5-Year Projections (2026-2031) for Cybersecurity vs. Data Science Jobs
Based on 2026 data from sources like the U.S. Bureau of Labor Statistics (BLS), World Economic Forum (WEF), and industry reports, here’s a comparison of job projections for core roles (information security analysts for cybersecurity; data scientists for data science). Projections include U.S. and rough global estimates (assuming U.S. represents ~30% of global tech jobs in these fields, a common ratio from reports). I extrapolated 2026-2031 figures using compound annual growth rates (CAGRs) derived from BLS 2024-2034 data: 2.54% for cybersecurity and 2.93% for data science. Net changes account for growth only; AI impacts are discussed separately below.
Job Growth Projections Table (U.S. and Global Estimates)
| Aspect | Cybersecurity (Info Security Analysts) | Data Science / AI Roles |
|---|---|---|
| U.S. Base (2026 est.) | ~192,200 jobs | ~260,500 jobs |
| U.S. Projected (2031 est.) | ~217,900 jobs | ~301,000 jobs |
| U.S. Net Change (2026-2031) | +25,700 jobs (+13.4%) | +40,500 jobs (+15.5%) |
| Global Base (2026 est.) | ~640,000 jobs | ~868,000 jobs |
| Global Projected (2031 est.) | ~726,000 jobs | ~1,002,000 jobs |
| Global Net Change (2026-2031) | +86,000 jobs | +134,000 jobs |
| Annual Job Openings (U.S. avg., incl. replacements) | ~16,000–19,000 (BLS est. for full decade) | ~25,000–28,000 (BLS est. for full decade) |
| Related Fields Growth | Strong in IT support, network admins (+5–10%); AI security specialists emerging at 15–20% CAGR due to new threats. | High in ML engineers (+20–25%), AI ethicists; but analysts (+10%) may slow as AI automates routine tasks. |
These are extrapolated from BLS 2024-2034 growth of 28.5% for cybersecurity and 33.5% for data science, adjusted for the 5-year window. Global figures scale U.S. data conservatively; actual global demand could be higher due to shortages (e.g., 3.4M cyber pros needed worldwide).
Market context: Cybersecurity spending projected to grow from ~$264–520B in 2026 to $472–1T by 2031 (12–20% CAGR), fueling jobs. Data/AI markets tie into broader tech growth, but with more volatility.
Job Losses Due to AI (2026-2031 Projections)
AI is a double-edged sword: It creates roles while automating others. Projections vary by source (WEF, IMF, Goldman Sachs), but here’s a synthesis focused on losses vs. net impact. I calculated nets by applying reported automation risks to base projections (e.g., 10–30% exposure leading to partial/full displacement).
- Cybersecurity Losses: Minimal direct losses (0–5% net reduction). AI augments roles (e.g., automation for threat detection reduces alert fatigue), but creates demand for AI-skilled pros to handle new threats like data poisoning or AI attacks. WEF notes cybersecurity as a growth area amid AI. Projected U.S. losses: ~1,000–5,000 (offset by growth); global: ~5,000–20,000. Trends: AI expands the market to $2T TAM, with roles shifting to “guiding AI” rather than competing. No “apocalypse”—stability high due to ongoing threats.
- Data Science Losses: Higher risk (10–25% net reduction in routine roles). AI automates data analysis, modeling, and entry-level tasks, with 50% of entry white-collar jobs potentially gone in 5 years. Young workers in high-AI exposure roles already down 13% since 2022. Projected U.S. losses: ~10,000–25,000 (partially offset); global: ~50,000–100,000. Broader: 85M global jobs lost by 2026 (WEF), many in data-heavy fields, but 69–97M created in AI/ML. Net global tech loss: 14M by 2027 (WEF), with data science exposed (two-thirds of jobs automatable to some degree).
- Related Fields: Cyber-related (e.g., IT ops): Low losses, as AI creates hybrid roles. Data-related (e.g., analysts): Higher, up to 30% automatable by 2030. Overall, AI “tsunami” fears up 40% in 2026, but no mass apocalypse yet—gains offset losses in high-skill areas.
Adjusted 2031 U.S. nets (factoring max losses): Cyber +20,000–24,000; Data +15,000–30,000. Data has higher upside but more volatility.
Best Financial Route
To pick the best financial path, I calculated 5-year cumulative earnings (2026-2031) using mid-entry salaries ($80k cyber, $100k data), a 4% base annual raise, plus field CAGRs for growth. Total: Cybersecurity ~$456,000; Data science ~$574,000. Adjusting data for 5% effective loss risk (from higher unemployment trends): ~$546,000.
Data science/AI wins financially, with 20–25% higher projected earnings even after risk adjustment, driven by higher starting pay and faster growth in AI/ML roles. However, if stability is prioritized (low unemployment in cyber ~0–1% vs. rising in data), cybersecurity is safer. Pursue data if you enjoy innovation; cyber for defensive, recession-proof work. Both benefit from AI skills—hybrid paths (e.g., AI in cyber) could yield even more.