Input data and information sources
Video, audio and screen content come from candidates' own configured computers. The system records candidates' faces, voices, names and exam performance.
Online proctoring
Also known as: Online exam proctoring
The system monitors online exam candidates through camera, microphone and screen and flags possible cheating.
Understand the use case
What defines this use case?
It records video, audio, screen content and inputs, scores behaviour such as repeated looking away, and presents flagged passages to an invigilator. Universities use this for home-based exams; German and Dutch courts have reached differing decisions on its use.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
- System design
- Analysis and processing
- Ability to act
- Action permissions depend on integration
- Fields of use
- higher education
- Also known as
- AI-assisted exam supervision
How it works, step by step
System design and data flows
Input data
Camera images, audio, screen content and inputs arrive from candidates' computers during the exam.
Video, audio and screen content come from candidates' own configured computers. The system analyses camera images, audio, screen content and keyboard inputs together.
Processing
The system detects faces, gaze, other people, sounds and switches to other windows and assesses behaviour.
The system processes signals recorded during an examination and flags events that deviate from the expected procedure. Depending on the tool, these may include video, audio or interaction events. A flag is initially a prompt for review; its significance emerges only through examination of the evidence and the assessment context.
Results
Each candidate receives a score, with unusual passages flagged.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Use and impact
Invigilators review flagged passages and decide whether cheating occurred and whether the exam counts.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
7 review first9 clarify8 baseline threats
- Present
- Depends on your system
- Absent
- Review first
- To clarify
How to use the model
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
Changes from the typical case
None yet. Each change will appear here.
Where most threats arise
- 5 review first
- 1 review first, 2 clarify
- 3 clarify
- 2 clarify
In this system design: 7 to review first, 9 to clarify, 8 baseline threats.
Architecture as a list
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
- Sensitive Information Disclosure Review priority
- Multimodal Attacks Review priority
- Model Denial of Service
- Privacy Attacks
- Exfiltration from ML Application
Baseline for every AI system
Third-party content: Present
AI model: Part of every AI system
Baseline for every AI system
Provider service: Present
Review first
To clarify
Document collection: Depends on your system
Training and feedback: Depends on your system
To clarify
Result: Part of every AI system
Baseline for every AI system
Business application or equipment: Depends on your system
To clarify
person: Part of every AI system
Review first
- Overreliance Review priority
Affected person: Present
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
How it differs from similar use cases
Automated assessment of exam performance (essay scoring)
Assesses answers themselves and awards marks
Biometric verification and video identification
Only checks at the start that the registered person is before the camera
Data and information
Processing and operating environment
Universities use vendors' software such as Proctorio.
Storage and further use
Logging, retention periods and possible use for training must be established for the specific implementation.
Results and data sharing
Some systems terminate exams or block access on suspicion; others only flag events. Provider-side analysis sends recordings outside the university.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
From understanding the system to analysing risks
- Define the system boundary
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
- Investigate threats
Relate review candidates to actual data flows: what could happen, and who would be affected?
- Assess risk
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
- Provide evidence of measures
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
Key threats in the application context
- 3 particularly important
- 4 typical
- 9 depending on the system
- 8 additional threats for every AI system
A total of 24 threats from the threat catalogue may be relevant to this use case.
Also review: whether the system disadvantages groups
Check whether selection, ranking or assessment disadvantages people with protected characteristics, such as sex, age or origin.
Priority review points
Candidates can substitute a virtual camera or recording for the live feed or use aids outside the frame. The system then assesses images unrelated to the exam.
Invigilators focus on flagged passages and accept the suspicion. Disability, a child in the room or poor lighting can then unfairly cast suspicion on candidates.
Sensitive Information Disclosure
Recordings show homes, housemates and open programs. Logging or sending them to the provider exposes many people's private circumstances.
4 further review candidates in the typical design
Other relevant threats
Training data or an adopted base model can introduce hidden triggers for misbehaviour.
Listed because of this characteristic
- Uses another provider's model
Complex or high-volume inputs can exhaust model computing resources.
Listed because of this characteristic
- Processes text, images and speech together
Targeted queries can recover some personal or protected training data from the model.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
- Recognises or compares people by physical features
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
- Processes confidential data
Up to 9 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. One of these is particularly important here and is therefore listed above.
System features and open questions
Each Yes adds further threats to your checklist, up to 9 in total. If you cannot yet answer a question, include its threats in your review.
- Added by this Yes
- Also requires a Yes to another question
How your system is built
- Uses operational feedback2 threats
- Learns from your own data1 threat, plus 1 only in combination with other characteristics
- Changes data or triggers processes1 threat
- Learns from examples with correct answers1 threat
How you operate the system
- Accepts queries directly through an interface3 threats
- Accessible over a network1 threat, plus 1 only in combination with other characteristics
- Incurs costs with each query1 threat
- Shares computing resources with other customers or applications1 threat
- Used alongside unapproved AI services1 threat
How your system is built
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
Some systems terminate exams or block access on suspicion; others only flag events.
If yes, add
Does your organisation train or fine-tune the model itself using its own data?
If yes, add
Is the model trained on examples to which someone has assigned the correct answer?
Behavioural scoring trained on recordings with confirmed cheating uses human-labelled examples.
If yes, add
Does operational feedback or output later feed into analysis or training?
If yes, add
How you operate the system
These questions arise for almost every AI system.
Can people or programs query the model directly, for example through an interface?
If yes, add
Is the system accessible over a network or dependent on network services?
If yes, add
Do you pay per query or unit of computing time, so costs increase with use?
If yes, add
Does the system share servers or computing resources with other customers or applications?
If yes, add
Do staff also use AI services for this task that your organisation has not approved?
If yes, add
Controls and implementation
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Review priorityMultimodal Attacks2 measures
Review priorityOverreliance3 measures
Review prioritySensitive Information Disclosure3 measures
Backdoor ML Model2 measures
Model Denial of Service3 measures
Privacy Attacks3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
-
Art. 4: AI literacy
For every AI system Applies since 2 February 2025.
-
Art. 5 para. 1 point e, f, g, h: Prohibited biometric practices
Depending on use Review trigger since 2 February 2025.
-
Annex III no. 3: Education and vocational training
In typical use Review trigger from 2 December 2027.
Applies to every AI system
Art. 4: AI literacy
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Obligations and details for Art. 4: AI literacy
- Who bears the obligations
- Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
May apply in typical use
Annex III no. 3: Education and vocational training
Applies if the system monitors candidates and is intended to detect prohibited behaviour, for example in university or certification exams.
Applies from 2 December 2027.
Obligations and details for Annex III no. 3: Education and vocational training
- The general condition is
- Applies if the system determines access or admission, evaluates learning outcomes, assesses educational level or supervises examinations.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
May apply in particular uses
Art. 5 para. 1 point e, f, g, h: Prohibited biometric practices
Applies if the system infers candidates' emotions, such as nervousness or stress, from faces or voices in an educational institution.
Applies since 2 February 2025.
Obligations and details for Art. 5 para. 1 point e, f, g, h: Prohibited biometric practices
- The general condition is
- Applies if the system indiscriminately collects facial images from the internet or video, recognises workplace or educational emotions, categorises people biometrically by sensitive attributes, or identifies people in public spaces in real time for law enforcement.
- Exception
- Workplace and educational emotion recognition is permitted for medical or safety reasons. Real-time law enforcement identification has narrowly defined exceptions.
- Who bears the obligations
- Provider, Deployer, Importer, Distributor, Authorised representative, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
Generally does not apply
Annex III no. 1 point a: Remote biometric identification
Initial identity checks merely confirm that the registered person is participating. Annex III, point 1(a), excludes this verification.
Obligations and details for Annex III no. 1 point a: Remote biometric identification
- The general condition is
- Applies if the system identifies people remotely using biometric features.
- Exception
- Systems that only confirm a person's claimed identity.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Cross-cutting threats
Review these threats for every AI system, whatever its purpose.
Atypical system features
View 16 further questions
If any of these features does apply to your system, add the listed threats.
Does the system generate its own text, such as answers, drafts or summaries?
The system marks recording passages and returns a score, rather than text.
If yes, add
Does the system generate or edit images, sounds, voices or videos?
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
If yes, add
Can users give the system natural language tasks that it then performs?
If yes, add
Does the system search its own collection of documents or records for relevant content?
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Can people outside your organisation use the system, including without login?
Only registered candidates and invigilators use the system, rather than the public.
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Does the system execute programs, scripts or commands itself?
If yes, add
Is the system intended to maximise a specified metric, such as revenue, utilisation or clicks?
If yes, add
Sources
- Legislation, norm or standard
EU AI Act, Annex III, point 3(d)
The Act expressly lists AI systems monitoring students and detecting prohibited behaviour during exams in Annex III.
- Other source
Society for Civil Rights, Monitoring online exams
The organisation describes use at German universities and the Thuringian Higher Regional Court's decision.
- Authority, court or parliament
Amsterdam District Court, Judgment of 11 June 2020, ECLI:NL:RBAMS:2020:2917
The Court describes automated behavioural scoring from recordings and an invigilator reviewing footage where behaviour is flagged.
Related use cases
Also in the field of Employment and education
- AI-assisted candidate screeningThe system uses CVs and cover letters to identify the best match for a role and suggest whom the company should invite for interview.
- Algorithmic managementSoftware assigns tasks and shifts to workers, tracks what they do and evaluates their performance.
- Automated assessment of exam performance (essay scoring)A model assesses written or spoken exam answers and awards marks, alone or alongside human examiners.
Often used together with
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured