Slack Enterprise_ Bot & Block Kit Message Collection

Slack Block Kit Overview

Slack Block Kit is Slack's UI framework for composing structured, richly formatted messages. Unlike plain text messages sent by users, Block Kit messages are built from a series of "blocks" — individual components that define layout, meaning, and content hierarchy.

Block Kit is the standard format used by:

Bots and automated workflows — task notifications, reminders, pipeline alerts
App integrations — summaries from tools like Jira, PagerDuty, Salesforce, or GitHub
System notifications — Slack-native alerts and status messages
AI chatbot experiences — responses from Slack's built-in AI features and third-party AI apps operating inside Slack

Block Kit messages are no longer edge cases in corporate Slack environments. They represent a growing share of machine-generated and AI-assisted communication that may be relevant to legal review, investigations, or compliance matters.

For a full technical reference of Slack Block Kit, see Slack's Block Kit documentation.

Why This Matters

Prior to this capability, Block Kit messages in Onna could be inconsistently represented — rendered as flattened plain text, or with structural meaning lost. This created risk for:

Legal review accuracy — bot responses and AI conversations may have lost their intended structure and context
Search relevance — key terms embedded in structured fields or context blocks could be missed
Understanding AI/bot intent — the distinction between a question asked and an answer given by an AI chatbot was not always preserved

Onna now captures Block Kit messages with their structure intact, ensuring that bot-generated and AI-driven Slack content is as reviewable and searchable as any other message type.

Supported Block Types

Onna captures messages built with Slack's Block Kit framework, preserving the structure and content of each block. For a full list of block types, see Slack's Block Kit block reference.

Supported block types include:

Section
Context
Divider
Image
Actions
Header
Rich Text

Note
Onna is designed to handle unknown or future block types gracefully. If a new block type is introduced by Slack that Onna does not yet fully support, the available text content from that block will still be captured and indexed where possible.

Text Formatting

Block Kit supports Slack's markdown-like formatting syntax (called mrkdwn) as well as plain text. Within Section and Rich Text blocks, Onna captures and preserves:

Bold (*text*) and italic (_text_) formatting
Hyperlinks and URL references
Ordered and unordered lists
Inline code snippets
Structured key-value field pairs (e.g., Priority: High | Status: Open)

In addition to the structured representation, Onna derives a plain-text version of each Block Kit message for full-text indexing and export compatibility.

How Interactive Elements Are Handled

Block Kit messages frequently include interactive components — buttons users can click, dropdown menus, or approval prompts. These are common in workflow automation, incident response tools, and AI chatbot interactions.

Within Onna, interactive elements are captured but not executed. This means:

Button labels, dropdown option text, and action descriptions are collected and searchable
Associated URLs or metadata linked to an action are preserved where available
No action can be triggered from within the Onna interface

This approach ensures the full intent and context of a bot or AI message is preserved for review without introducing any risk of unintended interactions.

Message Source Identification

Onna distinguishes between different types of message senders within collected Slack data:

User messages — messages authored by a human Slack user
Bot/app messages — messages posted by a Slack bot or integrated application using Block Kit
AI chatbot responses — messages generated by Slack AI or third-party AI apps operating within Slack

This distinction is reflected in the message metadata available within Onna and is preserved in exports, supporting accurate custodian attribution and source identification during review.

Common Use Cases

The following are examples of Block Kit content that Onna will collect and make available for search, review, and export:

AI chatbot conversations — exchanges between users and Slack AI or third-party AI assistants, including the structured responses generated by those tools
Workflow and automation notifications — messages triggered by tools such as Jira, PagerDuty, GitHub, or Workato, delivered as structured Block Kit cards
Incident and alert notifications — system-generated alerts with status fields, severity levels, and action buttons
Approval and feedback prompts — survey-style messages or approval flows with selectable options or structured response fields
App-generated summaries and dashboards — digest messages or reporting summaries posted by integrated business applications

Considerations

Search

Block Kit message content — including section text, field labels and values, context text, image alt text, and action button labels — is fully indexed for full-text search within Onna.

Rendering

Block Kit messages are rendered in Onna in a structured format that preserves the hierarchy and ordering of blocks as they appeared in Slack. The goal is to faithfully represent the message as a reviewer would have seen it in the Slack interface.

Export

When exporting Slack data that includes Block Kit messages, both a structured representation and a derived plain-text version are available. This ensures compatibility with downstream review platforms that expect plain text, while preserving the full structured version for reference.

Unknown Block Types

Slack continues to introduce new block types as the platform evolves, particularly around AI features. Onna is built to handle unrecognized block types gracefully — extracting and indexing any available text content even when full structured rendering is not yet supported for a given block type.

Note
Block Kit message collection applies to the Slack Enterprise connector. For questions about which connector is right for your organization, see Getting Started with Sources.