Task Overview

Objective:
Develop an AI-powered OWASP NestBot Slack assistant that acts as an auto-responder for frequently asked questions, guides users to the appropriate OWASP channels, and handles typical OWASP community queries. The bot will integrate with Slack APIs, leverage machine learning to understand context and intent, and be gradually deployed for production use.

Scope:

• Respond to questions similar to those seen in our Slack history:
  • https://owasp.slack.com/archives/C04T40NND/p1739967605804329
  • https://owasp.slack.com/archives/C04T40NND/p1739853253068169
  • https://owasp.slack.com/archives/C04T40NND/p1739359272447139
• Redirect specific questions to appropriate channels.
• Answer general OWASP community questions.
• Exclude non-OWASP related queries.

1. Slack API Integration

Goals:

• Seamlessly connect NestBot with Slack to fetch content and interact with users.
• Implement Slack commands to trigger bot actions and provide help.

Key Tasks:

• User Interaction:

  • Implement interactive messages and dialog components to facilitate user feedback.
  • Enable command handling (e.g., /owasp help, /owasp ask <query>).
  • Enable channel message triggers
  • Consider adding thumbs up / thumbs down reactions handling to understand user feedback

• Content Fetching & Routing:

  • Use Slack APIs to fetch historical messages and channel content for training data.
  • Enable the bot to post messages and channel redirects based on message content.

2. ML Model Training and Integration

Goals:

• Train an ML model on content sourced from specific Slack channels to classify and route queries.
• Integrate the trained model into OWASP NestBot for real-time decision-making.

Key Tasks:

• Data Collection:

  • Gather historical data from relevant OWASP Slack channels (source: provided links and additional channels).
  • Annotate training data to identify typical questions and their corresponding target channels.

• Model Training:

  • Choose an appropriate NLP model or framework (e.g., using transformer-based models or traditional classifiers).
  • Train the model to classify questions as either "channel-specific" or "general OWASP community" inquiries.
  • Develop a routing mechanism to suggest appropriate channels for offtopic questions.

• Integration:

  • Embed the trained model within the NestBot codebase.
  • Set up an API endpoint (internal to NestBot) to query the model and receive routing recommendations.

3. Typical Scenarios and Coverage

Goals:

• Identify and plan for typical scenarios the bot must handle.
• Ensure comprehensive coverage for both frequently asked and edge-case queries.

Key Tasks:

• Scenario Definition:

  • Document common question types (e.g., "How do I contribute?", "Where can I find X?", "Who do I contact for Y?").
  • Define expected responses and channel redirects for each scenario.

• Edge Cases:

  • Identify potential ambiguities or off-topic queries.
  • Design fallback mechanisms (e.g., returning a "Please rephrase" message or routing to a general FAQ channel).

• Documentation:

  • Create a scenario matrix mapping question types to desired actions.
  • Maintain a living document to update scenarios as new types of questions emerge.

4. End Product Testing and Acceptance Criteria

Goals:

• Ensure the final NestBot implementation is robust, accurate, and user-friendly.
• Define clear criteria for testing and acceptance.

Key Tasks:

• Unit and Integration Testing:

  • Develop tests for Slack API interactions (simulated events, command responses).
  • Test the ML model integration with sample queries and verify correct routing.

• User Acceptance Testing (UAT):

  • Pilot the bot within a controlled internal Slack channel.
  • Collect feedback from community members and mentors on accuracy and usability.

• Acceptance Criteria:

  • Bot responds accurately to a defined set of test queries.
  • Correctly identifies and routes questions to appropriate channels.
  • Handles unexpected queries gracefully with a fallback response.
  • Demonstrates seamless Slack integration without noticeable delays.

5. Gradual Production Deployment

Goals:

• Roll out NestBot gradually to ensure stability and gather community feedback.
• Initially deploy in a controlled environment before full production in #owasp-community.

Key Tasks:

• Staged Deployment:

  • Start with an internal testing channel (e.g., #project-nest-bot-testing).
  • Monitor bot performance and user feedback closely.

• Incremental Rollout:

  • Once validated internally, deploy the bot to a broader set of channels (e.g., OWASP-specific channels).
  • Finally, enable the bot in the #owasp-community channel with an announcement and detailed usage instructions.

• Monitoring and Maintenance:

  • Set up logging and monitoring to track bot interactions, errors, and performance metrics.
  • Prepare a rollback plan and continuous improvement cycle based on real-world usage feedback.