Airport Security Technology: Advanced Screening Systems

CT Scanners: The Revolution in Carry-On Screening

Computed Tomography (CT) scanners represent the most significant advance in airport security screening since the introduction of X-ray machines in the 1970s. Unlike conventional 2D X-ray systems that produce flat images requiring trained operators to identify threats from a single projection angle, CT scanners rotate an X-ray source around the baggage conveyor to capture hundreds of images from different angles, reconstructing a full three-dimensional model of every item in a bag. Security officers can rotate the 3D image on a monitor, zoom in on specific items, and virtually remove overlapping objects to examine contents that would be obscured in a flat X-ray view.

The operational consequence of CT scanning is that passengers can leave laptops and liquids in carry-on bags during screening. Standard 2D X-ray systems cannot reliably identify threat materials when laptops and other dense electronics obscure the image, which is why TSA and international equivalents have required passengers to remove these items. CT systems produce images of sufficient resolution and dimensional detail to identify explosives, weapons, and other threat materials even when surrounded by other objects. The TSA began deploying CT scanners at U.S. checkpoints in 2017 and accelerated rollout through 2022, targeting coverage at all Category X airports (the largest airports by passenger volume).

Smiths Detection's HI-SCAN 10080 XCT and L3Harris Technologies' ProVision ATD are the two primary CT systems approved for checkpoint deployment in the United States. In Europe, Analogic Corporation (now part of Examion) and Vanderlande have supplied CT systems to airports operating under European Civil Aviation Conference (ECAC) standards. The machines cost approximately $300,000–$500,000 each — roughly five times the cost of conventional X-ray units — but the throughput improvement (eliminating the 20–30 second delay caused by laptop removal) reduces checkpoint staffing requirements enough to offset much of the capital cost differential over a 7–10 year deployment lifecycle.

Automated Threat Detection (ATD) software integrated with CT scanners applies machine learning algorithms to the 3D image data to automatically flag potential threats, reducing the cognitive load on human operators. The algorithms are trained on databases of threat items — explosive precursors, improvised explosive device (IED) components, prohibited weapons — and generate alerts when detected characteristics match known threat signatures. Human operators remain the final decision-makers, but ATD reduces false alarm rates compared to operator-only detection and maintains consistent threat detection standards regardless of operator fatigue or experience level.

Advanced Imaging Technology and Millimeter Wave Screening

Passenger body screening technology has undergone parallel advancement. Millimeter wave scanners — deployed extensively since 2010 as replacements for backscatter X-ray units (which were retired due to privacy concerns about detailed body imagery) — transmit non-ionizing radio frequency energy at 24–30 GHz and detect the reflected signals to construct a generic mannequin-style image of the passenger. Threat items are identified as anomalies on the mannequin image rather than as detailed body images, addressing the privacy concerns that ended backscatter deployment.

L3Harris Technologies' ProVision 2 and Rohde & Schwarz QPS201 are the primary millimeter wave systems in service at major airports. Both use active imaging — emitting low-power RF signals — rather than passive imaging (detecting naturally emitted body heat), allowing screening in the 3–4 seconds it takes a passenger to stand in the scanner booth with arms raised. Automated Target Detection (ATD) software on these systems generates a generic outline image with threat anomalies marked, reducing operator involvement to examining flagged areas rather than reviewing full body scans.

Explosive Trace Detection (ETD) remains a critical layer in the security screening stack. ETD machines — either swab-based analysis units or portal systems that sample air around a passenger — can detect trace quantities of explosive compounds at parts-per-billion concentrations. Swab testing involves wiping a sampling swab across a bag or passenger's hands and inserting it into the analyzer, which uses ion mobility spectrometry (IMS) to identify chemical signatures. ETD is applied selectively at security checkpoints and universally at many international departure gates as an additional screening layer for passengers who have already cleared the checkpoint.

The Credential Authentication Technology (CAT) units deployed by TSA at U.S. checkpoints combine document validation with optional biometric identity verification. CAT-2 units use ultraviolet, infrared, and visible light imaging to examine travel documents, checking for physical security features including holograms, microprinting, and fluorescent inks that forgeries typically cannot replicate. The units interface with TSA's Secure Flight database to verify that the person presenting the document matches the person on the flight reservation, flagging discrepancies that require secondary screening.

AI and Machine Learning in Threat Detection

Machine learning has transformed threat detection from a rule-based process (flag bags that contain objects matching known threat templates) to a probabilistic process (assign risk scores to bags based on learned patterns across millions of scanned images). The distinction matters operationally: rule-based systems generate high false alarm rates as new bag configurations and benign objects trigger pattern matches, while ML-trained systems learn which image features are predictive of actual threats versus common false alarms, reducing unnecessary manual inspection.

Analogic Corporation's Cobra CT system and Smiths Detection's eqo platform both integrate deep learning-based ATD that has been trained on classified databases maintained by the TSA and equivalent European agencies. These databases contain images of real threat items discovered at checkpoints and in investigative operations, supplemented by computer-generated synthetic images of novel threat configurations. Training on synthetic data is particularly important for preparing algorithms for threats that have not yet been encountered operationally but can be anticipated based on intelligence assessments.

The TSA's Artimis (Automated Real-Time Identity Security) program applies AI to checkpoint data beyond baggage — integrating behavioral indicators, travel pattern analysis, and document verification results to generate passenger risk scores. High-risk scores can trigger enhanced screening procedures, while low-risk scores accelerate trusted traveler processing. Similar risk-based screening approaches are used by the Israeli Airport Authority (which pioneered behavioral profiling at Ben Gurion Airport) and the UK's Border Force.

Computer vision applied to checkpoint camera feeds can identify queue lengths, estimate wait times, and detect behavioral anomalies — a passenger moving against pedestrian flow, unattended baggage, or unusual loitering patterns. These systems feed operational dashboards that allow checkpoint supervisors to redeploy staff before queues reach critical lengths rather than reacting to visible congestion. Denver International Airport and Frankfurt Airport have deployed AI queue management systems that provide real-time predictions of security wait times accurate to within 5 minutes, information they share with passengers through airport apps and dynamic signage.

Trusted Traveler Programs and Risk-Based Screening

TSA PreCheck, Global Entry, CLEAR, and equivalent international programs represent the risk-based segmentation approach to airport security: invest more heavily in pre-screening trusted travelers to create separate, faster processing lanes that reduce overall checkpoint congestion without reducing security effectiveness for the screened population. TSA PreCheck, launched in 2011 and expanded to all major U.S. airports by 2014, allows enrolled passengers to use dedicated lanes where shoes, belts, and light outerwear remain on and laptops and liquids stay in bags — a 3–4 minute screening process compared to 8–12 minutes in standard lanes.

CLEAR, the biometric identity verification company operating at over 50 U.S. airports, uses iris and fingerprint scanning to verify identity at a dedicated CLEAR pod, allowing enrolled members to skip the ID verification queue and proceed directly to the X-ray belt. CLEAR does not bypass X-ray screening — it accelerates only the identity verification stage. The distinction matters: CLEAR's $189 annual fee purchases faster identity verification, not reduced screening rigor. Airlines including Delta, United, and Alaska have integrated CLEAR enrollment into their loyalty programs as a premium benefit.

The Netherlands Privium program, operated at Amsterdam Schiphol, uses iris recognition to allow enrolled frequent travelers to clear border control and security through dedicated automated lanes. The UK's e-Passport Gates use facial recognition from e-passport chips to automate border control, with eligible nationalities (EU, UK, US, Canadian, and others) cleared in approximately 20 seconds without officer involvement. Australia's SmartGate operates on the same principle with similar eligibility.

Intelligence-led security — directing enhanced screening resources toward specific travelers identified through behavioral, travel pattern, or intelligence indicators rather than selecting randomly or profiling by demographic characteristics — is the dominant philosophy in modern airport security design. Pre-screening programs create a population of low-risk travelers that can be processed efficiently, freeing security resources to concentrate on travelers who exhibit risk indicators. The effectiveness of this model depends on the quality of intelligence feeding the risk assessment algorithms and the legal frameworks governing how traveler data can be collected, retained, and analyzed.

Checkpoint of the Future: Integration and Seamless Flow

The TSA's Innovation Task Force has defined a roadmap for the "checkpoint of the future" that integrates CT screening, biometric identity verification, behavioral analytics, and AI-assisted threat detection into a continuous screening flow that eliminates the stop-and-divest process of current checkpoints. In this vision, passengers walk through a screening corridor at normal walking pace — removing nothing, stopping nowhere — while an array of sensors captures CT images of their bags, millimeter wave images of their bodies, biometric identity data, and behavioral indicators simultaneously. A risk assessment engine combines these data streams to assign a clearance decision within the corridor passage time.

Partial implementations of this vision are operational. The TSA's Innovation Checkpoint at Las Vegas Harry Reid International Airport incorporates multiple advanced screening technologies in a reconfigured physical layout, testing new equipment configurations and workflow designs. Schiphol Airport operates Project Checkpoint, a research facility where full seamless screening concepts are evaluated with real passengers before commercial deployment. The research consistently shows that corridor-based scanning without divesting reduces per-passenger processing time by 30–50% compared to current checkpoints, but requires passengers to wear only clothing (no bulky winter coats that could conceal threats) — a constraint that requires climate-controlled checkpoints.

International harmonization of security standards is a prerequisite for seamless passenger flow across borders. When a passenger cleared through an advanced checkpoint at one airport connects to an international flight, destination country standards may require re-screening using equipment they have already passed through. The ICAO Aviation Security Panel is working on mutual recognition frameworks that would allow screening results to transfer across borders, eliminating duplicative security at connection airports — a goal that would significantly reduce friction on international itineraries but requires deep trust between national security agencies.