Will AI replace Transportation Security Screeners?

Not as a job, but it is already doing parts of the work. Across the 26 official task statements scored for Transportation Security Screeners (United States, SOC 33-9093), 4% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 16 out of 100 (range 12–22, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Transportation Security Screeners” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record information about any baggage that sets off alarms in monitoring equipment” (61/100, high); “Inspect carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation” (42/100, partial); “Notify supervisors or other appropriate personnel when security breaches occur” (42/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Transportation Security Screeners” stay human?

About 87% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Patrol work areas to detect any suspicious items” (0/100, minimal); “Follow those who breach security until police or other security personnel arrive to apprehend them” (0/100, minimal); “Send checked baggage through automated screening machines, and set bags aside for searching or rescreening as indicated by equipment” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Transportation Security Screeners” do about AI?

Start from the ledger rather than the headline: 4% of this job's weighted core work is exposed, and roughly 87% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Transportation Security Screeners calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 26 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Transportation Security Screeners

inspecting carry-on items, deciding whether baggage that triggers alarms and locating suspicious bags pictured in printouts sent from remote monitoring areas. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: searching carry-on or checked baggage by hand when it is suspected to contain prohibited items is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 4%
changing shape: 9%
staying human: 87%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 16 out of 100 (12–22 allowing for uncertainty): minimal exposure, across 26 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for transportation security screeners is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is recording information about any baggage that sets off alarms in monitoring equipment: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Conduct screening of passengers, baggage, or cargo to ensure compliance with Transportation Security Administration (TSA) regulations. May operate basic security equipment such as x-ray machines and hand wands at screening checkpoints. The job title says “transportation security screeners”. The real job is the part underneath: searching carry-on or checked baggage by hand when it is suspected to contain prohibited items. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But transportation security screeners is not one task. It is 26 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is searching carry-on or checked baggage by hand when it is suspected to contain prohibited items, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 4%
changing shape: 9%
staying human: 87%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Recording information about any baggage that sets off alarms in monitoring equipment
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record information about any baggage that sets off alarms in monitoring equipment.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (57–65 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Logging which bags set off alarms is routine record-keeping the screening equipment can largely produce itself.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Notifying supervisors or other appropriate personnel when security breaches occur
The software now makes the first pass at supervisors, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Notify supervisors or other appropriate personnel when security breaches occur.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Passing word up the chain when something goes wrong is a straightforward alert that systems already send.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Inspecting carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation
The software now makes the first pass at carry-on items, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Inspect carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Reading bag X-ray images is pattern spotting that automated detection already does, though a certified officer makes the call.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

23 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Testing baggage for any explosive materials
This work happens in the physical world: baggage, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Test baggage for any explosive materials, using equipment such as explosive detection machines or chemical swab systems.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Swabbing a bag and feeding the sample into a detector requires hands at the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Searching carry-on or checked baggage by hand when it is suspected to contain prohibited items
This work happens in the physical world: carry-, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Search carry-on or checked baggage by hand when it is suspected to contain prohibited items such as weapons.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Opening a bag and searching it by hand is physical work at the checkpoint.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Checking passengers' tickets to ensure that they are valid
This work happens in the physical world: passengers' tickets, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check passengers' tickets to ensure that they are valid, and to determine whether passengers have designations that require special handling, such as providing photo identification.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Document checks are already partly automated, but matching the ID to the person standing there needs someone present.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Performing pat-down or hand-held wand searches of passengers who have triggered machine alarms
This work happens in the physical world: pat-down, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Perform pat-down or hand-held wand searches of passengers who have triggered machine alarms, who are unable to pass through metal detectors, or who have been randomly identified for such searches.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: A pat-down is physical contact with a passenger and cannot happen any other way.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Contacting leads or supervisors to discuss objects of concern that are not on prohibited object lists
This work happens in the physical world: leads, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Contact leads or supervisors to discuss objects of concern that are not on prohibited object lists.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Escalating an odd item is a quick conversation with a supervisor who can see the object.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Inspecting checked baggage for signs of tampering
This work happens in the physical world: checked baggage, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect checked baggage for signs of tampering.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Spotting tampering means handling and looking closely at the actual bag.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Sending checked baggage through automated screening machines
This work happens in the physical world: checked baggage, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Send checked baggage through automated screening machines, and set bags aside for searching or rescreening as indicated by equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Putting bags through the machine and pulling them aside is physical handling of luggage.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Show the other 16 tasks

Viewing images of checked bags and cargo
staying human
The rules require a named, qualified person to answer for images of checked bags, and that person cannot be a piece of software.
importance 5 · Supplemental
Source: “View images of checked bags and cargo, using remote screening equipment, and alert baggage screeners or handlers to any possible problems.” (O*NET task statement)
How this row was scored
Exposure score: 36 out of 100 (29–43 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Screening bag images from a remote room is already a screen-based task that automated detection assists.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Directing passengers to areas where they can pick up their baggage after screening is complete
staying human
This work happens in the physical world: passengers, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Direct passengers to areas where they can pick up their baggage after screening is complete.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Pointing passengers toward baggage claim is simple wayfinding information given in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Providing directions and responding to passenger inquiries
staying human
This work happens in the physical world: directions, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Provide directions and respond to passenger inquiries.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Answering passenger questions is well-trodden information, though people ask the officer standing in front of them.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Decide whether baggage that triggers alarms should be searched or should be allowed to pass
staying human
The rules require a named, qualified person to answer for whether baggage, and that person cannot be a piece of software.
importance 5 · Core
Source: “Decide whether baggage that triggers alarms should be searched or should be allowed to pass through.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: someone qualified has to answer for it.
The rating behind it: The machine can suggest, but a certified officer carries the judgment on whether a flagged bag gets searched.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Monitoring passenger flow through screening checkpoints to ensure order and efficiency
staying human
This work happens in the physical world: passenger flow, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor passenger flow through screening checkpoints to ensure order and efficiency.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Camera analytics already measure queue flow, though keeping order at the lanes needs staff present.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Watching for potentially dangerous persons whose pictures are posted at checkpoints
staying human
This work happens in the physical world: potentially dangerous persons whose pictures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Watch for potentially dangerous persons whose pictures are posted at checkpoints.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Face matching against posted photographs is something recognition software does well, but the watching happens at the checkpoint.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Informing other screeners when baggage should not be opened because it might contain explosives
staying human
This work happens in the physical world: other screeners, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inform other screeners when baggage should not be opened because it might contain explosives.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Warning colleagues not to open a bag is a quick spoken alert given right there at the checkpoint.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Asking passengers to remove shoes and divest themselves of metal objects prior to walking through metal detectors
staying human
This work happens in the physical world: passengers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Ask passengers to remove shoes and divest themselves of metal objects prior to walking through metal detectors.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Recorded announcements exist, but getting a queue of people to actually divest takes someone standing there directing them.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Informing passengers of how to mail prohibited items
staying human
This work happens in the physical world: passengers of how, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inform passengers of how to mail prohibited items to themselves, or confiscate these items.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Explaining the mailing option is simple information, but taking the item off the passenger happens face to face.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Closing entry areas following security breaches or reopen areas after receiving notification that the airport is secure
staying human
This work happens in the physical world: entry areas following security breaches, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Close entry areas following security breaches or reopen areas after receiving notification that the airport is secure.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Closing or reopening a checkpoint area means physically controlling the space and the people in it.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Contacting police directly in cases of urgent security issues
staying human
This work happens in the physical world: police, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Contact police directly in cases of urgent security issues, using phones or two-way radios.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (0–15 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: The radio call itself is simple, but spotting the problem depends on being on the ramp.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Challenging suspicious people, requesting their badges and asking what their business is in a particular areas
staying human
This work happens in the physical world: suspicious people, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Challenge suspicious people, requesting their badges and asking what their business is in a particular areas.” (O*NET task statement)
How this row was scored
Exposure score: 1 out of 100 (0–8 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Stopping someone in a restricted area and asking their business needs a person physically standing in the way.
The five ratings: output a model can produce 0/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 1/4.
Confiscating dangerous items and hazardous materials found in opened bags and turn them over to airlines
staying human
This work happens in the physical world: dangerous items, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Confiscate dangerous items and hazardous materials found in opened bags and turn them over to airlines for disposal.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Taking a prohibited item out of a bag and handing it over is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Locating suspicious bags pictured in printouts sent from remote monitoring areas
staying human
This work happens in the physical world: suspicious bags, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Locate suspicious bags pictured in printouts sent from remote monitoring areas, and set these bags aside for inspection.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Finding the flagged bag on the belt and pulling it aside is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Following those who breach security until police or other security personnel arrive to apprehend them
staying human
This work happens in the physical world: those who breach security until police, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Follow those who breach security until police or other security personnel arrive to apprehend them.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Following someone through the terminal until police arrive is physical pursuit.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Patrolling work areas to detect any suspicious items
staying human
This work happens in the physical world: work areas, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Patrol work areas to detect any suspicious items.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Spotting suspicious items means physically walking the area and looking.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $66,770a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 50,290in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: information in, a record out. The rows above are exactly that shape: recording information about any baggage that sets off alarms in monitoring equipment and notifying supervisors or other appropriate personnel when security breaches occur. What it cannot do is be answerable: carry- needs a named person the rules will accept, and software cannot be that person. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: searching carry-on or checked baggage by hand when it is suspected to contain prohibited items is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 4% of this job's task weight sits in rows the software is already learning, 9% in rows that change shape rather than disappear, and 87% in rows it is nowhere near. That is the position, measured across 26 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Recording information about any baggage that sets off alarms in monitoring equipment is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to information, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether carry- is in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near notifying supervisors or other appropriate personnel when security breaches occur. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to transportation security screeners (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was first-line supervisors of security workers: only about 2% of its durable work is work you already do and it pays 16.2% less. And on the numbers you do not need one. This job scores 16/100 here, with only 4% of the task list in the top band, and “test baggage for any explosive materials, using equipment” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

First-Line Supervisors of Security Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect checked baggage for signs of tampering, and their equivalent is to inspect and adjust security equipment to ensure it is operational or to detect…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $55,940 against your $66,770, 16.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Reservation and Transportation Ticket Agents and Travel Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct passengers to areas where they can pick up their baggage after screening…, and their equivalent is to check baggage and cargo and direct passengers to designated locations for loading. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $44,390 against your $66,770, 33.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Facilities Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already notify supervisors or other appropriate personnel when security breaches occur, and their equivalent is to identify, investigate, or resolve security breaches. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 4% of its task weight, across 26 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like searching carry-on or checked baggage by hand when it is suspected to contain prohibited items arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Security guards and related occupations is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Security guards and related occupations. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for transportation security screeners launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for transportation security screeners yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for transportation security screeners existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for transportation security screeners launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Transportation Security Screeners?: Not as a job, but it is already doing parts of the work. Across the 26 official task statements scored for Transportation Security Screeners (United States, SOC 33-9093), 4% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 16 out of 100 (range 12–22, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Transportation Security Screeners” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record information about any baggage that sets off alarms in monitoring equipment” (61/100, high); “Inspect carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation” (42/100, partial); “Notify supervisors or other appropriate personnel when security breaches occur” (42/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Transportation Security Screeners” stay human?: About 87% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Patrol work areas to detect any suspicious items” (0/100, minimal); “Follow those who breach security until police or other security personnel arrive to apprehend them” (0/100, minimal); “Send checked baggage through automated screening machines, and set bags aside for searching or rescreening as indicated by equipment” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Transportation Security Screeners” do about AI?: Start from the ledger rather than the headline: 4% of this job's weighted core work is exposed, and roughly 87% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Transportation Security Screeners calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 26 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

How we score a job Download this release Look up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.