Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Gambling Surveillance Officers and Gambling Investigators
Occupation · SOC 33-9031.00
Observe gambling operation for irregular activities such as cheating or theft by either employees or patrons. Investigate potential threats to gambling assets such as money, chips, and gambling equipment. Act as oversight and security agent for management and customers.
Also called: Surveillance Agent · Surveillance Observer · Surveillance Officer · Surveillance Operator · Casino Enforcement Agent · Gaming Investigator · Security Officer · Surveillance Investigator · Surveillance Monitor · Surveillance Technician · Armed Security Officer · Casino Investigator
Job family: Protective Service Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-33-9031-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+0.3% by 2034)
- 1,300 openings/yr
- Moderate AI exposure
- Median pay $43,900/yr
- Can largely be done remotely (teleworkable)
- Both the 2013 computerization estimate and today's AI exposure rank this high
55th-percentile task overlap — yet about 1,300 openings a year (+0.3% projected, BLS) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 65th | 0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 51st | 0.6 | |
| AI assistant applicability (Microsoft) Moderate | 53rd | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.4), and including AI-powered software (γ 0.6). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.9 · 89th percentile among occupations · High
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +0.3% by 2034 |
| Projected annual openings | 1,300 |
| Employment 2024 → 2034 | 10,300 → 10,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Gambling Surveillance Officers and Gambling Investigators sits at the 60th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
32% mean task exposure (2025)
60th percentile of 427 placed occupations
+1 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Legal and Related Associate Professionals · 3411 | 39% | Gradient 1 |
| Security Guards · 5414 | 20% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Tasks
All 8 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures. Core · importance 4.8
- Observe casino or casino hotel operations for irregular activities, such as cheating or theft by employees or patrons, using audio and video equipment and one-way mirrors. Core · importance 4.8
- Report all violations and suspicious behaviors to supervisors, verbally or in writing. Core · importance 4.8
- Develop and maintain log of surveillance observations. Core · importance 4.7
- Inspect and monitor audio or video surveillance equipment to ensure it is working appropriately. Core · importance 4.7
- Review video surveillance footage. Core · importance 4.6
- Act as oversight or security agents for management or customers. Core · importance 4.4
- Supervise or train surveillance observers. Core · importance 4.1
Work activities
- Compile operational data.
- Compile data or documentation.
- Record operational or environmental data.
- Inspect facilities or equipment to ensure specifications are met.
- Inspect equipment or systems.
- Inspect materials or equipment to determine need for repair or replacement.
- Observe individuals' activities to gather information or compile evidence.
- Operate surveillance equipment to detect suspicious or illegal activities.
- Discuss performance, complaints, or violations with supervisors.
- Monitor operations to ensure compliance with safety or security policies or regulations.
- Maintain surveillance of individuals or establishments.
- Train employees in proper work procedures.
- Direct security operations.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
Abilities
| Problem Sensitivity | 4.3 | |
| Selective Attention | 4.0 | |
| Far Vision | 4.0 | |
| Oral Comprehension | 3.9 | |
| Written Expression | 3.8 | |
| Inductive Reasoning | 3.8 | |
| Near Vision | 3.8 | |
| Oral Expression | 3.6 | |
| Flexibility of Closure | 3.6 | |
| Deductive Reasoning | 3.5 | |
| Perceptual Speed | 3.4 | |
| Speech Recognition | 3.3 | |
| Speech Clarity | 3.3 | |
| Written Comprehension | 3.1 | |
| Information Ordering | 3.1 | |
| Speed of Closure | 3.1 | |
| Time Sharing | 3.1 | |
| Category Flexibility | 2.9 |
Essential skills
| Monitoring | 4.1 | |
| Critical Thinking | 3.8 | |
| Speaking | 3.6 | |
| Reading Comprehension | 3.3 | |
| Active Listening | 3.3 | |
| Writing | 3.1 | |
| Active Learning | 3.1 |
Transferable skills
| Judgment and Decision Making | 3.4 | |
| Social Perceptiveness | 3.1 | |
| Instructing | 3.0 | |
| Complex Problem Solving | 3.0 | |
| Coordination | 2.9 | |
| Time Management | 2.9 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Office software | Office suite software | Hot technology In demand |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| FileMaker Pro | Data base user interface and query software | |
| iView Systems | Data base user interface and query software | |
| Microsoft Paint | Graphics or photo imaging software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
What to study: Culinary, Entertainment, and Personal Services , Homeland Security, Law Enforcement, Firefighting and Related Protective Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 84.1% | |
| Post-Secondary Certificate | 5.9% | |
| Associate's Degree (or other 2-year degree) | 5.1% | |
| Some College Courses | 2.7% | |
| Less than a High School Diploma | 2.2% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Conventional | 5.6 | |
| Enterprising | 4.8 | |
| Realistic | 4.1 | |
| Investigative | 2.2 | |
| Social | 2.1 |
Work styles
| Dependability | 5.0 | |
| Attention to Detail | 4.0 | |
| Integrity | 3.0 | |
| Cautiousness | 2.5 | |
| Self-Control | 2.2 | |
| Stress Tolerance | 2.0 |
Interest areas
| Protective Service | 4.6 | |
| Law | 2.8 | |
| Management/Administration | 2.6 | |
| Information Technology | 2.0 | |
| Office Work | 1.8 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $34,020 |
| 25th percentile | $37,410 |
| Median (50th) | $43,900 |
| 75th percentile | $50,610 |
| 90th percentile | $62,360 |
| People employed | 10,000 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 4,850 | $40,310 |
| Accommodation and Food Services · Sector | 3,230 | $44,690 |
| Casino Hotels · National industry | 3,230 | $44,680 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 260 | $48,120 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Casino Hotels · National industry | 147.78× | 3,230 |
| Arts, Entertainment, and Recreation · Sector | 28.3× | 4,850 |
| Accommodation and Food Services · Sector | 3.5× | 3,230 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.44× | 260 |
Related occupations
Part of the Public Service & Safety career cluster.
- Security Management Specialists · 13-1199.07 Compare →
- Security Managers · 11-3013.01 Compare →
- Digital Forensics Analysts · 15-1299.06 Compare →
- First-Line Supervisors of Security Workers · 33-1091.00 Compare →
- Private Detectives and Investigators · 33-9021.00 Compare →
- First-Line Supervisors of Gambling Services Workers · 39-1013.00 Compare →
- Gambling Managers · 11-9071.00 Compare →
- Retail Loss Prevention Specialists · 33-9099.02 Compare →
- Detectives and Criminal Investigators · 33-3021.00 Compare →
- Camera Operators, Television, Video, and Film · 27-4031.00 Compare →
- Information Security Analysts · 15-1212.00 Compare →
- Information Security Engineers · 15-1299.05 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Gambling Surveillance Officers and Gambling Investigators — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
- Culinary, Entertainment, and Personal Services · field of study
- Homeland Security, Law Enforcement, Firefighting and Related Protective Services · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 60th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Gambling Surveillance Officers and Gambling Investigators show 55th-percentile AI task overlap — and about 1,300 annual U.S. openings
- Gambling Surveillance Officers and Gambling Investigators rank in the 55th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 1,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be about average (+0.3%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $43,900, across about 10,000 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Gambling Surveillance Officers and Gambling Investigators show 55th-percentile AI task overlap — and about 1,300 annual U.S. openings • Gambling Surveillance Officers and Gambling Investigators rank in the 55th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 1,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+0.3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $43,900, across about 10,000 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Gambling Surveillance Officers and Gambling Investigators". https://singulariki.com/roles/role-33-9031-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Gambling Surveillance Officers and Gambling Investigators." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-33-9031-00
APA
Singulariki. (2026). Gambling Surveillance Officers and Gambling Investigators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-33-9031-00
BibTeX
@misc{singulariki-role-33-9031-00,
title = {Gambling Surveillance Officers and Gambling Investigators},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-33-9031-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.