Security Guards

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Security Guards (ISCO-08 5414) score an average of 0.20 on a 0–1 exposure scale — more exposed than about 34% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Controlling access to establishments, monitoring and authorizing the entrance or departure of employees and visitors, checking identification and issuing security passes;”

Scores 0.36 on the 2025 scale. The task "Controlling access to establishments, monitoring and authorizing the entrance or departure of employees and visitors, checking identification and issuing security passes" involves verifying identities, managing entry points, and maintaining security measures. Semantically similar tasks, such as "Controlling the identity of people entering the area supervised by the pass office" (score 0.375) and "Issuing passes" (score 0.485), suggest moderate automation potential. Generative AI can assist in document verification and automate some data processing aspects through image recognition and database queries, aligning with modern security practices. However, the necessity for human judgment in handling unique circumstances, assessing potential threats, and interpersonal interactions limits full automation. Given the high accessibility of digital tools in Poland, AI's role can be significant but not comprehensive, leading to the adjusted score. This score acknowledges both the digital capabilities and the irreplaceable need for human oversight in security-sensitive operations.

Moving fastest, 2023 → 2025

“Performing security checks of passengers and luggage at airports;”

Model capability on this task changed by +0.07 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 5414, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Security Guards
• Transportation Security Screeners
• First-Line Supervisors of Protective Service Workers, All Other
• Gambling Surveillance Officers and Gambling Investigators

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Security Guards sit at the 34th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Security Guards rank in the 34th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Controlling access to establishments, monitoring and authorizing the entrance or departure of employees and visitors, checking identification and issuing security passes;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Security Guards". https://singulariki.com/gradient/5414-security-guards.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.