AI Fact Sheets and FAQs

General Background
Description	Allows customers to embed requests to their own LLMs directly into their designer workflows, helping to solve advanced data problems like mapping data from one format to another and cleaning up messy categorical data.
Is personal data used in the training or operation of this model?	Any personal data included by the user in prompts will be sent to the selected model provider.
Base Model	Customers bring their own credentials for supported models from supported providers.
Model Type	LLM (Large Language Model)
Model Customization	No
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	Large language model outputs are not inherently explainable
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes
Trust and Accountability
Base model trained with customer data?	No
Training data de-identified?	This is managed by the model provider chosen by the customer and may vary based on which model provider is selected.
Customer data shared with model vendor?	Using the feature sends prompts to the chosen provider. No data is shared if the feature is not used.
Data deletion?	AI Tools does not store any data sent to the LLM or any responses received from the LLM. That data only exists at rest in the user’s workflow on their machine. Users can choose to delete data from their local machine.
Data retention?	Prompts and generated content may be retained by the chosen model provider based on their terms & conditions for a set period of time.
Data processing location?	LLM requests are routed via the Alteryx One control plane to the selected model provider for processing in the region where that provider runs the model. Alteryx does not persist prompts or model responses.
Data storage details?	AI Tools does not store any data sent to the LLM or any responses received from the LLM. That data only exists at rest in the user’s workflow on their machine. LLM connection information and credentials are stored in Alteryx One.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Yes
Guardrails?	LLM Provider safety systems apply. Alteryx supplies prompt-level controls but does not add separate content filtering
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Outputs are non-deterministic by nature. Determinism can be increased by adjusting model settings and prompts. Output consistency is never guaranteed.
Fairness and Inclusivity
Data Sources?	The feature compiles prompts for the LLM using user-provided inputs and metadata from the uploaded data, such as column names and sample values. See ‘Customer data shared with model vendor’ for more details.
Bias detection and mitigation in place?	LLM Provider safety systems apply. There is no native bias scanner in AI tools.
Empower Social Good
Designed for Ethical Use?	Yes

What is the AI feature, and what is its intended use and purpose?

The AI Tools Palette enables customers to connect to a large language model (LLM) directly within a Designer workflow. This allows users to quickly prepare and transform data with advanced capabilities such as auto-mapping different datasets, identifying variations of the same value in a column, and creating streamlined workflows with minimal manual effort

What data does the AI system require?

To produce responses, AI Tools send only the compiled prompts as defined by the user generated prompt templates and input data. This data is used only during the operation of the feature and is not retained for future fine-tuning.

Can users disable the AI features?

Yes, the AI Tools Palette is an optional feature that can be disabled by the Organization Admin. It is provided as a separate installer package, similar to the Alteryx Intelligence Suite, giving customers full control over enabling or disabling its functionality

How is the data processed and stored as it flows through the AI system?
AI Tools do not store any data sent to the LLM or the responses received from the LLM. Data only exists within the user’s workflow on their machine. LLM connection information and credentials are securely stored in Alteryx One. Data processing occurs in the region where the Alteryx One environment is deployed (e.g., US East, EU Central).

What encryption methods are used to protect data at rest and in transit?

• In Transit: Data is encrypted using HTTPS with TLS 1.2/TLS 1.3.
• At Rest: While AI Tools does not store operational data, LLM connection credentials are securely stored in Alteryx One using AES-128-CBC with a per-environment key stored in Google Secret Manager (GSM). Data processed by the customer remains within their workflow environment and is protected according to their security configuration.

What testing and validation are performed throughout the AI model’s lifecycle?

AI Tools are designed to connect customers to their own provisioned AI models. Lifecycle management, testing, and validation of those models are managed by the customer. Alteryx ensures tool’s compatibility and provides thorough testing to ensure the quality and reliability of the integration.

 

For more AI FAQs, please visit the Alteryx Artificial Intelligence FAQ page. View the Alteryx Copilot Architecture and Data Flow Diagrams on our Alteryx Trust Center.

General Background
Description	Allows customers to quickly translate the findings of their Report into actions by summarising, rephrasing, translating or creating an executive summary.
Is personal data used in the training or operation of this model?	Personal data data is not used in the operation of this model.
Base Model	Azure OpenAI – GPT-4o.
Model Type	LLM (Large Language Model)
Model Customization	No
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	Yes
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes
Trust and Accountability
Base model trained with customer data?	No
Training data de-identified?	Yes. Anonymization is managed by the underlying GPT-4o model from Azure OpenAI.
Customer data shared with model vendor?	Yes (content and insights; no raw underlying data unless row-level displayed).
Data deletion?	Yes
Data retention?	Prompts/content retained for 30 days by vendor.
Data processing location?	Data is processed in the region where the Analytics Cloud environment is deployed (e.g., US East, EU Central).
Data storage details?	Same region as Analytics Cloud environment.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Yes
Guardrails?	Yes (e.g., data curation, content filtering, bias mitigation, ethical guidelines, user feedback loops, continuous monitoring, collaborative research etc.).
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Yes
Fairness and Inclusivity
Data Sources?	All content and insights displayed in the Report are shared with the model vendor to facilitate summarization, translation, or rephrasing. Raw underlying data is not included; however, if the user opts to display row-level data within the Report, such as in a table, this data will also be shared with the model vendor.
Bias detection and mitigation in place?	Yes
Empower Social Good
Designed for Ethical Use?	Yes

What is the AI feature, and what is its intended use and purpose?

The AI feature is Alteryx Custom Reports – AI Assistant, powered by Azure OpenAI GPT-4o
Allows customers to quickly translate findings into actions by summarising, rephrasing, translating, or creating an executive summary.

What data does the AI system require?

The system uses:

• Content and insights displayed in reports
• Optionally, row-level data if users choose to display it (e.g., in tables)

Important notes:

• Raw underlying data is not shared by default
• Personal data is not used in training or operation

Can users disable the AI features?

Yes. The feature is optional, meaning users can choose whether or not to use it.

How is the data processed and stored as it flows through the AI system?

Data is sent to the Azure OpenAI model only when the feature is used

Processing occurs in the same region as the Analytics Cloud environment

Data storage also remains in the same regional infrastructure

What encryption methods are used to protect data at rest and in transit?

Data is encrypted both in transit and at rest.

What testing and validation are performed throughout the AI model’s lifecycle?

The system includes multiple Responsible AI and governance practices, such as:

• Data curation and content filtering
• Bias detection and mitigation
• Ethical guidelines
• Continuous monitoring
• User feedback loops

For more AI FAQs, please visit the Alteryx Artificial Intelligence FAQ page. View the Alteryx Copilot Architecture and Data Flow Diagrams on our Alteryx Trust Center.

General Background
Description	Supports customers identifying high value analytics use cases tailored to their specific business, role or problem and creates a synthetic dataset to match the use case to build a proof-of-concept Mission.
Is personal data used in the training or operation of this model?	Personal data data is not used in the operation of this model.
Base Model	Azure OpenAI – GPT-4o.
Model Type	LLM (Large Language Model)
Model Customization	No
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	Yes
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes
Trust and Accountability
Base model trained with customer data?	No
Training data de-identified?	Yes. Anonymization is managed by the underlying GPT-4o model from Azure OpenAI.
Customer data shared with model vendor?	Yes, only metadata such as column names and representative sample values are shared with the model vendor. Raw underlying data is not shared.
Data deletion?	AI Suggested Use Cases for Custom and Preset Reports stores the content generated by the LLM. Users can choose to delete this generated content. Additionally, it is deleted when the user is deleted.
Data retention?	Prompts and generated content are retained by the model vendor for 30 days to detect and mitigate abuse.
Data processing location?	Data is processed in the region where the Analytics Cloud environment is deployed (e.g., US East, EU Central).
Data storage details?	Data is stored within the same regional infrastructure as processing. For more details, refer to the Data Storage and Residency Documentation.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Yes
Guardrails?	Yes (e.g., data curation, content filtering, bias mitigation, ethical guidelines, user feedback loops, continuous monitoring, collaborative research etc.).
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Yes
Fairness and Inclusivity
Data Sources?	The feature compiles prompts for the LLM using user-provided inputs and metadata from the uploaded data, such as column names and sample values. See ‘Customer data shared with model vendor’ for more details.
Bias detection and mitigation in place?	Yes
Empower Social Good
Designed for Ethical Use?	Yes

What is the AI feature, and what is its intended use and purpose?

The feature supports customers identifying high-value analytics use cases and generating synthetic data, missions and reports, tailored to their needs in minutes. Allowing users to easily see how Auto Insights can help them on their data journey.

What data does the AI system require?

Personal data is not used in the operation of these models, and only metadata (e.g., column names and sample values) or the prompt that the customer inputs is shared with the vendor. Raw underlying data is not included. The shared data is only used during the operation and not for future fine tuning. The user does not need to provide anything else.

Can users disable the AI features?
The feature is optional, indicating it can be disabled in Admin Portal by the Organisation Admin only via a toggle. It is split up so that AI Suggested Use Cases for Custom and Preset Reports with your own data and AI Suggested Use Cases for Custom and Preset Reports with synthetic data can be enabled/disabled individually. Meaning customers can have both enable or either just one or the other.

How is the data processed and stored as it flows through the AI system?

• For AI Suggested Use Cases for Custom and Preset Reports with synthetic data: A customer provides details such as their role, company, or business problem. The system composes a prompt based on the customers input. This prompt is sent to Azure’s OpenAI service. The available model processes the prompt. The response is returned to Auto Insights and used to generate and display relevant use cases to the customer. The customer selects a use case from the provided options. Based of the use case selection the system returns available report options along with a synthetic data structure. The customer picks a specific report. The system returns a mission or report based on the customers choice. The prompts and generated content are retained for 30 days for monitoring purposes.
• For AI Suggested Use Cases for Custom and Preset Reports with the customers own data: A customer selects a dataset. The system composes a prompt based on the dataset structure and some randomised samples. This prompt is sent to Azure’s OpenAI service. The available model processes the prompt. The response is returned to Auto Insights and used to generate and display relevant use cases to the customer. The customer selects a use case from the provided options. Auto Insights will then generate a Mission or Report based of the selection made. The prompts and generated content are retained for 30 days for monitoring purposes.

What encryption methods are used to protect data at rest and in transit?

• In Transit: TLS 1.2 / TLS 1.3 over HTTPS
• At Rest: AES256 encryption with differences based on the environment:
  • Azure: Server-Side Encryption with Customer-Managed Keys (SSE-CMK)
  • AACP: Google’s default encryption (AES-256)
  • On-Prem: Configured according to the customer’s preferences

What testing and validation are performed throughout the AI model’s lifecycle?
Logging, auditing, and bias mitigation processes are in place. On our end we have done months and months of manual testing where we entered various scenarios and tested with many different datasets to evaluate the results until we were satisfied with the quality and consistency. Once a new model becomes available, we will do extensive testing again and only if we are satisfied with the quality do we upgrade to the new model.

 

For more AI FAQs, please visit the Alteryx Artificial Intelligence FAQ page. View the Alteryx Copilot Architecture and Data Flow Diagrams on our Alteryx Trust Center.

General Background
Description	Ask Alteryx is an AI-powered workflow assistant that enables users to interact in a conversational way to streamline workflow creation and receive tailored recommendations using Generative AI.
Is personal data used in the training or operation of this model?	No
Base Model	Google Gemini
Model Type	LLM
Model Customization	The model is provided with information about Alteryx Designer as context, including how to configure Alteryx Designer Tools.
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	The model will explain its thinking process and why it made certain recommendations in the chat response sent to the user.
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes, Ask Alteryx will often ask the user for confirmation or feedback before taking any action on the canvas.
Trust and Accountability
Base model trained with customer data?	No
Training data de-identified?	N/A
Customer data shared with model vendor?	Yes, workflow metadata as well as raw chat messages sent by the user are sent to the model vendor for use in preparing the response. If Data Awareness is enabled, Ask Alteryx may access a sample of user data at specific tool anchors only with user permission, and only when necessary to answer a question accurately.
Data deletion?	Ask Alteryx stores conversation message history including sanitized workflow information.  Conversation history is retained for 90 days and then deleted, but users can request it to be deleted on demand. Data is deleted when a user is deleted.
Data retention?	Conversation history is retained for 90 days and then deleted unless the user requests deletion sooner. Prompts are retained by the model vendor for 30 days to detect and mitigate abuse.
Data processing location?	Data is processed in the region where the Analytics Cloud environment is deployed. Ask Alteryx Trials operate in the US1 Alteryx One environment.
Data storage details?	Metadata that helps Ask Alteryx operate is stored in the Alteryx One Control Plane in the environment being used. Customer-provided chat messages and Ask Alteryx responses (conversation history) are stored in the Alteryx One Data Plane associated with the workspace selected by the customer, or in the US1 Alteryx One control plane for Ask Alteryx trials.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Ask Alteryx maintains extensive logging mechanisms to ensure transparency and system integrity, including customer-facing debug information, system logs, and internal service logs.
Guardrails?	Yes
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Yes, although because Generative AI models are nondeterministic, users should expect some variability in response to a given prompt.
Fairness and Inclusivity
Data Sources?	Ask Alteryx creates prompts to send to the underlying model from information such as: the chat message entered by the user, workflow metadata sent by Alteryx One during a chat session, conversation history, information about how to configure tools in Alteryx One, Alteryx Help Documentation, and Alteryx Knowledge Base. If Data Awareness is enabled, data is retrieved from the Designer Browse Everywhere (YXBE) file—a temporary snapshot of data at anchor points after running a workflow. Learn more about Browse Everywhere data.
Bias detection and mitigation in place?	Yes
Empower Social Good
Designed for Ethical Use?	Yes

What is the AI feature, and what is its intended use and purpose?

Ask Alteryx is an AI-powered workflow assistant designed to help you build workflows more efficiently. You can Ask Alteryx questions about Designer or get assistance with adding tools to your workflow in a natural, conversational way. Ask Alteryx uses Generative AI to analyze your current workflow and provide tailored recommendations. It can even add preconfigured tools directly to the canvas. With Ask Alteryx, you can spend less time building workflows and get to actionable insights faster.

 

What data does the AI system require?

To produce responses, Ask Alteryx uses:

• The chat message sent by the user
• Conversation history
• Workflow metadata (e.g., tool configuration, connection details, selected tools, column names, and data types at tool anchors).

This data is used only during the operation of the feature and is not retained for future fine-tuning.

 

Can users disable the AI features?

Ask Alteryx is included with Alteryx One and as part of the Professional and Enterprise Pricing Tiers for free, on an opt-in basis. Ask Alteryx can be disabled by using Custom Roles in the Workspace Admin panel and the Account Admin Panel.

 

How is the data processed and stored as it flows through the AI system?

When a customer initiates a conversation with Ask Alteryx by opening the Ask Alteryx Extension in Alteryx One and starting a trial or connecting it to their Alteryx One Workspace, user and conversation metadata are stored in the Alteryx One Control Plane.

When a chat message is sent:

• The message, conversation history, and workflow metadata are sent to the Ask Alteryx service in Alteryx One.
• Ask Alteryx sanitizes the workflow metadata to remove any sensitive information and generates a series of prompts to send to the underlying model to generate a response.

Messages are then stored in the appropriate Alteryx One Data Plane. For trials, messages are stored in the Alteryx One Control Plane. Conversation message history is deleted under the following conditions:

• After 90 days
• When a user is deleted
• Upon user request.

What encryption methods are used to protect data at rest and in transit?

• In Transit: Data is encrypted using HTTPS with TLS 1.2 or TLS 1.3.
• At Rest: Data is encrypted using AES-256 in both the Alteryx One Control and each Data Plane.

What testing and validation are performed throughout the AI model’s lifecycle?

Logging, auditing, and bias mitigation processes are in place. Alteryx performs extensive manual and automated testing to compare Ask Alteryx chat responses against expected outcomes to ensure quality and consistency. When developing a new Ask Alteryx agent, the same testing process is followed, and the new agent is only released if it meets Alteryx’s quality standards.

 

For more AI FAQs, please visit the Alteryx Artificial Intelligence FAQ page. View the Ask Alteryx Architecture and Data Flow Diagrams on our Alteryx Trust Center.

General Background
Description	Allows customers to quickly translate their findings into a presentation, email, or message to share it with their team and stakeholders.
Is personal data used in the training or operation of this model?	No
Base Model	Azure OpenAI – GPT-4o
Model Type	LLM (Large Language Model)
Model Customization	No
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	Yes
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes
Trust and Accountability
Base model trained with customer data?	No
Training data de-identified?	Yes. Anonymization is managed by the underlying GPT-4o model from Azure OpenAI.
Customer data shared with model vendor?	On the request of the user, all content and insights displayed in the Preset Report are shared with the model vendor to facilitate the creation of the presentation, email, or message. Raw underlying data is not included. However, if the user opts to display row-level data within the Report, such as in a table, this data will also be shared with the model vendor.
Data deletion?	Users can choose to delete this generated content. Additionally, it is deleted when the Preset Report is deleted. All Preset Reports are removed when the user account is removed.
Data retention?	Prompts and generated content are retained by the model vendor for 30 days to detect and mitigate abuse.
Data processing location?	Data is processed in the region where the Analytics Cloud environment is deployed (e.g., US East, EU Central).
Data storage details?	Data is stored within the same regional infrastructure as processing. For more details, refer to the Data Storage and Residency Documentation.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Yes
Guardrails?	Yes (e.g., data curation, content filtering, bias mitigation, ethical guidelines, user feedback loops, continuous monitoring, collaborative research etc.).
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Yes
Fairness and Inclusivity
Data Sources?	The feature compiles prompts for the LLM using user-provided inputs and all content and insights displayed in the Preset Report.
Bias detection and mitigation in place?	Yes
Empower Social Good
Designed for Ethical Use?	Yes

What is the AI feature, and what is its intended use and purpose?

The AI feature allows users to generate a presentation, email, or message based on their Preset Report, and tailor the results based on inputs, such as audience, objective, tone of voice, language, and whether AI-generated business recommendations should be included.

What data does the AI system require?

Content and insights displayed in the Preset Report are shared with the model developer to enable the creation of presentations, emails, or messages. Raw underlying data is not shared. However, if users choose to display row-level data within a report (e.g., in a table), this data will also be shared with the model developer. Shared data is only used to perform the operation of the feature and is not retained for future fine-tuning or other uses.

Can users disable the AI feature?

Yes, the feature is optional and can be disabled by a user’s Organization Admin via a toggle in the Auto Insights Settings page, accessible via the Workspace management (Admin Portal).

How is the data processed and stored as it flows through the AI system?

The system generates a prompt based on the user’s input and the selected Preset Report. This prompt is sent to Azure’s OpenAI service, where the model processes it. The response is returned to Auto Insights and used to display the requested presentation, email, or message to the user, who can choose to further modify it inside or outside of Auto Insights or use it as is. Prompts and generated content are retained for 30 days to monitor for abuse, minimizing risk of harmful use.

What encryption methods are used to protect data at rest and in transit?

• In Transit: TLS 1.2 / TLS 1.3 over HTTPS
• At Rest: AES256 encryption with differences based on the environment:
  • Azure: Server-Side Encryption with Customer-Managed Keys (SSE-CMK)
  • AACP: Google’s default encryption (AES-256)
  • On-Prem: Configured according to the customer’s preferences

What testing and validation are performed throughout the AI model lifecycle? 

Azure OpenAI logging, auditing, and bias mitigation processes are in place. Alteryx has conducted extensive manual testing using various scenarios and many different datasets to evaluate the results until we were satisfied with the quality and consistency of the outputs. Once a new model becomes available, we will conduct extensive testing and will only release the model if we are satisfied with the quality and consistency of the outputs.

General Background
Description	Enable an AI Agent to access the Insights MCP from Google Gemini Enterprise. Customers will be able to ask the agent questions about their datasets and get meaningful responses.
Is personal data used in the training or operation of this model?	Potentially if the customer has personal data in their dataset.
Base Model	gemini-2.5-flash (Google)
Model Type	N/A
Model Customization	N/A
Third-Party LLM Responsibility
To the extent that this product or feature utilizes a third-party LLM, please refer to the respective provider’s documentation for information on their data handling practices. This document describes how Alteryx’s product interacts with and uses the LLM, but the model’s management of data is governed by the third-party provider.
Transparency and Explainability
Model Outputs Explained	Gemini provides the frontend for the agent. The output is worded by the LLM used and includes a link to Auto Insights to allow the user to verify the data.
Human Agency and Oversight
Is the Feature Optional?	Yes
Human in the Loop?	Yes, human makes the requests to the agent.
Trust and Accountability
Base model trained with customer data?	N/A
Training data de-identified?	N/A
Customer data shared with model vendor?	N/A
Data deletion?	N/A
Data retention?	N/A
Data processing location?	US, EU, AU (GCP data centers in those locations depending on where agent is enabled).
Data storage details?	The agent uses a DB to store session data that allows it to maintain context between user interactions.
Data encrypted in transit and at rest?	Yes
Reliability and Safety
Logging and Auditing Mechanisms Available?	Results return a link to the data for the user to audit if they want.
Guardrails?	Access controls, agent can only access datasets the user has access to, in a specific Aleryx One workspace selected by the user.
Impact Assessment Conducted?	Yes
Compliant with Applicable Regulations?	Yes
Input/Output Consistency?	Yes
Fairness and Inclusivity
Data Sources?	N/A
Bias detection and mitigation in place?	N/A
Empower Social Good
Designed for Ethical Use?	N/A

What is the AI feature, and what is its intended use and purpose?

This is an AI Agent that accesses the Insights MCP to retrieve information about a customer’s datasets.

What data does the AI system require?

Insights dataset.

Can users disable the AI feature?

Yes.

How is the data processed and stored as it flows through the AI system?

Gemini Enterprise provides the user interface and sends messages to the AI Agent, which resides on Alteryx One. The agent sents queries to the MCP server to retrieve data about the dataset, formulates a response to the user’s message and returns it to the Gemini Enterprise.

What encryption methods are used to protect data at rest and in transit?

• In transit: Traffic between external clients and our services is encrypted using TLS 1.2+. Within the control plane Kubernetes clusters, inter-service traffic is protected by Linkerd mutual TLS over TLS 1.3, using Linkerd’s default AEAD cipher suites.
• At rest: Encryption is in Place – Tested as a part of our ISO 27001 and SOC 2 audits.

What testing and validation are performed throughout the AI model lifecycle? 

The agent translates user messages into queries againts Aleryx Auto Insights and re-words the responses into user friendly language. The responses are manually verified against the results visible in Auto Insights’ user interface.