EXPOSED
This title covers supervisors of security officers, lifeguards, crossing guards, animal control and gaming surveillance staff — people who post, brief and correct a physical crew across shifts, then respond in person when something goes wrong. The paperwork half of the job (shift rosters, post orders, incident report drafting, camera log review, training compliance tracking) is squarely in AI's wheelhouse and is already being absorbed by scheduling and video-analytics platforms. What survives is the part that requires a named human standing at the scene: deciding whether to escalate, restrain, evacuate or call police, and owning that call afterward in an investigation or lawsuit.
Most of this decline happened after 2021 — it is not the pandemic dip.
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing.
BLS projection, 2024–2034
+1.6% 21,500 → 21,800 on the projections basis
Growing, and only partly exposed
The BLS expects +1.6% more of these jobs by 2034, and at 59/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~2,100 openings a year on average, including replacing people who leave.
Guard ChiefGuard SupervisorSecurity ManagerCaptain of GuardsSecurity DirectorMuseum Security ChiefHead of Loss PreventionLoss Prevention OfficerAnimal Control SupervisorInternal Security ManagerSecurity Guard SupervisorPlant Protection SupervisorStore Loss Prevention ManagerDog License Officer SupervisorAnimal Cruelty Investigation SupervisorTransportation Security Administration Screener SupervisorTransportation Security Administration (TSA) Screener Supervisor
The BLS uses First-Line Supervisors of Protective Service Workers, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half the shift is rosters, post-order updates, incident-report drafting and camera-log review that scheduling and video-analytics platforms already generate, but the other half — walking the posts, briefing a relief crew face-to-face, correcting an officer who is freezing on a hostile subject, and being physically present at the incident — has no software substitute, which puts it mid-band rather than at 6 or at 16.
Hands-on in uncontrolled environments You are on your feet in parking structures, pool decks, casino floors, school crossings and animal-control calls where the environment is uncontrolled and the variable is a person or an animal behaving unpredictably; it stops short of 18-20 only because a meaningful share of the shift is spent in a monitoring station or office writing up what happened.
Certification preferred, not legally required Most states require only a guard-card or unarmed/armed security registration and a few hours of training — no professional licence that names you personally, so the contract security firm or the property owner absorbs the negligent-security suit while your exposure is termination and a revoked card, hence 6 rather than the 11+ a licensed peace officer carries.
Exists to be accountable for ambiguous calls The call to restrain, evacuate, pursue, use force or hand off to police is made in seconds with incomplete information and no supervisor above you on site, and you will defend that decision in an internal investigation, a police report and possibly a deposition; it lands at 15 rather than 18 because post orders and client protocols pre-define a large portion of your responses.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (12/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (6/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (15/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 32 of this occupation's 59 points (54%).
Embodiment (15/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 72/100 — SAFE.
Task-mix shift plus doctrine: as video analytics generate far more alerts than a crew can act on, the supervisor's day becomes triage of ambiguous flags and use-of-force/evacuation calls. If agency policy or a court ruling (following the deliberate-indifference and negligent-security lines) fixes the on-scene supervisor as the accountable decision-maker for acting on an algorithmic alert, and body-cam/after-action review formally names that supervisor, the ownership of consequential ambiguous calls deepens.
Genuine two-tier job: once rostering, post-order generation, camera log review and training-compliance tracking are absorbed by platforms (Trackforce, Silvertrac, Ambient/Verkada analytics), the residual day is briefing and correcting humans, de-escalation coaching, and scene command — which current systems cannot do at usable quality. Score rises arithmetically as the routine tier leaves, though headcount may fall at the same time.
State private-security licensing boards (e.g., California BSIS, Texas DPS Private Security Bureau, New York DOS) tightening rules so each guard post/site must have a named, individually licensed 'qualifying supervisor' who signs use-of-force reviews and incident reports, with personal license exposure — plus contract-guard clients or insurers requiring that a licensed supervisor countersign any AI-generated incident narrative before it enters the record. Also live in motion: state bills requiring human review of automated video-surveillance flags before an intervention.
Little upward route from capability; a modest rise if drone/robot patrol adoption shifts supervisors toward being the mandated on-foot responder for every robot-flagged event in unstructured space (crowds, water, aggressive animals), as some Knightscope and lifeguard-drone deployments already require a human to close out each alert physically.
The limit. Trust premium has no credible route: contract-security and crossing-guard buyers are procuring on price and coverage, not on the identity of the supervisor, and no client specifically pays extra for a human doing rosters. Gains here are almost entirely liability-driven, and liability rules can also be written the other way — a compliance-certified analytics platform accepted as the system of record would push liability_shield down instead.
| New York-Newark-Jersey City, NY-NJ | 2,010 | $86,860 +14% |
| Los Angeles-Long Beach-Anaheim, CA | 810 | $94,810 +24% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 770 | $57,950 -24% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 550 | $81,370 +7% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 450 | $59,580 -22% |
| Dallas-Fort Worth-Arlington, TX | 410 | $83,050 +9% |
| Denver-Aurora-Centennial, CO | 400 | $75,300 -1% |
| Las Vegas-Henderson-North Las Vegas, NV | 390 | $77,310 +1% |
| San Diego-Chula Vista-Carlsbad, CA | 370 | $103,240 +35% |
| San Francisco-Oakland-Fremont, CA | 270 | $98,590 +29% |
| Salem, OR | 40 | $97,500 +28% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 59. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.