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AI is being used to flag possible threats, inspect infrastructure, monitor deployed software, and support emergency decisions—but these systems range from operating services to pilots and announcements. A September 28, 2026 snapshot of AI Weekly’s catalogue lists 27 deployments across eight sectors; the number is a dated catalogue count, not a definitive census or proof that the systems are effective.
What the 27-example count includes
AI Weekly’s safety-and-monitoring catalogue groups examples by sector and records status and reported outcomes. Its September 28, 2026 snapshot lists 27 deployments: 18 in production or with results, 13 with a reported outcome, and two halted or reversed. Those are catalogue classifications, not independent evaluations. An earlier rendering of the page showed 21 examples, so the total should always be read with its snapshot date.
The catalogue spans public-space surveillance, AI software safeguards, battlefield sensing, vehicle assistance, air-traffic trials, industrial robots, conservation, and research. These use cases have different goals and consequences: an alert from a public camera is not comparable to a software safeguard or an inspection model in a nuclear setting.
Where the catalogue places the deployments
The sector breakdown below is the catalogue’s classification in its September 28, 2026 snapshot. It is not a definitive taxonomy of AI safety work.
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| Sector | Examples in snapshot | Kinds of use described |
|---|---|---|
| Government and public sector | 9 | Police cameras and facial recognition, license-plate readers, government surveillance, and flood warnings |
| Software and technology | 7 | Age assurance, tool-call checks, safety classifiers, and other runtime or deployment safeguards |
| Military | 5 | Surveillance-drone procurement and battlefield data or sensing pilots |
| Automotive | 2 | Aftermarket driver-assistance devices and a vehicle-related pilot |
| Transportation | 1 | AI contrail forecasts used in flight-altitude decisions |
| Manufacturing | 1 | Robot safety technology |
| Nonprofit | 1 | Wildlife and conservation monitoring |
| Science and research | 1 | Satellite-image analysis related to animal behavior |
Public safety: cameras, recognition, and emergency warnings
The government and public-sector entries illustrate why “monitoring” does not necessarily mean preventing harm. Some systems collect or classify information for a human-led investigation; others issue warnings or support decisions. The catalogue is a secondary compilation, so deployment details and figures below are reports attributed to that catalogue rather than independently verified findings.
Police and roadway monitoring
Examples include Berlin police cameras described as flagging loitering, fighting, or abandoned packages; a Florida Highway Patrol investigation in which a Flock license-plate-reader match reportedly led to a wrongful arrest; and Flock readers in Troy, New York. Western Australia Police’s mobile facial-recognition trial reportedly scanned more than 131,000 faces, generated 33 alerts, and contributed to 19 arrests. Those figures describe reported system activity and outcomes; they do not establish the system’s accuracy, that the alerts caused the arrests, or that it reduced crime.
The catalogue also reports that Alpharetta roadway cameras produced 1.6 million images and photographed 50,200 vehicles over 21 days. Those are surveillance-volume figures, not evidence of improved road safety. Other public-sector examples include Chinese police smart glasses and AI-supported flash-flood warnings in South Africa.
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Government surveillance and scale
The catalogue reports that documented U.S. Department of Homeland Security AI use cases grew from 20 in 2022 to 238. This is a reported change in documented use cases, not a measure of performance or safety. Across these examples, the questions that matter include what triggers an alert, who reviews it, what happens when the system is wrong, and how affected people can challenge a decision.
Software safeguards: monitoring models and AI actions
Software and technology examples include age-assurance classification, automated checks on tool calls, model safety classifiers, and other runtime or deployment monitoring. These controls can be intended to identify disallowed inputs or actions, or to detect behavior that warrants intervention. The catalogue also includes work that was halted or changed, an indication that a deployment decision can be revised rather than treated as permanent.
For entries associated with OpenAI, Anthropic, Meta, and other companies, the catalogue records reported systems and responses. Inclusion does not amount to an independent audit or general endorsement. The useful comparison is what the safeguard observes, what event activates it, whether a person reviews the result, and what action follows—not simply whether a company describes a system as safe.
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Military sensing: distinguish pilots and announcements from validated performance
The military entries include a UK Ministry of Defence contract announcement for a surveillance-drone system and Ukrainian battlefield-data platform pilots involving drone AI, analytics, and fiber-optic sensors. These examples operate in high-consequence settings, and procurement or pilot status should not be confused with mature, independently validated performance.
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The catalogue says Avengers AI Labs’ platform processes more than 100,000 drone video feeds monthly and reports roughly 70% real-time enemy-target identification. These are claims reported through a secondary catalogue; the reviewed information does not establish the validation method, denominator, operating conditions, or independent confirmation. They should not be treated as directly comparable with accuracy claims from other tasks.
Vehicles, air traffic, robots, and conservation
Automotive and aviation
The automotive entries include aftermarket driver-assistance devices and Toyota’s Woven City pilot. The catalogue reports crashes, deaths, and injuries in connection with the aftermarket devices, but that association does not establish that AI alone caused those events. NATS and Google are described as testing AI contrail forecasts to inform flight-altitude decisions; a test is not evidence of a measured climate or safety outcome.
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Manufacturing and conservation
In manufacturing, the catalogue names Agility Robotics’ adoption of NVIDIA’s robot safety system. It does not provide a comparable outcome measure for that deployment. EarthRanger is reported to span 900 sites in 90 countries; that indicates reported reach, not demonstrated conservation impact.
The catalogue describes ESA’s EcoPulse as using satellite imagery to detect animal panic, with 87% accuracy. The reviewed account does not state the validation set or measurement conditions, so the percentage cannot be interpreted as a general performance guarantee or compared fairly with other AI accuracy figures.
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The U.S. Centers for Disease Control and Prevention names TowerScout’s computer-vision work in public-health investigations related to Legionnaires’ disease. The CDC’s AI Success Stories page does not quantify TowerScout’s impact in the reviewed summary. The CDC also notes that it maintains an annual HHS AI Use Case Inventory.
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In May 2026, the UK Office for Nuclear Regulation described a sandbox project exploring two supervised machine-learning applications for analyzing and interpreting computer-vision data, with potential uses in monitoring, inspection, and safety. More than 20 people from 10 organizations considered four industry examples through technical workshops. ONR explicitly says the findings are based on project discussions and “do not represent a formal regulatory position.” The sandbox is therefore evidence of exploratory regulatory work, not formal approval of a system or a finding that it is safe to deploy.
The UK government’s AI assurance techniques catalogue lists examples including a federated monitoring service and Aival Monitor, described as helping organizations understand how a deployed AI product works over time. A catalogue listing establishes that tools and case studies are presented there; it does not independently validate their performance.
What post-deployment monitoring needs to check
NIST’s March 9, 2026 announcement says post-deployment monitoring remains fragmented and matters because AI systems can introduce variability and behave unpredictably in real settings. Its AI 800-4 report organizes six common monitoring categories. The announcement excerpt defines two: functionality monitoring measures a system’s functions, capabilities, and features to check that it works as intended; operational monitoring measures infrastructure components to check service consistency. These are complementary: a service can remain online while its AI behavior changes, or the model can behave as intended while infrastructure fails.
For any deployment, the same questions make comparisons more meaningful:
- Purpose and hazard: What harm, failure, or condition is the system supposed to detect or prevent?
- Status: Is it an operating system, a pilot, an announcement, an outcome report, or a halted or reversed use?
- Inputs and signal: Does it process images, video, text, sensor readings, or model behavior—and what precisely triggers an alert?
- Response: Does a person review the alert, does software act automatically, or does the system only inform a decision?
- Evidence: Who reported the result, when, and with what denominator? Is the number a volume measure, an operational outcome, or a validated causal effect?
- Risk and governance: Who is affected or monitored, what errors or misuse matter, and what oversight or appeal is available?
- Changing conditions: What field conditions, infrastructure changes, or software updates could make performance degrade over time?
The reported numbers in this inventory measure unlike things: scans, images, alerts, arrests, site counts, and task-specific accuracy claims. Without common definitions, measurement conditions, and independent evaluation, none supports a single cross-sector ranking of effectiveness.
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