FriendlyData VS Polisis

• Ease of Use
  FriendlyData allows users to interact with databases using natural language queries, which makes it accessible for non-technical users who are not familiar with complex database query languages.
• Time-Saving
  By enabling natural language processing, FriendlyData reduces the time it takes for users to get the information they need, facilitating quicker decision-making processes.
• Integration
  FriendlyData can be integrated with various platforms and services, making it versatile for different business needs.

Possible disadvantages

• Complex Queries Limitation
  While FriendlyData is useful for simple queries, it may struggle to accurately interpret and execute more complex queries, limiting its effectiveness for advanced data analysis.
• Language Ambiguity
  Natural language processing might not always accurately interpret user queries, especially if the input is ambiguous or lacks specificity.
• Dependency on Vendor
  Relying on third-party services like FriendlyData introduces dependency on the vendor for maintenance, updates, and support, which could pose risks if the service changes or is discontinued.

• Automated Privacy Policy Analysis
  Polisis uses machine learning to automatically analyze privacy policies, providing users with a quick overview without the need to read the entire document.
• Visualization
  The tool offers a user-friendly visual representation of privacy policy contents, making it easier to understand the main points and data practices.
• Data Extraction
  Polisis can extract key information from privacy policies, such as what data is collected, how it is used, and with whom it is shared, aiding in better privacy decision-making.
• Browser Extension
  It provides a browser extension, allowing users to analyze privacy policies directly from their web browser, enhancing accessibility and convenience.

Possible disadvantages

• Accuracy Limitations
  As with any automated tool, Polisis may not always perfectly analyze complex and nuanced texts inherent in privacy policies, leading to potential misunderstandings.
• Dependency on Machine Learning Models
  The effectiveness of Polisis relies heavily on the quality of its machine learning models, which might not capture all subtleties of legal language.
• Limited Language Support
  At present, Polisis might support a limited number of languages, restricting its usability for non-English policies.
• Privacy Concerns
  Users might have privacy concerns about feeding sensitive privacy policy data into the system, given it processes and analyses data online.

Tracking FriendlyData since Mar 2021.

• Meet ‘CookieEnforcer’: A Machine Learning-Based Proposed Browser Extension That Rejects Optional Cookies in Your Browser Automatically

  I am a researcher from another team, so a competitor to this publication. I can vouch for Hamza Harkous, his research on privacy policies or later on privacy settings were great contributions to the community. He is a competent... Source: over 4 years ago

• A guide on how to read privacy policies quickly and effectively

  This is a service that I found that reads privacy policies and kind of tell you what they use each thing for. https://pribot.org/polisis. Source: over 5 years ago