65 lines
1.6 KiB
Plaintext
65 lines
1.6 KiB
Plaintext
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---
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@agent {
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role: data privacy consultant,
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llm: llama2,
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max_tokens: 2048,
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temperature: 0.7
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}
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@input data_type: String
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@input use_case: String
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@import ".env" as env_file
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@shell "ls -la *.json *.yaml *.toml 2>/dev/null | head -10" as config_files
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---
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# Privacy-First Data Analysis
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## Scenario
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You need to analyze {{data_type}} for {{use_case}}.
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## Environment Configuration
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{{env_file}}
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## Configuration Files Found
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{{config_files}}
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---
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## Why Ollama for Sensitive Data?
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This agent uses **Ollama** (llama2) which runs entirely locally on your machine:
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- ✅ No data sent to external APIs
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- ✅ Complete privacy - data never leaves your computer
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- ✅ No API costs
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- ✅ Offline operation
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- ✅ Full control over model execution
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## Task
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Analyze the following aspects of handling {{data_type}}:
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1. **Privacy Requirements**
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- What privacy regulations apply? (GDPR, CCPA, HIPAA, etc.)
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- What data classification level is this?
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- What consent mechanisms are needed?
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2. **Security Recommendations**
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- How should this data be encrypted at rest and in transit?
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- What access controls should be implemented?
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- What audit logging is required?
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3. **Processing Guidelines**
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- Can this data be processed in the cloud?
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- Should it remain on-premises?
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- What data minimization strategies apply?
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4. **Compliance Checklist**
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- What documentation is required?
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- What rights do data subjects have?
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- What breach notification procedures apply?
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Provide specific, actionable recommendations for {{use_case}}.
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**Note:** Since this analysis runs locally, you can safely include sensitive context in your prompts!
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