Explore how Alteryx integrates Responsible AI practices across its suite of products. Our detailed AI Fact Sheets provide a thorough overview of our AI models, including our transparency, data handling, and accountability measures. The FAQs address common questions about data usage, encryption methods, and user controls. Refer to the legend for explanations of key terms used throughout our AI documentation. Learn more and understand how Alteryx ensures trust and reliability in its AI-driven solutions.
Naming Updates
Some features were renamed. Former names are shown in section headers for clarity.
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?
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 | 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:
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:
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:
What encryption methods are used to protect data at rest and in transit?
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.
This consolidated fact sheet covers the following Auto Insights AI capabilities:
1. AI Assistant for Custom Reports (formerly Magic Reports)
2. AI Suggested Use Cases (formerly Auto Insights Playbooks)
3. AI Export Options for Preset Reports (formerly Magic Documents)
General Background | |||
Descriptions (unique by capability) | AI Assistant for Custom Reports | AI Suggested Use Cases | AI Export Options for Preset Reports |
| Allows customers to quickly translate the findings of their Report into actions by summarising, rephrasing, translating or creating an executive summary. | 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 Report. | Allows customers to quickly translate their findings into a presentation, email, or message to share it with their team and stakeholders. | |
Model details (all three capabilities indicate) | |||
| Is personal data used in the training or operation? | No (Personal data is not used in operation) | ||
| Base Model | Azure OpenAI – GPT-4o (unless specified by customer to BYO) | ||
| Model Type | LLM (Large Language Model) | ||
| Model Customization | No | ||
Third-Party LLM Responsibility | |||
| To the extent that a capability 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 (all three capabilities indicate) | |||
| Model outputs explained | Yes | ||
Human Agency and Oversight (all three capabilities indicate) | |||
| Is the Feature Optional? | Yes | ||
| Human in the Loop? | Yes | ||
Trust and Accountability | |||
Shared answers (all three capabilities indicate) | |||
| Base model trained with customer data? | No | ||
| Training data de-identified? | Yes (managed by the underlying GPT-4o model from Azure OpenAI) | ||
| Data retention? | Prompts and generated content are retained by the model vendor for 30 days. | ||
| 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 | ||
| Data encrypted in transit and at rest? | Yes | ||
Capability-specific answers (where they differ) | AI Assistant for Custom Reports | AI Suggested Use Cases | AI Export Options for Preset Reports |
| Customer data shared with model vendor? | On user request, content and insights displayed in the Custom Report; raw underlying data not included unless row-level displayed. | On user request, only metadata (e.g., column names and representative sample values); raw underlying data is not shared. | On user request, content and insights displayed in the Preset Report; raw underlying data not included unless row-level displayed. |
| Data deletion? | Users can delete generated content; also deleted when the Custom Report is deleted. | Users can delete generated content; also deleted when the user is deleted. | Users can delete generated content; also deleted when the Preset Report is deleted. |
Reliability and Safety (all three capabilities indicate) | |||
| 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 | |||
Shared answers (all three capabilities indicate) | |||
| Bias detection and mitigation in place? | Yes | ||
Capability-specific answers (where they differ) | |||
| Capability (Data sources used to compile prompts) | AI Assistant for Custom Reports | AI Suggested Use Cases | AI Export Options for Preset Reports |
| All content and insights displayed in the Report; raw underlying data not included unless row-level detail displayed (eg, in a table) | User-provided inputs and metadata from uploaded data (e.g., column names and sample values) | User-provided inputs and all content and insights displayed in the Preset Report; raw underlying data not included unless row-level detail displayed. | |
Empower Social Good (all three capabilities indicate) | |||
| Designed for Ethical Use? | Yes | ||
This consolidated FAQ covers:
1. AI Assistant for Custom Reports (formerly Magic Reports)
2. AI Suggested Use Cases (formerly Auto Insights Playbooks)
3. AI Export Options for Preset Reports (formerly Magic Documents)
What is the AI feature, and what is its intended use and purpose?
Shared (all three capabilities indicate)
AI Assistant for Custom Reports (unique)
Alteryx Custom Reports – AI Assistant helps customers quickly translate findings into actions by summarising, rephrasing, translating, or creating an executive summary.
AI Suggested Use Cases (unique)
Supports customers identifying high-value analytics use cases and generating synthetic data, missions and reports tailored to their needs in minutes, helping users see how Auto Insights can help on their data journey.
AI Export Options for Preset Reports (unique)
Allows users to generate a presentation, email, or message based on their Preset Report, and tailor the results using 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?
Shared (all three capabilities indicate)
AI Assistant for Custom Reports (unique)
Content and insights displayed in reports.
AI Suggested Use Cases (unique)
Only metadata (e.g., column names and sample values) or the prompt the customer inputs is shared with the vendor.
AI Export Options for Preset Reports (unique)
Content and insights displayed in the Preset Report are shared with the model developer to generate presentations, emails, or messages.
Can users disable the AI feature(s)?
Shared (all three capabilities indicate)
How is the data processed and stored as it flows through the AI system?
Shared (all three capabilities indicate)
What encryption methods are used to protect data at rest and in transit?
Shared (all three capabilities indicate)
AI Suggested Use Cases and AI Export Options for Preset Reports (details)
What testing and validation are performed throughout the AI model lifecycle?
Shared (all three capabilities indicate)
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?
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.
