Data retention policy
We retain customer data for as long as needed to provide the service, bill for it, and authenticate users, and to meet legal obligations (Privacy Policy, Articles 4 and 10). When a service agreement ends, identifiable personal data (such as names and email addresses) is deleted or anonymized within 90 days, after the data-export period set out in our Terms. De-identified behavioral history, statistics and metadata that cannot identify a person may be retained after termination to improve the service. When you disconnect Slack from DynaMeet, the Slack access token is revoked and the stored Slack connection is deleted.
Data archiving and removal policy
Users can request the deletion, correction, or suspension of their data at any time by contacting us at info@dynameet.ai, as provided in Articles 11 and 15 of our Privacy Policy. Upon receiving a valid request, we verify the user's identity and handle the request without delay in accordance with applicable law, including the Act on the Protection of Personal Information (APPI). For data regarding end users (site visitors), we act as a processor and refer requests to the relevant customer, who acts as the data controller (Privacy Policy, Article 1).
Data storage policy
Customer data is stored in Japan, in the Amazon Web Services Tokyo region (ap-northeast-1), as set out in Article 8 of our Privacy Policy. We use the infrastructure providers listed in Article 6 (Amazon Web Services and Supabase) under contracts that require appropriate security measures; the current list of sub-processors is published at dynameet.ai/en/legal/sub-processors/. We operate an information security management system certified to ISO/IEC 27001 and ISO/IEC 27017 and apply technical and organizational measures against loss, destruction, alteration and leakage of data (Article 9). All data in transit is encrypted with TLS, and Slack OAuth tokens are encrypted at rest with AES-256-GCM.
Data hosting details
Cloud hosted
App/service has sub-processors
yes
Guidelines for sub-processors
App/service uses large language models (LLM)
yes
LLM retention settings
The LLM provider is configured to not retain customer data for model training. Inputs and outputs are processed ephemerally for the sole purpose of generating the response and are not stored by the provider to improve their general AI models.
LLM data tenancy policy
The LLM uses strict multi-tenant logical isolation. Customer data is processed within a secure boundary and is never accessed by other customers or used to train shared foundation models.
LLM data residency policy
Data is processed within Google Cloud's secure global infrastructure. Data is encrypted in transit and at rest, and processing occurs within the provider's compliance boundary.