AI Technology for GDPR Compliance

Daisy employs specialized AI technology designed specifically for European data protection requirements. As a Data Protection Officer, you need solutions that understand the unique complexities of GDPR, EDPB guidelines, and related European privacy frameworks.

AI Technology for Data Protection Professionals

Our AI systems are purpose-built for DPOs and privacy professionals working under European regulatory frameworks:

How Our AI Supports DPO Workflows

GDPR-Compliant Document Generation

Our AI creates documentation that meets supervisory authority expectations:

• Article-Specific Compliance: Documents adhere to specific GDPR article requirements
• Member State Variations: Accounts for country-specific implementations of GDPR
• Regulatory Currency: Incorporates the latest EDPB guidelines and court decisions
• Appropriate Formality: Uses suitable language for regulatory audiences
• Authority-Ready Format: Creates documentation that satisfies supervisory authority requirements

European Privacy Law Research

When faced with complex GDPR questions, our AI:

• Multi-Jurisdictional Analysis: Examines interpretations across different EU member states
• Contextual Understanding: Recognizes the nuanced European approach to privacy rights
• Supervisory Authority Focus: Prioritizes guidance from relevant data protection authorities
• Identifies Legal Grounds: Helps establish appropriate legal bases for processing
• Drafts Remediation Plans: Suggests compliance measures for identified gaps

DPO Decision Support

Support for your most critical compliance decisions:

• DPIA Necessity Assessment: Helps determine when Article 35 DPIAs are required
• Risk Evaluation: Assists in assessing data protection risks to data subjects
• Breach Notification Analysis: Supports Article 33/34 decision-making processes
• Cross-Border Transfer Analysis: Evaluates compliance with Chapter V requirements
• Processor Due Diligence: Assists with Article 28 compliance verification

Regulatory Monitoring

Stay updated with changing European privacy requirements:

• EDPB Alert System: Tracks new guidelines from the European Data Protection Board
• Court Decision Analysis: Monitors impactful CJEU and national court decisions
• Compliance Timeline Planning: Helps prepare for upcoming regulatory changes
• Impact Assessment: Evaluates how changes affect your existing documentation

Technical Safeguards for DPOs

As a Data Protection Officer, you need assurance about AI usage:

• Data Processing Limitation: Our AI only processes the minimum data necessary
• Purpose Limitation: AI usage is strictly limited to specified compliance purposes
• Algorithmic Transparency: Clear documentation of how AI generates compliance documents
• Human Oversight: Critical compliance decisions always include human review
• Secure Processing: All AI operations comply with Article 32 security requirements

Specialized AI Models for European Privacy

Our AI technology includes specialized models for different aspects of GDPR:

• Article 35 DPIA Model: Specialized in risk assessment methodology
• Article 30 RoPA Model: Designed for comprehensive processing records
• Articles 13/14 Transparency Model: Focused on clear information provision
• Article 28 Processor Model: Specialized in contract requirements
• Articles 44-50 Transfer Model: Expert in cross-border data transfer mechanisms

Technical Details (For Those Interested)

Behind the scenes, Daisy uses a combination of:

• Large Language Models: Similar to GPT-4 and other advanced AI systems
• Vector Databases: For efficient storage and retrieval of legal knowledge
• Fine-tuning: Models specifically trained on legal documents and regulations
• Prompt Engineering: Carefully crafted instructions that guide the AI

No AI Expertise Required

The best part? You don't need to understand any of this to use Daisy. Our API handles all the complexity, so you can focus on your business while we take care of the legal documents.

Getting Started

Ready to see our AI in action? Try generating your first document:

// Generate a DPIA document
const response = await daisyClient.legalDocuments.create({
  documentType: 'DPIA',
  companyName: 'Your Company',
  processingActivities: [
    {
      activityName: 'Customer Data Collection',
      dataTypes: ['email', 'name', 'address'],
      processingPurpose: 'Order fulfillment and delivery',
    },
  ],
});

// The response includes your generated document
console.log(response.document);

Check out our API Reference for more examples and detailed documentation.

• Purpose: Condenses large text into concise markdown format
• Use Case: Content summarization and processing

Question Generator

• Model: DeepSeek-v3
• Purpose: Generates relevant questions from content
• Use Case:
  • Content understanding validation
  • Research expansion
  • Report generation

Meta Prompt Processor

• Model: DeepSeek-v3
• Configuration:

  {
    model: 'accounts/fireworks/models/deepseek-v3-0324',
    temperature: 0.3,
    maxTokens: 80000,
    metadata: {
      pricePerToken: 0.0000009
    }
  }

• Purpose: Breaks down complex prompts into manageable tasks
• Use Case: Handling large or complex prompting tasks

Report Writer

• Model: DeepSeek-R1
• Configuration:

  {
    model: 'accounts/fireworks/models/deepseek-r1',
    temperature: 0.6,
    maxTokens: 80000,
    metadata: {
      pricePerToken: 0.000008
    }
  }

• Purpose: Generates reports from given context
• Use Case: Automated report generation

Translator

• Model: Gemini Pro
• Purpose: Translates text between languages
• Use Case: Multilingual content processing

ReAct Agent Creation

The module provides functionality to create ReAct (Reasoning and Acting) agents that can use tools to accomplish tasks.

Basic Usage

import { ChatModelsService } from './chat-models.service';

@Injectable()
class YourService {
  constructor(private chatModels: ChatModelsService) {}

  async createAgent() {
    const agent = this.chatModels.createReactAgent({
      name: 'research-agent',
      tools: [
        /* your tools */
      ],
      prompt: 'Custom prompt for the agent',
    });

    return agent;
  }
}

Agent Configuration

interface ICreateReactAgentParams {
  tools: DynamicStructuredTool[];
  name: string;
  llm?: BaseChatModel; // Optional, defaults to DeepSeek-v3
  checkpointer?: Checkpointer;
  store?: Store;
  prompt?: string;
}

Tool Description Generation

The service provides utilities for generating tool descriptions:

// Get descriptions for tools
const toolDesc = chatModels.getToolDescriptions(tools);
// Result:
// - tool_name: Tool Description
// - another_tool: Another Tool Description

// Get descriptions for multiple agents and their tools
const agentDesc = chatModels.getAgentDescriptions(agents);
// Result:
// agent_name:
// - tool_name: Tool Description
// - another_tool: Another Tool Description
//
// another_agent_name:
// - third_tool: Tool Description

Best Practices

Model Selection

1. Task-Specific Models

   • Use specialized models for their intended purposes
   • Consider token limits and pricing for each model
   • Match model capabilities to task requirements

2. Temperature Settings

   • Lower (0.3) for precise tasks (meta prompt processing)
   • Higher (0.6) for creative tasks (report writing)
   • Adjust based on desired output consistency

ReAct Agents

1. Tool Organization

   • Group related tools together
   • Provide clear tool descriptions
   • Maintain focused agent responsibilities

2. Prompt Engineering

   • Be specific about agent roles
   • Include relevant context
   • Define clear output expectations

Performance Optimization

1. Token Management

   • Use appropriate models for token requirements
   • Implement chunking for large inputs
   • Monitor token usage and costs

2. Caching Strategy

   • Enable caching for repeatable operations
   • Use cache invalidation when needed
   • Monitor cache hit rates

Error Handling

try {
  const result = await chatModel.generate(input);
} catch (error) {
  if (error.type === 'token_limit') {
    // Handle token limit exceeded
  } else if (error.type === 'model_error') {
    // Handle model-specific errors
  }
  // Handle other errors
}

Monitoring

Key Metrics

1. Performance

   • Response times
   • Token usage
   • Model availability

2. Cost Management

   • Token costs per model
   • Usage patterns
   • Budget tracking

3. Quality Control

   • Output consistency
   • Error rates
   • User feedback

Security Considerations

1. Input Validation

   • Sanitize user inputs
   • Implement rate limiting
   • Validate tool parameters

2. Output Safety

   • Implement content filtering
   • Monitor for sensitive information
   • Apply output validation

3. Access Control

   • Implement model-specific access controls
   • Monitor usage patterns
   • Audit model access