated 2026-07-17
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
Mixed signal: This occupation has very high relative AI exposure but is currently on the Shortage Occupation List — indicating labour shortage despite AI exposure.
How This Rank Is Built
The source percentiles are combined using the displayed reliability weights, then ranked against all 562 Singapore occupations. This is not a percentage of tasks and not a job-loss probability.
Likely job pathway
Demand-buffered redesign
Current demand context
Strong current demand
Pathway and demand are reported beside the exposure rank. Current demand does not change the rank.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. How this works
Tasks AI can handle
With 83% AI task overlap (based on Felten AIOE), the Cyber risk specialist tasks most exposed include: code generation, test writing, documentation, code review suggestions, and debugging common patterns.
What AI can't do here
At 27% human bottleneck protection, the tasks that remain hardest to automate for Cyber risk specialist include: system architecture decisions, complex debugging in production, cross-team coordination, requirements gathering, and security-critical code review.
Skills to focus on
Dell'Acqua et al. (2023) found consultants using AI improved quality 12-40% depending on task boundary — but performance dropped when AI was used outside its capability frontier ("jagged frontier" effect).
Sources: Felten AIOE (2021), Pizzinelli et al. bottleneck model. Full methodology.
From exposure to employment
What could change the employment outcome?
AI capability is only the starting point. Adoption, task design, human oversight, demand, mobility and evidence quality determine how exposure may resolve in practice.
Workplace adoption
Leading sectors represented
All listed industries have MOM sector adoption evidence; observed-sector average 62.7%.
Task structure
Task evidence limited
Task-weighted shadow evidence is not active for this occupation yet.
Human advantage
Coordination and judgment still matter
At 27% human bottleneck protection, the tasks that remain hardest to automate for Cyber risk specialist include: system architecture decisions, complex debugging in production, cross-team coordination, requirements gathering, and security-critical code review.
Hiring and demand
3.4% vacancy rate · 2026 Q1
PMET vacancies increased from Q4 and remained below their year-ago level. Hiring stayed net positive but slowed, while retrenchment incidence remained the highest of the three broad occupation groups. In 2025, this broad occupation group had 0.9% time-related underemployment and 6.9% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -0.8% to +2.3% from 2018 to 2023.
Career transitions
Cloud specialist
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
low confidence
1 exposure sources · sensitivity 85–87/100 across source-weight sensitivity checks.
These lenses are reported separately and do not alter AI Exposure Rank. Labour-market figures describe a broad official occupation cluster, not this SSOC occupation alone.
Singapore Now
Current labour market conditions and how they affect this role.
Local conditions are under more strain. Vacancies have softened and displacement signals are less forgiving.
Vacancy
3.4%
↓ 2.9% YoY
Hiring
1.2%
vs 0.7% resign
Retrenchment
2.6
per 1,000 · moderate
Re-entry
69.1%
find work in 12mo· +1.4pp
Professionals, Managers, Executives & Technicians · 2026 Q1
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Cyber risk specialist has some offset potential, but it depends on transition pathways holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Exposure-reducingHigher AI exposure, but comparatively credible exposure-reducing moves exist — the strongest scores 71% match. Escape-route quality and labour demand matter alongside exposure.
Compare within Professionals
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 86% of occupations · V8 AI Exposure Rank· University Degree
Raw scores
AIOE 1.241 · θ 0.648 · C-AIOE 1.039
Stability
stable · Optimistic 32% · Pessimistic 41%
Score range (best/worst case)
Exposure sensitivity 83–83% · Rank sensitivity 85–87/100 across source-weight sensitivity checks
Scoring basis
V8 AI Exposure Rank. A relative Singapore occupation index. It ranks AI task exposure; it is not a probability of job loss or a percentage of tasks.
Wage range (SGD/mo)
25th 7,579 · Median 10,071 · 75th 13,583
Evidence & sources
Data matching
direct · SSOC 25241
SOL 2026: exact match
Data quality
low evidence · 1 exposure sources · direct mapping
Task-weighted shadow evidence is not active for this occupation yet.
AI overlap by data source
Weights: aioe 100%
Conflicting data signals
Worker profile & local context
- Vacancy rate is 3.4% and rose by 0.3 points from last quarter.
- Hiring read: recruitment is running above resignation (1.2% vs 0.7%).
- Retrenchment was moderate at 2.6 per 1,000 employees.
- 69.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is moderate, based on 7 captured Singapore-relevant company signals through 2025-08-12.
Worker profile
Gender mix
70% male / 30% femalePublished Singapore worker composition for the detailed occupation family 25 Information & Communications Technology Professionals.
Employment structure
Employee-heavy96% employees, 4% employers or self-employed workers.
Work arrangement
Mostly full-time4% part-time and 96% full-time in 2025.
Age profile
Mid-career heavy14% aged 15 to 29, 62% aged 30 to 49, and 24% aged 50 or older.
Qualification mix
Degree-heavyDegree 81%; Diploma / professional qualification 15%.
Gross wage by sex
Female median 7% lowerPublished June 2024 gross wage medians: male $10,326, female $9,648.
Where this work is concentrated
Top planning areas
Sengkang, Bedok, Tampines19% of workers in this occupation group live in these three planning areas.
Residential concentration
Broadly distributed30% live across the top five planning areas in the 2020 Census.
Commute pattern
Mid-range commutesEstimated average commute 37.5 minutes. 33% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
General role profile on a 0–100 scale. These are modelling inputs, not measurements of an individual job or worker.
How this changes by career stage
Career stage can change the task mix and human context. These directional profiles are illustrative, not occupation-level forecasts of hiring or displacement.
Frequently asked questions
Will AI replace Cyber risk specialist?
Cyber risk specialist has an AI Exposure Rank of 86/100, meaning its work is more exposed to current AI capabilities than approximately 86% of Singapore occupations. The evidence currently points to demand buffered redesign; this is a relative rank, not a probability of job loss. AI Exposure Rank: 86/100 (Very High). Median wage: SGD 10,071/month.
What is the AI exposure rank for Cyber risk specialist?
Cyber risk specialist has an AI Exposure Rank of 86/100, rated Very High. It ranks higher than approximately 86% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Cyber risk specialist?
Cyber risk specialist has modeled transition pathways to related occupations. The strongest adjacent pathway is Cloud specialist, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Cyber risk specialist salary compare in the live market?
Cyber risk specialist earns a median gross wage of SGD 10,071/month in the live market (25th-75th percentile: SGD 7,579-13,583). This is 124% above median across all 562 scored occupations, and 55% above group median within Professionals occupations.