SOC 33-9031

Gambling Surveillance Officers AI displacement risk

Gambling surveillance officers watch casino floors through camera systems to catch cheating and theft. AI video analytics now do the first pass — tracking cards, reading bets, flagging anomalies — turning the officer's job from watching screens into adjudicating what the system flags.

Exposure56

Share and intensity of work current AI systems can materially affect.

Automation32%

Likely potential for exposed tasks to move to software after workflow integration.

Risk bandModerate

This is a genuine role transformation, not elimination: surveillance AI catches more, which generates more cases for human review. Judgment about advantage play, employee collusion, and regulatory reporting stays with licensed officers who understand the games.

Distribution

Where Gambling Surveillance Officers sits across 620 tracked roles

Gambling Surveillance Officers · 44050100

Displacement pressure 44 — higher than 73% of the 620 occupations tracked on displacement.ai.

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

8 O*NET task statements matched to SOC 33-9031. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $43,370 (May 2025, US national). The latest BLS row matched SOC 33-9031.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

2030 economic stress test

How Anthropic's scenarios classify Gambling Surveillance Officers

SOC 33-9031 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 44/100 role score and are not an occupation forecast.

Modest change

+1.1% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +1.6% GDP and 3.9% unemployment.

Substantial change

+5.9% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +8.3% GDP and 4.6% unemployment.

Extreme change

+33.6% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +32.4% GDP and 11.9% unemployment.

Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.

Official task evidence

O*NET task matches for Gambling Surveillance Officers

The current evidence import matched 8 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.

Dataset31.0 (August 2026)
Matched tasks8
SOC33-9031
  • Core task / ID 4409

    Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures.

  • Core task / ID 4407

    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 task / ID 4408

    Report all violations and suspicious behaviors to supervisors, verbally or in writing.

  • Core task / ID 21145

    Develop and maintain log of surveillance observations.

  • Core task / ID 21146

    Inspect and monitor audio or video surveillance equipment to ensure it is working appropriately.

  • Core task / ID 21147

    Review video surveillance footage.

Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.

Task profile

Where AI changes the work

technical

Observe casino operations for cheating or theft

Exposure 44, automation 24%, augmentation 54%.

O*NET evidence: Observe casino or casino hotel operations for irregular activities, such as cheating or... (ID 4407)

information

Review video surveillance footage

Exposure 62, automation 38%, augmentation 60%.

O*NET evidence: Review video surveillance footage. (ID 21147)

compliance

Report violations and suspicious behavior

Exposure 48, automation 25%, augmentation 56%.

O*NET evidence: Report all violations and suspicious behaviors to supervisors, verbally or in writing. (ID 4408)

information

Maintain surveillance observation logs

Exposure 58, automation 33%, augmentation 58%.

O*NET evidence: Develop and maintain log of surveillance observations. (ID 21145)

TaskExposureAutomationAugmentation
Observe casino operations for cheating or theft4424%54%
Review video surveillance footage6238%60%
Report violations and suspicious behavior4825%56%
Maintain surveillance observation logs5833%58%

Transition pathways

Adjacent moves that preserve existing skills

role redesign

Surveillance Operations Manager

Training horizon: 6-12 months. Skill overlap 66. Wage preservation signal 122.

  • Own analytics platforms
  • Lead investigation reviews
  • Liaise with gaming regulators
Moderate
credentialed transition

Gaming Compliance Investigator

Training horizon: 12-24 months. Skill overlap 60. Wage preservation signal 118.

  • Master gaming regulations
  • Build prosecution-ready cases
  • Audit internal controls
Moderate

Comparison guides

Compare the next move before you commit

What the AI risk score means for Gambling Surveillance Officers

The displacement pressure score for Gambling Surveillance Officers is 44. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Review video surveillance footage carries 38% automation pressure, while Review video surveillance footage carries 60% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $43,370 (May 2025, US national). Employment context: The role where AI now watches the watchers. Typical education: High school plus gaming-commission licensing.

Wage vulnerability is 52, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

  • Moderate displacement pressure
  • AI runs the first watch pass
  • Adjudicating flags stays human

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Gambling Surveillance Officers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.

Priority 1

Surveillance systems

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 2

Gaming compliance

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 3

Fraud detection

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 4

Incident reporting

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

  1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
  2. By 60 days, complete one small project connected to Surveillance Operations Manager, such as own analytics platforms.
  3. By 90 days, compare internal openings and external postings for Surveillance Operations Manager or Gaming Compliance Investigator and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Gambling Surveillance Officers

Will AI replace Gambling Surveillance Officers?

Gambling surveillance officers watch casino floors through camera systems to catch cheating and theft. AI video analytics now do the first pass — tracking cards, reading bets, flagging anomalies — turning the officer's job from watching screens into adjudicating what the system flags. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.

Which parts of Gambling Surveillance Officers work are most exposed to AI?

Review video surveillance footage and Maintain surveillance observation logs show the strongest automation pressure in this model. Review video surveillance footage and Maintain surveillance observation logs are better treated as AI-augmented work.

What should Gambling Surveillance Officers learn next?

Start with Surveillance systems, Gaming compliance, Fraud detection. The most practical adjacent paths in this model are Surveillance Operations Manager and Gaming Compliance Investigator.

How should this score be used?

Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.

Sources

Evidence trail