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Datenschutz & Governance

Datenschutz & Governance

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PrivacyRiskGovernance
Globale Netzwerkkarte in einem leeren Besprechungsraum
KI-generierte redaktionelle Illustration

In diesem Thema

Personal data · Risk management · AI governance · Compliance · Sovereignty

Praxisleitfäden

Map data flows and responsibilities

How do you build an actionable data map? Follow one processing activity from collection through copies, exports, backups and deletion. The register describes purposes, data categories, applications, providers, access, retention, owners and control evidence. The aim is to explain a decision, handle a request and verify a change. Examples concern methodology; legal classification depends on context and should be checked against original sources.

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Gespeicherte Schlagzeilen · Aktualisierung nicht verfügbar · Letzter erfolgreicher Abruf : 22.10.2026, 21:23

Referenzen: Daten, Nutzung und Grenzen

Dokumentation geprüft am .

Ausführliche Hinweise stehen in dieser Ausgabe auf Englisch zur Verfügung.

Referenzen: Daten, Nutzung und Grenzen
Quelle und ZweckGeprüfte Daten oder RichtwertePraktische NutzungZu beachtende Grenze
FIRST · Traffic Light Protocol

Indicate who may receive redistributed information.

TLP 2.0 uses RED, AMBER, GREEN and CLEAR; AMBER+STRICT restricts sharing to the recipient organisation. Labels must not be translated. Keep the marking with the information and check recipients before forwarding. Ask the source to clarify any ambiguity.

Methode vertiefen →

TLP is neither a formal classification nor a reuse licence. It does not guarantee content accuracy.
NIST · AI Risk Management Framework

Organise management of AI-related risks.

The AI RMF Core is organised around Govern, Map, Measure and Manage. It is a voluntary risk-management framework. Define intended use, affected people and error consequences. Connect each risk to an evaluation, owner and stop or recovery mechanism.

Methode vertiefen →

The framework certifies neither a model nor an application. Using it does not remove the need to examine applicable requirements.
NIST · Privacy Framework

Examine risks to people arising from data processing.

The Privacy Framework is a voluntary privacy risk-management tool. It is distinct from a universal legal compliance checklist. Describe actual processing: data, purposes, recipients, access and retention. Compare documents, settings and deletion or retrieval tests.

Methode vertiefen →

Using the framework alone does not determine compliance with applicable law. Processing context and jurisdiction still need examination.

Veröffentlichung und Sichtbarkeit in der KI-Suche

Ausführliche Hinweise stehen in dieser Ausgabe auf Englisch zur Verfügung.

Assign publication decisions to an owner

Public publication and private access require different evidence. Snippet and AI participation choices must be linked to the relevant service and property.

  • Record owner, scope, effective choice and inherited publication settings.
  • Protect private resources through authorisation; robots.txt publicly reveals listed paths.
  • Date acceptance evidence and review documentation again when the service changes.

Compare AI controls and report data →

References reviewed

security.txt und Offenlegung

Ausführliche Hinweise stehen in dieser Ausgabe auf Englisch zur Verfügung.

Preserve contact scope when domains change

A migration, provider delegation or subdomain change can make a disclosure channel ambiguous. Domain governance should therefore include publication and review of security.txt.

  • Check each relevant host: a parent domain’s file does not automatically apply to its subdomains.
  • Compare Canonical URIs with actual retrieval and document provider redirects.
  • Review contacts, keys, policy and expiry when responsibility transfers; preserve older versions as history.

Read the security.txt reference and disclosure cases →

Sources checked on · IETF · RFC 9116 · §2.5, §3, §5