Automated Data Tagging

Streamline your investigative, analytic, and response workflows.

Introduction

How to use Automated Data Tagging in DigitalStakeout Scout, a feature that leverages complex filtering, Intelligent Discovery, and entity filtering to streamline investigative, analytic, and response workflows.

Efficient Data Management with Automated Tagging

Step 1: Understanding Automated Tagging

Automated Data Tagging in DigitalStakeout Scout uses complex filtering mechanisms to categorize vast amounts of data efficiently, significantly reducing the manual effort in data classification.

Step 2: Utilizing Intelligent Discovery

  • Navigate to Intelligent Discovery: This feature automatically categorizes data into various risk and exposure categories.
  • Identify Key Categories: Focus on categories like Cyber Exposure, Physical Exposure, and more, to quickly identify potential threats.

Step 3: Implementing Entity Filtering

  • Apply Entity Filters: Refine data tagging by focusing on specific data entities, such as locations, names, or organizations.
  • Customize Filters: Adapt filters to target the most relevant data.

Creating and Managing Automation Rules

Creating Custom Rules

  • Access Rule Settings: Create rules to automate actions for specific data patterns.
  • Customize Rule Conditions: Set up conditions based on data patterns, risk levels, and desired actions.

Managing Rule Configuration

  • Configure Rule Details: Define the rule’s name, description, and notification settings.
  • Set Risk Levels: Assign a risk level to each rule for prioritized monitoring.

Tagging Actions

  • Automate Actions: Choose the action "Apply a System Tag" or "Apply User Defined Tag" when a rule is triggered.
  • Select Applicable Tag: Select the applicable tag for your use case or workflow.

Benefits of Automated Data Tagging

  • Streamlined Workflow: Focus on high-value data and reduce manual data sorting.
  • Rapid Response: Quickly identify and respond to critical data patterns.
  • Customization and Flexibility: Tailor categories and filters to meet specific analytical needs.

Conclusion

Automated Data Tagging in DigitalStakeout Scout is an invaluable tool for enhancing data analysis efficiency. By automating the categorization process and focusing on relevant data, users can achieve more accurate insights and quicker responses, leading to better-informed decisions and a robust security posture.