Davis CoPilot data privacy and security

At Dynatrace, we take our responsibility to safeguard your data seriously. Understand how Davis CoPilot uses your data and understand your responsibility to keep your data secure.

Prompt data

Do not share personal or confidential information in your prompts. There is currently no masking of customer data or personal data entered in the prompt, however updates to this effect will be released.

Your prompts are sent to a third-party vendor, Microsoft Azure OpenAI Service, which provides the LLM behind Davis CoPilot. Microsoft Azure OpenAI Service does not store the data you submit or the responses you receive. The prompts you submit and the responses you receive are used only to serve your experience. Microsoft Azure OpenAI Service also does not use the prompts to fine-tune or improve any models or services, or to train models across customers or environments.

Each data request is sent to Azure OpenAI individually, over an SSL-encrypted service, processed by Azure, and sent back to Dynatrace. If your environment is located in EMEA, your prompts are processed in an EU region. If your environment is located in NORAM, LATAM, or APAC, your prompts are processed in a US region.

Dynatrace may store the prompts submitted to Davis CoPilot and the responses provided by the LLMs to understand the use cases, contextualize the feedback on the responses, and identify additional user expectations.

Personal Identifiable Information masking

Dynatrace version 1.305+

Starting with Dynatrace version 1.305, Personal Identifiable Information masking is in place for user prompts. This ensures that sensitive information included in your prompts for Davis CoPilot won't be forwarded to Microsoft AzureAI and won't be stored within Dynatrace systems.

Currently masked fields include:

  • Email address
  • Phone number
  • IBAN information
  • Credit card number
  • IP address
  • US bank number
  • US driver license
  • Passport number
  • US social security number
  • US taxpay identification number (ITIN)
  • A general fallback for groups of numbers and separators that can represent ID numbers

In our logs and calls to LLM models, we replace values from the identified patterns above with fake patterns. This means that you'll be able see IBANs in logs, for example, but they'll be made up of random numbers, replacing the original values included in your prompts.