Frameworks EU legal act, binding European Union (EUR-Lex)
GDPR (General Data Protection Regulation)
The Union’s data protection law: it applies to AI systems as it does to any other processing of personal data.
What this framework governs
The General Data Protection Regulation governs the processing of personal data within its scope. Its requirements also apply to the development, training and use of AI systems.
In the catalogue it appears wherever a threat touches personal data, whether in training data, in outputs, in logs or in data an attacker reconstructs from a model.
It is complemented by Opinion 28/2024 of the European Data Protection Board, which clarifies when an AI model can be considered anonymous, under which conditions legitimate interest may serve as the legal basis for development and deployment, and what follows from training data that was processed unlawfully.
Mappings in the threat catalogue
GDPR (General Data Protection Regulation). Publisher: European Union (EUR-Lex) and European Data Protection Board (EDPB). The Versatile AI Risk Assessment threat catalogue reaches 8 of its 52 threats through this framework, backed by 2 documents at 5 locations. Mappings are documented for 8 of the 52 threats in the catalogue. The overview shows their distribution by topic group.
44 threats without a mapped reference
The catalogue contains no reference from this framework for these threats. This does not establish whether the original document addresses them. See the threat pages for recorded mappings to other frameworks.
- Supply Chain – Infrastructure Supply Chain and Provenance
- Supply Chain – Models Supply Chain and Provenance
- Training Data Poisoning Model and Training Data Manipulation
- Targeted Poisoning / Label Poisoning Model and Training Data Manipulation
- Backdoor ML Model Model and Training Data Manipulation
- Sleepy Agent (Time/Event-Triggered Hidden Instructions) Model and Training Data Manipulation
- Model Theft Attacks on the Running Model and Service
- Adversarial Inputs Attacks on the Running Model and Service
- Prompt Injection – Direct Prompt Attacks and Guardrail Evasion
- Prompt Injection – Indirect Prompt Attacks and Guardrail Evasion
- Jailbreaks Prompt Attacks and Guardrail Evasion
- Meta Prompt Extraction Prompt Attacks and Guardrail Evasion
- Model Denial of Service Attacks on the Running Model and Service
- Cost Harvesting / Repurposing Attacks on the Running Model and Service
- Insecure Output Handling Application and Integration Security
- Insecure Tool Design Application and Integration Security
- Application Vulnerabilities Application and Integration Security
- Application Denial of Service Attacks on the Running Model and Service
- Excessive Agency Agentic and Autonomous AI
- Overreliance Reliability and Responsible Use
- Hate Speech and Discrimination Harmful Content
- Profanity Harmful Content
- Sexual Content Harmful Content
- Violence / Unsafe Actions Harmful Content
- Controversial Topics Harmful Content
- Illegal Activities Harmful Content
- Self-harm Harmful Content
- Harassment Harmful Content
- Unethical Actions Harmful Content
- Social Engineering Malicious Use for Attacks, Fraud and Disinformation
- Fraud Malicious Use for Attacks, Fraud and Disinformation
- Malicious Software Malicious Use for Attacks, Fraud and Disinformation
- Disinformation Malicious Use for Attacks, Fraud and Disinformation
- Factual Inconsistencies (Hallucinations) Reliability and Responsible Use
- Misalignment Agentic and Autonomous AI
- Agentic AI / Autonomous Agents Agentic and Autonomous AI
- RAG-Specific Attacks (Document Poisoning) Application and Integration Security
- Model Drift & Degradation Reliability and Responsible Use
- Multimodal Attacks Prompt Attacks and Guardrail Evasion
- Model Reconnaissance Attacks on the Running Model and Service
- Middleware Exploits (AI Framework Attacks) Application and Integration Security
- MCP Hijacking (Model Context Protocol) Application and Integration Security
- Side-Channel Attacks (Timing Analysis) Attacks on the Running Model and Service
- Graph-RAG Poisoning (Knowledge Graph Injection) Application and Integration Security
Analysis: combine several frameworks and see the gaps that remain
Threats referencing this framework (8)
Grouped by topic. Every entry leads to the full threat page.
Agentic and Autonomous AI
2 threatsPrivacy and Data Leakage
4 threatsReliability and Responsible Use
1 threatSupply Chain and Provenance
1 threatNo threat matches this input. Reset the filters to see all of them again.
Filter the catalogue by GDPR (General Data Protection Regulation)
Sources used (2)
These exact versions are pinned in the catalogue. Every page states the publisher, the version used, the locations and the referencing threats.
This page does not reproduce the text of the standards. It states the identifier, title and location; the wording itself is in the original document. The mappings are taxonomic and not evidence of compliance.
The references provide guidance on data protection. The specific processing activity still needs to be assessed, including a data protection impact assessment where required.
Further frameworks
The frameworks address different aspects: legal requirements, organisational risk management and technical security. Explore the further perspectives included in the catalogue.
From the framework to the assessment
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Cite this page
For reports, policies or internal documents; the link leads directly to this framework page.
“GDPR (General Data Protection Regulation)” (European Union (EUR-Lex), European Data Protection Board (EDPB)). Mappings in the AI threat catalogue, Versatile AI Risk Assessment, as of July 2026. https://www.versatile-ai-risk-assessment.com/en/wissensbasis/frameworks/gdpr/