Input data and information sources
Pleadings, applications and correspondence also come from parties and people outside the organisation. The system specifically searches for names, addresses, dates of birth and other personal information.
Automated anonymisation and redaction
Also known as: Automated anonymisation, PII redaction
The system finds names, addresses and other personal information in documents and suggests redactions for a person to approve.
Understand the use case
What defines this use case?
Courts in Baden-Württemberg and Hesse use this approach to anonymise judgments before publication. Amsterdam uses it to redact documents released in response to access requests.
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
- judiciary, public administration
- Also known as
- redaction software, pseudonymisation, PII detection
How it works, step by step
System design and data flows
Input data
A judgment, file or other document is to be published or released.
Pleadings, applications and correspondence also come from parties and people outside the organisation. For scanned documents, the system analyses images and text together to place redactions correctly.
Processing
A detection model searches for names, addresses and other information that identifies a person.
A detection model marks passages or image regions that can be linked to individuals. An editing component replaces or removes the selected areas. Detection, suggestions and effective technical removal are separate steps; a visible black rectangle alone does not tell you which data remain in the file.
Results
The system suggests redactions or placeholders for a staff member to review and approve.
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
The edited document is published or released to the requesters.
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.
5 review first17 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 clarify
- 5 clarify
- 3 review first, 1 clarify
- 1 review first, 2 clarify
In this system design: 5 to review first, 17 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
- Privacy Attacks Review priority
- Sensitive Information Disclosure Review priority
- Exfiltration from ML Application
To clarify
Baseline for every AI system
Third-party content: Present
AI model: Part of every AI system
To clarify
- Sleepy Agent (Time/Event-Triggered Hidden Instructions)
- Prompt Injection: Direct
- Prompt Injection: Indirect
- Jailbreaks
- Meta Prompt Extraction
Baseline for every AI system
Provider service: Depends on your system
To clarify
Training and feedback: Present
Review first
To clarify
Result: Part of every AI system
To clarify
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
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
Intelligent document processing (IDP)
Extracts information from documents and transfers it to a case management system
E-discovery and technology-assisted review (TAR)
Sorts large collections by relevance to an investigation, with redaction only as a secondary function
Data and information
Processing and operating environment
The model processes the inputs. Whether it runs internally or with a service provider must be established for the specific implementation.
Storage and further use
Some deployers further train detection using their own judgments or files. Corrections made during approval can feed back as new training examples.
Results and data sharing
Depending on the workflow, redactions take effect immediately or only after approval; JANO and Amsterdam both require human approval. With a cloud service, documents leave the organisation's environment before they are redacted.
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
- 2 typical
- 17 depending on the system
- 8 additional threats for every AI system
A total of 30 threats from the threat catalogue may be relevant to this use case.
Priority review points
Sensitive Information Disclosure
The system misses an unusually spelt name or information in a footnote. The document is published and the person can be identified.
Names are redacted, but occupation, place of residence and the time of the offence remain. Comparing the published judgment with other sources can still identify the person.
Most suggestions are correct, so approval becomes routine. Missed passages are then discovered only after publication.
2 further review candidates in the typical design
Other relevant threats
Targeted Poisoning / Label Poisoning
Falsified training labels can deliberately shift model outputs.
Listed because of this characteristic
- Learns from examples with correct answers
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 17 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 17 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
- Performs natural language tasks5 threats
- Generates its own text2 threats, plus 1 only in combination with other characteristics
- Learns from your own data2 threats, plus 1 only in combination with other characteristics
- Processes text, images and speech together2 threats
- Changes data or triggers processes1 threat
- Uses another provider's model1 threat
- Uses operational feedback1 threat
How you operate the system
- Accepts queries directly through an interface4 threats, plus 1 only in combination with other characteristics
- Accessible over a network2 threats, 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
Does the system generate its own text, such as answers, drafts or summaries?
Traditional methods only mark passages. If a language model inserts substitute terms or rewrites passages, it generates text itself.
If yes, add
Can users give the system natural language tasks that it then performs?
If a language model is involved, it may also follow instructions embedded in the document.
If yes, add
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
Depending on the workflow, redactions take effect immediately or only after approval; JANO and Amsterdam both require human approval.
If yes, add
Does your organisation train or fine-tune the model itself using its own data?
Some deployers further train detection using their own judgments or files.
If yes, add
Does the model or its base model come from another provider, even if you host it?
If yes, add
Does operational feedback or output later feed into analysis or training?
Corrections made during approval can feed back as new training examples.
If yes, add
Does the system process several input types together, such as text with images or speech?
For scanned documents, the system analyses images and text together to place redactions correctly.
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 prioritySensitive Information Disclosure3 measures
Review priorityPrivacy Attacks3 measures
Review priorityOverreliance3 measures
Targeted Poisoning / Label Poisoning3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
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
Generally does not apply
Annex III no. 8 point a: Administration of justice
The system prepares judgments for publication. It does not help courts establish facts or interpret legal provisions.
Obligations and details for Annex III no. 8 point a: Administration of justice
- The general condition is
- Applies if the system helps courts or dispute resolution bodies establish facts and interpret law.
- Exception
- Purely ancillary tasks such as anonymising judgments or staff communication are excluded under recital 61.
- 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 15 further questions
If any of these features does apply to your system, add the listed threats.
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
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
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Can people outside your organisation use the system, including without login?
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
- Report of a deployment
Baden-Württemberg Ministry of Justice and Migration, New AI tool for the judiciary
Since 12 December 2025, JANO has been available to all civil divisions of the ordinary courts in Baden-Württemberg; staff review and approve its suggestions.
- Report of a deployment
Hessian Ministry of Justice, Hesse and Baden-Württemberg to use JANO AI tool in the judiciary
Hesse initially uses JANO at Darmstadt Regional Court; court staff review and approve the suggestions.
- Register or case collection
Dutch Algorithm Register, Anonimiseringssoftware, Gemeente Amsterdam
The city automatically redacts personal data in documents.
Related use cases
Also in the field of Documents and casework
- Intelligent document processing (IDP)The system reads correspondence, invoices and forms, routes them to the responsible team and transfers their data into business systems.
- E-discovery and technology-assisted review (TAR)The system ranks large document collections by their likely relevance to an investigation or information request, making review easier.
- Text analytics for feedback and consultation responsesThe system summarises large volumes of free-text survey, complaint or consultation responses, identifies themes and classifies each response.
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