Taxonomy

Accessibility

Ensures that everyone has equal access to a space or a service.

Arts & Culture

Enables artistic and/or cultural expression.

Border & Immigration

Supports immigration, asylum, or border-crossing decisions or processing — for example, border-control facial recognition, visa adjudication, asylum risk assessment.

Commerce

Enables the buying and selling of goods and services.

Content Moderation

Filters, ranks, or removes user-generated content based on policies — for example, comment moderation on a public-facing kiosk, image filtering, or hate-speech detection.

Dining

For providing food or meal services.

Ecology

Supports the measurement or monitoring of the natural environment.

Education & Learning

Supports teaching, learning, assessment, or educational guidance — for example, tutoring assistants, automated grading, or library and museum recommenders.

Eligibility & Public Benefits

Determines eligibility for, or distribution of, government benefits and essential public services — for example, welfare allocation, housing assistance, child-services risk scoring.

Employment & Work

Supports hiring, scheduling, performance evaluation, or workplace operations — for example, resume screening, shift assignment, productivity monitoring.

Energy Efficiency

Reduces energy use and/or helps conserve energy.

Enforcement

Used for enforcement of rules or regulations.

Entry

Supports authentication or validation in order to access a space or a service.

Financial Services

Supports lending, insurance, fraud detection, or other financial decisions about people — for example, credit scoring, insurance underwriting, transaction-fraud flagging.

Fire & Emergency

Supports services that ensure public safety and health related to emergencies.

Environmental Health

Measures or monitors aspects of the physical environment that affect human health — air quality, water quality, radiation, noise, or workplace exposures. Distinct from Healthcare, which supports clinical care for individuals.

Healthcare

Supports clinical care for individuals — for example, diagnosis assistance, treatment recommendations, patient triage, or health chatbots. Distinct from Environmental Health, which monitors the physical environment.

Inform

Supports general-purpose information assistance — concierge, FAQ, signage, or guidance about a service. Distinct from Wayfinding & Services, which covers spatial orientation and routing within a place.

Logistics

Supports the movements of goods or materials.

Marketing & Personalization

Targets advertising, personalizes recommendations, or profiles audiences for marketing — for example, in-store loyalty offers, personalized signage, behavioural ad targeting.

Mobility

Supports how people and materials move around.

Planning & Decision-making

Supports forecasting and policy-impact analysis at the population or system level — for example, urban planning, infrastructure planning, or measuring the impact of a policy decision. Distinct from Risk Assessment & Triage, which scores individuals.

Research & Development

Supports a deployed research instrument — a system that studies a place, population, or environment as part of a research program. Distinct from production AI that delivers a service to the public.

Risk Assessment & Triage

Scores or prioritizes individuals or cases for further attention or action — for example, recidivism scoring, hospital triage, child-welfare risk flags. Distinct from Planning & Decision-making, which forecasts at a population or system level.

Safety & Security

Supports preventive risk reduction in physical environments — for example, fire safety, home security, or ensuring safe passage in airports or on roads. Distinct from Enforcement (rule application) and from Fire & Emergency (incident response).

Social

For interacting with another person or a group.

Translation & Language Access

Translates speech, text, or signs to make a place or service accessible across languages — for example, real-time interpretation at a service counter, multilingual signage.

Waste Management

Supports the handling and disposal of waste, including as recyclables, compost and hazardous materials.

Water Efficiency

Reduces water use and/or helps conserve water.

Wayfinding & Services

Enables navigation of a location and its amenities and services.

Institution

The entity that is responsible and accountable for this data collection activity

Organization

The entity that is responsible and accountable for this data collection activity

People

The people this system is used on, or whose lives its decisions affect.

Acts

Acts — plans a goal into steps and carries them out in software: calling tools, booking, filing, escalating, running multi-step workflows on someone's behalf. The output is an action taken, not a piece of content returned. Examples: booking a meeting, filing a return, escalating a ticket. When the action moves something physical, that is Moves.

Answers

Answers — responds to people's questions in conversation: a chatbot, a voice assistant, an automated phone line. It holds a back-and-forth addressed to you in the moment. Examples: a transit chatbot explaining a delay, a service line routing your request. Distinct from Creates, which produces content that stands on its own afterward.

Creates

Creates — produces new text, images, audio, video, or code that did not exist before this system ran. The output is content authored by the system, not a label or score about existing data. Examples: drafting a paragraph from a prompt, generating an image from a description, composing audio, completing code. Distinct from Answers, which responds to you in conversation.

Decides

Decides — determines an outcome that takes effect: approving or denying, pricing, allocating, flagging for action. Examples: a dynamic road toll set from traffic, an application approved or refused, a dispatch assignment. When a person makes the final call from its suggestions, that is Recommends instead.

Identifies

Identifies — recognizes who or what something is by matching a face, voice, fingerprint, plate, or account against known records. It turns "someone is here" into "this person is here." Examples: a face match at a boarding gate, a plate lookup at a toll gantry. Distinct from Senses, which detects without naming.

Moves

Moves — acts on the physical world. The system drives motors, valves, signals, or other actuators and changes something around you. Examples: steering a robot, changing a traffic-signal phase, opening a gate, adjusting a building's ventilation, releasing a parking barrier.

Predicts

Predicts — estimates what is likely: a forecast, a risk score, a probability. It says what may happen; someone or something else still decides what to do about it. Examples: a queue-length forecast, a credit-risk score, a crowding prediction for the next hour. Distinct from Decides, which makes an outcome take effect.

Recommends

Recommends — suggests options for a person to choose from: a ranking, a shortlist, a suggested route or treatment. Nothing takes effect until someone accepts the suggestion. Examples: a triage ranking for a clinician, an alternate route offered to a driver, products shown first. Distinct from Decides, where the outcome takes effect on its own.

Senses

Senses — turns raw signals from cameras, microphones, or sensors into things the rest of the system can use: detections, transcriptions, readings. Includes continuous monitoring that watches for events. Examples: detecting a vehicle in a camera frame, transcribing a spoken sentence, noticing that a platform is crowded. Distinct from Identifies, which puts a name to who or what it perceives.

Understands

Understands — pulls meaning from text or speech, finds related ideas, and connects what it reads to saved context. It returns matches, intents, summaries, or links to relevant prior knowledge, not new content. Examples: matching a question to the right help article, retrieving the policy clause that applies to a case, recognizing the topic of a sentence.

Loss of autonomy

The risk that the AI system limits a person's ability to make their own decisions, control their identity, or choose freely. This includes risks of restricted access to alternatives, deceptive design that nudges decisions toward a particular outcome, and lack of meaningful consent. Mitigations may include opt-out mechanisms, transparent recommendations, defaults that protect agency, and human review of consequential decisions.

Civil liberties harm

The risk that the AI system compromises fundamental human rights and civil liberties — speech, assembly, movement, due process, or freedom from arbitrary surveillance. This includes risks from mass facial recognition, predictive policing, censorship of lawful expression, and chilling effects on protest. Mitigations may include strict purpose limits, judicial or independent oversight, narrow data retention, and human-rights impact assessments before deployment.

Environmental harm

The risk that the AI system causes environmental degradation through energy consumption, water use, e-waste, carbon emissions, or resource extraction for hardware. This includes risks from training-time compute, always-on inference, cooling demands of data centers, and rapid hardware churn. Mitigations may include energy and water efficiency targets, transparent reporting of compute footprint, hardware-lifecycle planning, and renewable-energy procurement for data-centre operations.

Financial & business harm

The risk that the AI system causes financial losses to individuals or organizational damage through erroneous pricing, denied services, fraud, or market manipulation. This includes risks from automated credit decisions, dynamic-pricing inequities, AI-enabled scams, and supply-chain disruption from misuse. Mitigations may include affordability guards, fairness audits of pricing models, fraud-detection layers, and clear redress procedures.

Physical harm

The risk that the AI system's outputs lead to physical injury to individuals or damage to property. This includes risks from autonomous vehicles, robotic actuation, faulty navigation guidance, or critical-infrastructure errors that put bodies or property in harm's way. Mitigations may include rigorous safety testing, fail-safe defaults, geofencing of hazardous behavior, and human oversight of high-stakes physical actions.

Political & economic harm

The risk that the AI system manipulates political discourse, interferes with elections, concentrates market power, or damages public institutions. This includes risks of synthetic media targeting voters, opaque ad-targeting, competitive distortion from monopoly access to data and compute, and erosion of institutional trust. Mitigations may include disclosure of synthetic content, antitrust attention to AI markets, audit access for regulators, and political-ad transparency requirements.

Psychological harm

The risk that the AI system causes emotional or mental-health impairment, directly or indirectly. This includes risks from addictive feedback loops, distress caused by surveillance, harassment via generated content, and anxiety from automated denials. Mitigations may include content warnings, well-being safeguards, age-appropriate design, accessible support channels, and rate limits on engagement-maximizing behavior.

Reputational harm

The risk that the AI system damages the reputation of individuals, groups, or organizations through misidentification, false categorization, or stigmatizing labels. This includes risks of inaccurate face recognition, defamatory generated content, and unfair public scoring that follow people across contexts. Mitigations may include accuracy thresholds before public-facing labels are applied, human review of high-impact identifications, takedown procedures, and clear rights to correction.

Societal & cultural harm

The risk that the AI system harms communities or culture through erosion of trust, loss of linguistic and cultural diversity, or unhealthy dependency on opaque systems. This includes risks of misinformation at scale, homogenization of cultural expression, replacement of local knowledge, and degradation of public-information ecosystems. Mitigations may include multilingual support, partnerships with affected communities, provenance signals on generated content, and investment in local-language and local-context models.

Right to Access

The right to request and receive information about what personal data an AI system has collected about you, how this data is being used, and what decisions have been made using this information. This includes the right to obtain a copy of your data in a readable format.

Right to Algorithmic Transparency

The right to be informed in plain language about how an AI system works in general — the logic involved, the significance of the processing, and the likely consequences for the people it affects. This is the right to understand the system itself; the right to an explanation of a specific decision made about you is a separate right.

Right to Be Forgotten

The right to have your personal data deleted from an AI system when the data is no longer needed for its original purpose, when you withdraw consent, or when there is no legitimate interest in continuing to process it. Where technically feasible, this also includes having the system "unlearn" information derived from your data. This is a right of deletion; the right to stop processing without deletion is a separate right.

Right to Contest

The right to challenge a decision an AI system has made about you — to provide additional information, express your point of view, and have the decision reconsidered based on your input. This is a remedy after a decision has been made; the right to a human review before a decision is acted on is a separate right.

Right to Correct Your Data

The right to have inaccurate or incomplete data that an AI system holds about you corrected or completed. This is separate from the right to access your data (which is the right to read it) and the right to be forgotten (which is the right to delete it). When the original data is corrected, downstream decisions made from the faulty data should be revisited.

Right to an Explanation of a Decision

The right to a clear and meaningful explanation of how an AI system contributed to a specific decision affecting you — what role the system played, the main factors that led to the outcome, and what it means for you. This right covers the decision actually made about you; the right to be informed about how the system works in general is a separate right.

Right to Non-discrimination

The right to be free from discriminatory treatment by AI systems based on protected characteristics such as race, gender, age, religion, disability, or sexual orientation. Organizations must implement and demonstrate appropriate technical and organizational measures to prevent discriminatory outcomes from their AI systems.

Right to Stop Processing

The right to ask an organization to stop using your personal data, even when the data itself is not deleted. This is separate from the right to be forgotten (which deletes the data) and from the right to purpose limitation (which constrains the original stated purposes). It is the user-invoked stop-now affordance — your data may stay on file, but no further processing should happen against it.

Right to Purpose Limitation

The right that your data is only used for the specific purposes that were stated when it was collected. The organization must declare and document those purposes before collection begins, and is barred from later using your data for new, unrelated purposes without your knowledge or consent. This is an upstream limit on what use is ever allowed; the right to stop processing already underway is a separate right.

Right to a Human Review

The right to have a person — not an automated system — make or meaningfully reconsider a decision affecting you. This is a path to a non-automated decision, separate from the right to challenge a decision that has already been made. The reviewing person must have the authority and information to actually change the outcome.

Right to Be Informed of AI Use

The right to know that an AI system is in operation before any decision affecting you is made. This includes being told when you are interacting with an automated system rather than a person, when content is AI-generated, and when biometric or emotion-recognition technology is in use. This is the precondition for every other right in this category — you cannot exercise rights you do not know apply to you.

Biometric Scanner

Captures a physical trait for identification — fingerprint, palm, iris, or a face scan at a gate, kiosk, or turnstile. The capture device itself; matching the scan against stored templates is biometric recognition, which appears under Algorithm or Model.

Camera

Captures still images or video of a place — mounted on a pole, a vehicle, a drone, or built into a device. Includes infrared and thermal cameras. This is the capture device itself; the techniques that analyze the footage appear under Algorithm or Model, and how identifiable the footage is appears on the Input Data category.

Connected Device Telemetry

Readings streamed automatically by connected things — vehicles reporting speed and position, wearables reporting heart rate, smart meters reporting energy use, machines reporting their own status. Continuous capture that runs in the background of everyday use.

Environmental Sensor

Measures conditions in the surroundings — air quality, temperature, humidity, noise level, water level, or weather. These sensors read the environment, not people, though noise and air readings can still reflect human activity.

Existing Records

Data the organization already holds — customer accounts, case files, registries, transaction histories, past applications. Nothing new is captured from the world; the system draws on what was recorded before, sometimes for a different original purpose.

Microphone

Captures sound — speech, ambient noise, or specific audio events like breaking glass or gunshots. Found in smart speakers, kiosks, intercoms, and acoustic monitoring installations. The capture device itself; speech recognition and other analysis appear under Algorithm or Model.

Network Connection

Connects this system to a network — a cellular modem, a WiFi access point, or a wired link. It moves data rather than capturing it, but connection logs can still reveal which devices were nearby or connected, and an access point that offers public WiFi is also a service people join.

Positioning & Location

Determines where something is — GPS receivers in vehicles or apps, Bluetooth beacons, or indoor positioning systems. Produces location traces, one of the most revealing kinds of personal data when tied to a person or their device.

Presence & Motion Sensor

Detects that someone or something is there, or is moving, without capturing an image — passive infrared, ultrasonic, pressure mats, door counters, seat occupancy sensors. Tells the system that a space is occupied, not who occupies it.

Third-Party Data Feed

Data received from another organization — purchased, licensed, or delivered through an API or data-sharing agreement. Examples: credit bureau records, weather services, map providers, data brokers. The people the data describes may never have dealt with this system's operator.

User Input

Information people provide deliberately — a form they fill in, text they type, a search they run, a photo they upload, a voice command they speak. The person knows they are handing something over, unlike sensing that happens around them.

Vehicle Detection

Senses vehicles on a road or in a space — radar, LiDAR, induction loops in the pavement, piezoelectric strips, or gantry-mounted detectors. Used to count traffic, measure speed, and detect congestion. License-plate cameras belong under Camera.

Wireless Signal Sensing

Detects the radio signals devices give off — WiFi probes from phones, Bluetooth, RFID tags, or NFC taps. Often used to count or track devices moving through a space; a device's signal can act as a persistent identifier for the person carrying it.

Data about a measurement

Sensor readings — air quality, temperature, sound level, energy, water flow. The AI takes in numeric measurements from physical sensors, usually with no person attached. Examples: PM2.5 readings from an air-quality monitor, decibel levels from a sound sensor, kilowatt-hours from a meter.

Data about a place

Locations, routes, surroundings, or floor plans. The AI takes in data describing where something is. Examples: GPS coordinates from a phone, foot-traffic counts on a sidewalk, a map of building corridors.

Data about behaviour

Taps, choices, paths walked, dwell time, purchases, or queries. The AI takes in records of what people did. Examples: tapped destinations on a wayfinding kiosk, items added to a cart, a search query typed into a public terminal.

Biometric data

Faces, fingerprints, voice prints, gait, gestures, gaze, posture. The AI takes in biological signals from a person's body. Examples: a face captured at a turnstile, a voice recorded by a kiosk microphone, gait analyzed by an overhead camera.

A decision about you

An eligibility, classification, ranking, or yes/no that another system already made about a person. The AI takes in this prior decision and uses it as input. Examples: a credit score from another model, an access-allow flag from upstream, a triage class assigned earlier in the pipeline.

Generated content

Text, images, audio, or video that another AI made. The AI takes in this synthetic content and processes it further. Examples: an LLM-written summary fed into a translator, a generated photo passed to a moderation model, a text-to-speech clip routed to an audio system.

Operational data

Schedules, routes, budgets, occupancy counts, public records, or other administrative data — not about any one person. The AI takes in data describing how a place or service runs. Examples: school occupancy by hour, trash-collection routes, a transit timetable, a department budget.

A physical action

A signal that something in the physical world changed — a door opening, a light turning on, an alert sounding. The AI takes in evidence of these changes from upstream actuators or sensors. Examples: a turnstile-unlock event, an HVAC adjustment recorded by a sensor, a public-address alert played upstream.

A recommendation or prediction

A suggestion, forecast, risk score, or ranking produced earlier by another system. The AI takes in this advisory output and uses it. Distinct from a binding decision because it advises rather than determines. Examples: a forecasted demand curve, a risk score from a prior model, a recommended route from a navigation service.

Sensitive personal data

Health, finances, beliefs, sexuality, immigration status, or other personal information that carries legal and social risk. The AI takes in this kind of protected data. Examples: insurance eligibility at a clinic kiosk, a benefits-application status, a self-declared pronoun.

Affect & Emotion Analysis

AI systems that infer emotional state, mood, or sentiment from text, speech, facial expressions, or physiological signals. Used to gauge crowd sentiment, evaluate service experience, or screen for distress. The accuracy and cultural fairness of affect inference is contested, and some jurisdictions restrict its use in workplaces and public services.

Anomaly Detection

AI systems that flag unusual events, patterns, or values that depart from a learned baseline of normal behavior. Used for equipment-failure prediction, fraud screening, intrusion detection, water-leak monitoring, and unusual crowd-flow alerts. Outputs are typically thresholded scores rather than direct decisions.

Biometric Recognition

AI systems that identify or verify a specific person from biological signals — face, fingerprint, voice, iris, or gait. Distinct from generic computer vision because the output is tied to a named individual. Many jurisdictions restrict or prohibit biometric recognition in publicly accessible spaces.

Classification & Prediction

AI systems that assign a label to an input or estimate a numeric outcome — including classifiers (spam vs. not spam, risk tier), regressors (predicted demand, expected wait), and time-series forecasters (next-hour ridership, energy load). Built from labeled historical data using statistical or machine-learning models.

Clustering & Segmentation

AI systems that group people, places, or events into clusters based on similarity, without using pre-defined labels. Used to segment audiences, mobility patterns, energy-use profiles, or service-demand zones. Outputs are cluster assignments that downstream systems may treat as categories — even though clusters are inferred, not authored.

Computer Vision

AI systems that interpret images or video — counting people, detecting objects, reading text in pictures, tracking motion, or segmenting scenes. Common in occupancy sensing, traffic and crowd monitoring, accessibility assistance, and signage inspection. Distinct from biometric recognition, which identifies specific individuals.

Language Models

AI systems that read, write, summarize, translate, or hold a conversation in human language. Includes large language models (LLMs), chatbots, and generative-text systems. They work by predicting the most likely next tokens given the input and any prior context.

Optimization

AI systems that search for the best assignment, route, schedule, or allocation under constraints. Includes vehicle routing, transit signal timing, energy and HVAC scheduling, staff rostering, and resource allocation. Works by exploring possibilities to maximize or minimize a stated objective such as cost, time, or emissions.

Privacy-Preserving Transformation

AI systems whose primary job is to transform input data so identifying details are removed, blurred, or replaced before any downstream use. Includes face blurring, license-plate masking, tokenization, k-anonymization, differential privacy, and synthetic-data generation. Acts as a barrier between raw collected data and the data used for analysis or decisions.

Recommendation & Ranking

AI systems that suggest or order items for a person — services, listings, news, wayfinding options — based on preferences, behavior, or similarity to other people. Includes collaborative filtering, content-based recommenders, and learned ranking. Personalizes what each person sees from the same underlying catalog.

Search & Retrieval

AI systems that find and surface relevant content from a body of documents, records, or media in response to a query. Includes semantic search, vector retrieval, and retrieval-augmented generation (RAG) pipelines that feed relevant passages into a language model. Used in document QA, public-records lookup, and library or archive search.

Speech & Audio

AI systems that convert between spoken words and other formats, or that classify non-speech sound. Includes speech-to-text (transcription), text-to-speech (synthetic voice), keyword spotting, and audio event detection. Voice-as-identity (recognizing who is speaking) belongs in Biometric Recognition.

Data about a measurement

The AI produces numeric measurements over time — sensor outputs, derived quality scores, forecasted values. Examples: an hourly air-quality score for a plaza, a predicted ridership curve for tomorrow, an aggregated noise-level reading published to a public dashboard.

Data about a place

The AI produces location, route, region, or floor-plan output. Examples: a wayfinding route shown on a kiosk, a heatmap of foot-traffic density, a geofence boundary published to downstream systems.

Data about behaviour

The AI produces records of what people did — paths walked, items selected, dwell times, interaction patterns. Examples: a weekly report of most-visited destinations, a session log of touchscreen interactions, an inferred route someone took through a venue.

Biometric data

The AI produces output containing biological signals from a person's body — face templates, voice embeddings, gait vectors, gesture sequences. Examples: an enrolled face template stored downstream, a voice embedding for later matching, a gait signature exported to another system.

A decision about you

The AI produces a binding determination about a person — eligibility, classification, ranking, or yes/no. Examples: an access-control system deciding to open a gate, an automated benefits eligibility result, an automated screening that rules someone out.

Generated content

The AI produces new content — text, images, audio, or video that did not exist before. Examples: an LLM-written service advisory shown on a sign, a synthesized voice announcement played over a public-address system, a generated illustration on a digital kiosk.

Operational data

The AI produces administrative output — schedules, routes, occupancy estimates, budget allocations, or other records about how a place or service runs. Not about any one person. Examples: an optimized trash-collection route, a school occupancy projection, a recommended budget allocation for next quarter.

A physical action

The AI triggers something in the physical world — a door unlocking, a light turning on, an alert sounding, signage changing, HVAC adjusting. Examples: a turnstile that opens on facial match, a public-address alert played automatically, an HVAC adjustment commanded by a building model.

A recommendation or prediction

The AI produces an advisory output — a suggestion, forecast, risk score, or ranking. Distinct from a binding decision because it advises rather than determines. Examples: a signage system suggesting an alternate route based on predicted crowding, a forecasted demand curve for tomorrow, a recommended next destination on a kiosk.

Sensitive personal data

The AI produces output about health, finances, beliefs, sexuality, immigration status, or other categories that carry legal and social risk. Examples: a triage priority assigned at a clinic, an eligibility result sent to a benefits portal, an inferred protected attribute exported downstream.

An automatic charge or payment

The output is money moving without you acting — a toll deducted as you drive through a gantry, a fare charged as you tap out, a fine issued, a payout released. Often the least visible encounter: you learn about it from a statement afterwards.

A chatbot or voice reply

The output arrives as a conversational answer — a chatbot message, a voice assistant's response, an automated phone agent. You encounter the system by talking with it, and it should be clear you are not talking to a person.

A dynamic price or fee

The output shows up as what something costs — a road toll that rises with congestion, surge pricing for a ride, a parking rate that changes by demand, a personalized offer. You may never see the system itself, only the price it set.

What you see in an app or feed

The output is the ordering itself — which posts, products, results, or prices appear first, and which you never see. There is no moment of encounter to notice; the system shapes the surface you were already looking at.

A gate, barrier, or machine

The output is something moving — a turnstile that opens or stays shut, a boom gate, a delivery robot, a door lock. The physical motion is how you encounter the decision; the command that triggers it is the system's output data.

A mobile notification

The output reaches you on your own device — a push notification, text message, email, or in-app alert. The system finds you rather than waiting for you to look; whether you can turn it off is an Access and Rights question.

A person acting on it

The output reaches you through a human — an officer who stops you after an alert, a case worker who follows a recommendation, a doctor reading a risk score. You meet a person, but a system shaped what they do; your right to know and contest that still applies.

A printed ticket or letter

The output arrives on paper — a parking ticket, a queue number, a boarding pass, a decision letter in the mail. The system's work reaches you through a physical document, sometimes days after it ran.

A screen or sign

The output appears on a display in the place — a variable message sign over a road, a departure board, a kiosk screen, a dashboard readout. You encounter the system by reading what it shows.

A signal or light change

The output changes what the environment tells you — a traffic light that adapts to flow, street lighting that brightens when people pass, a status light turning red. Subtle enough that most people never realize a system decided it.

A spoken announcement

The output is played aloud in the place — a public-address announcement, a synthesized voice at a crossing or platform, an audible alarm. Reaches everyone present, including people who never looked at a screen.

Available for resale

The data collected may be resold to other 3rd parties

Available to 3rd parties

Data is available to 3rd parties not involved in the data activity. This does not always mean that data is being resold.

Available to download

Data that can be accessed and downloaded online, either for free or for a fee

Available to me

Available to me but not to other individuals. For example, as an individual you have access to all your electronic toll records for your car, but other individuals do not have access to that.

Available to the accountable organization

Data is available to the accountable organization

Available to vendor

Data is available to the data collection or technology provider

Not available to me

Not available to me or other individuals. As an individual, there isn't a way for you to access this data.

Not available to the accountable organization

Data is not available to the accountable organization

Not available to vendor

Data is not available to the data collection or technology provider.

Retained {{duration}}

Data is stored for {{duration}}, and after this time period is deleted

No data retained

No data is kept or stored

Backed up internationally

Data is backed up outside the jurisdiction where it was collected.

Backed up locally

Data is backed up with the jurisdiction where it was collected.

Shared storage and governance

In a shared data store model the data store is shared across multiple parties and because the infrastructure is shared it becomes possible to enforce governance around the retention, access and destruction of the data by policies built into the structure of the data itself.

Stored locally

Data is stored in the jurisdiction where it was collected.

Stored on 3rd Party Cloud

Data is stored on behalf of the organization or the data collector in an off-site data centre, such as Amazon Web Services, Google Cloud and Microsoft Azure

Stored primarily internationally

Data is stored outside the jurisdiction where it was collected.

Stored primarily locally

Data is stored mainly in the jurisdiction where it was collected.