Generative Development Framework
GDF.ai
  • Intro to GDF-FSE
    • Generative AI, Large Language Models, ChatGPT?
    • Knowledge Areas
    • Access a Chat Based LLM
    • Why GDF?
    • Expectations
  • Limitations
  • Prompting
    • Prompt Patterns
    • Prompt Context
    • Prompt Stores
    • Prompt Operators
    • Prompt Chaining
  • Security
    • Protecting Data
    • Protecting Application Security
    • Protecting Intellectual Property
    • Protection Stores
    • AI Security Assessments and Penetration Testing
    • Social Engineering Testing with AI
  • Subject Knowledge Areas
    • Ideation
      • Identifying a Problem Statement
      • Plan and Prioritize Features
      • Develop User Stories
      • Requirement Gathering
      • Ideation Prompting
      • Ideation Template
    • Specification
      • Specifying Languages
      • Specifying Libraries
      • Specifying Project Structures
      • Specify Schemas
      • Specifying Elements
      • Specifying API Specs
    • Generation
      • Generating UI Elements
      • Generating Mock Data
      • Generating Schemas
      • Generating Parsers
      • Generating Databases
      • Generate Functions
      • Generate APIs
      • Generate Diagrams
      • Generating Documentation
    • Transformation
      • Converting Languages
      • Converting Libraries
    • Replacement
      • Replacing Functions
      • Replacing Data Types
    • Integration
      • Connecting UI Components
      • Connecting UI to Backend
      • Connecting Multiple Services Together
      • Connecting Cloud Infrastructure (AWS)
    • Separation
      • Abstraction
      • Model View Controller (MVC)
    • Consolidation
      • Combining UI Elements
      • Deduplicating Code Fragments
    • Templating
      • Layouts
      • Schemas
      • Project Structures
      • Content Management Systems
    • Visualization
      • General Styling
      • Visual Referencing
      • Visual Variations
    • Verification
      • Test Classes
      • Logging and Monitoring
      • Automated Testing
      • Synthetic Monitoring
    • Implementation
      • Infrastructure
      • DevOps / Deployment
    • Optimization
      • General Optimization
      • Performance Monitoring
      • Code Review
  • Guidance
    • Business Process
    • Regulatory Guidance
  • Generative Pipelines
  • Troubleshooting
    • Client Side Troubleshooting
    • Server Side Troubleshooting
    • Troubleshooting with AI
    • Documentation
    • Infrastructure Engineering
  • Terminology
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  1. Security

Protecting Intellectual Property

Considerations for protecting your intellectual property while using generative AI

PreviousProtecting Application SecurityNextProtection Stores

Last updated 3 months ago

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With large language models and generative redefining the value of work, intellectual property will become an increasing valuable asset that must be protected. When sending prompts containing sensitive information or intellectual property, it is essential to ensure that your data remains secure and confidential. Here are some steps you can take to protect your IP when interacting with AI models:

  1. Anonymize your data: Before sending any sensitive information to the AI model, remove or obfuscate any personally identifiable information (PII) or confidential data. We discuss this in detail in.

  2. Use secure connections: Ensure that your connection to the AI model's API is encrypted and secure. Use HTTPS and SSL/TLS to transmit data between your application and the AI service. This will help prevent unauthorized access to your data during transit.

  3. Limit data retention: Check the data retention policies of the AI service provider. Ensure that they have a reasonable data retention period and that they follow proper data deletion practices. If possible, use a provider that allows you to configure data retention settings according to your needs.

  4. Review terms of service and privacy policies: Carefully read the terms of service and privacy policies of the AI service provider. Ensure that they do not claim any ownership of the data you send and that they have proper security measures in place to protect your data.

  5. Monitor usage and access: Keep track of who has access to the AI model within your organization. Limit access to only those who require it and regularly review the usage logs to identify any suspicious activity.

  6. Contractual agreements: Establish clear contractual agreements with the AI service provider that outline the ownership of intellectual property, data protection requirements, and the responsibilities of both parties.

  7. Use on-premises or private cloud solutions: If available and feasible, consider using on-premises or private cloud solutions for AI processing. This will give you more control over the storage and processing of your data, as well as the security measures in place.

  8. Stay informed and adapt: As technology evolves, so do the threats and risks associated with it. Keep yourself informed about the latest developments in AI and data protection and update your strategies accordingly.

Unlike data, intellectual property is much more difficult to anonymize. When using generative AI, you should always be mindful and ask yourself what someone could infer by reading through your prompt history. By keeping this mindset and taking the precautions above, about the security of your intellectual property, you can minimize the risks associated with using large language models while still benefiting from their capabilities.

Protecting Data