LLM Processing Privacy Policy

Data Flow, Network Boundaries & Privacy Protection

Status: Complete
Version: 1.1
Last Updated: 2025-01-12
Applies To: All Knowcode Ltd LLM and AI tool usage

Executive Summary

Complete Transparency in AI Data Processing

This policy provides detailed information about what data sits where, what information crosses network boundaries, and how privacy is protected when using Large Language Models (LLMs) and AI tools in Knowcode Ltd operations.

Critical Insight: Understanding data flows is essential for maintaining security, compliance, and client trust in AI-powered development workflows.

LLM Data Flow Architecture

Complete Data Journey Mapping

graph TD subgraph " Local Environment" A[Developer Workstation] B[Local Files & Code] C[Environment Variables] D[Configuration Files] E[Git Repository] end subgraph " Network Boundary" F[Corporate Firewall] G[VPN Gateway] H[TLS Encryption Layer] end subgraph " External LLM Services" I[Anthropic Claude Code] J[OpenAI API Services] K[Other AI Providers] end subgraph " Data Processing" L[AI Model Processing] M[Response Generation] N[Temporary Storage] O[Analytics & Logging] end A --> F B --> I C -.->|"Protected by Default"| A D --> I E --> I F --> H G --> H H --> I H --> J H --> K I --> L J --> L K --> L L --> M M --> N M --> O N -.->|"Temporary Only"| L style A fill:#f0fdf4,color:#000 style C fill:#fefce8,color:#000 style H fill:#fefce8,color:#000 style L fill:#fdf2f8,color:#000 style N fill:#fefce8,color:#000

Data Location Matrix

STAYS LOCAL

Always Protected

SSH keys and certificates
Database credentials
API keys and secrets
Personal identification data
Proprietary algorithms (unless explicitly shared)

Local Processing

File system metadata
Local configuration preferences
Cached responses
Development environment settings
Git history and branches

CROSSES NETWORK

Transmitted to LLM Services

Source code files (when read)
Natural language prompts
Error messages and logs
File structure information
Development context data

Potentially Sensitive

Business logic and algorithms
Custom implementations
Client-specific code
Internal processes
Architecture patterns

REMOTE PROCESSING

AI Provider Infrastructure

LLM model processing
Response generation
Temporary data storage
Usage analytics
Error reporting

Retention Periods

30-day standard retention
Zero retention options available
Processing memory only
No permanent model training

Network Boundary Analysis

What Crosses the Corporate Network

Detailed Network Flow Analysis

Outbound Data Flows

sequenceDiagram participant Dev as Developer participant Local as Local Machine participant Corp as Corporate Network participant Internet as Internet participant LLM as LLM Provider Dev->>Local: Execute AI command Local->>Local: Read local files Local->>Corp: Encrypt data packet Corp->>Corp: Firewall & security checks Corp->>Internet: Forward through VPN Internet->>LLM: TLS encrypted transmission Note over Corp: Corporate security controls apply Note over Internet: Data encrypted in transit Note over LLM: Processed on remote servers LLM-->>Internet: Encrypted response Internet-->>Corp: Return through VPN Corp-->>Local: Security validated response Local-->>Dev: Display results

Network Security Layers

Layer	Protection	Data State	Controls
Local Machine	OS-level permissions	Plaintext files	File system access controls
Corporate Firewall	Network filtering	Encrypted packets	Traffic monitoring & filtering
VPN Gateway	Network tunneling	Encrypted tunnel	Corporate network policies
Internet Transit	TLS encryption	Encrypted in transit	Certificate validation
LLM Provider	Provider security	Processed remotely	Provider privacy policies

Critical Network Boundary Considerations

Data Exposure Risks

Internet Transmission

All prompts and code sent over internet
Potential for network interception
Dependency on external service availability
Possible service provider data breaches

Corporate Visibility

Network logs may capture metadata
Firewall logs show connection patterns
VPN logs record data transfer volumes
Security teams can monitor AI tool usage

Service Provider Logging

Usage patterns tracked by LLM providers
Error logs may contain sensitive context
Analytics data collection
Potential compliance audit trails

Protection Measures

Encryption Standards

TLS 1.3 encryption for all transmissions
Certificate pinning where supported
End-to-end encryption maintenance
Regular security protocol updates

Access Controls

Multi-factor authentication requirements
API key rotation and management
Network access restrictions
User permission management

Monitoring & Auditing

Comprehensive usage logging
Regular security assessments
Compliance monitoring
Incident response procedures

Service Provider Data Handling

Anthropic (Claude Code)

Anthropic Data Processing Details

Data Handling Practices

Default Policy: No training on user code or conversations
Retention: 30-day automatic deletion from backend systems
Local Storage: Up to 30 days on user devices for session resumption
Zero Retention: Available for enterprise customers with special API keys

Security Measures

Encryption: TLS encryption for all data in transit
Access Controls: Strict server-side access limitations
Processing: Temporary processing memory only, no persistent storage
Compliance: SOC 2 Type II compliance and regular security audits

Data Locations

Primary Processing: United States (specific regions may vary)
Backup Systems: Geographic redundancy for service reliability
Legal Jurisdiction: Governed by Anthropic's terms of service
Data Residency: Confirm with Anthropic for specific regulatory requirements

Other LLM Providers

OpenAI Services

Data Handling (as of 2024)

Training Policy: Data not used for model training by default (with API)
Retention: 30-day retention period for abuse monitoring
Zero Retention: Available for enterprise customers
Location: Primarily US-based processing

Security Features

TLS encryption in transit
SOC 2 Type II compliance
Regular security assessments
Enterprise-grade security controls

Cloud Provider LLMs

Enterprise Options

AWS Bedrock: Data stays within your AWS account
Google Vertex AI: Processed within Google Cloud infrastructure
Azure OpenAI: Data remains in your Azure tenant

Enhanced Control

Customer-managed encryption keys
VPC/private network connectivity
Detailed audit logging
Regional data residency options

User Rights & Privacy Controls

Individual Privacy Rights

Your Data Rights

Access & Control Rights

Right to Know: What data is collected and how it's used
Right to Access: Review data that providers have about you
Right to Delete: Request deletion of your data from provider systems
Right to Correct: Update or correct inaccurate information
Right to Export: Download your data in portable formats

Opt-Out Mechanisms

Telemetry Opt-Out: Disable usage analytics and error reporting
Training Opt-Out: Ensure data is not used for model training
Data Retention Opt-Out: Use zero retention services where available
Service Opt-Out: Discontinue use of AI services entirely

Implementation Controls

Technical Controls

Environment Configuration

# Disable telemetry across all tools
export DISABLE_TELEMETRY=true
export DISABLE_ERROR_REPORTING=true
export DISABLE_BUG_COMMAND=true

# Use zero retention API keys
export ANTHROPIC_API_KEY="zero-retention-key"
export OPENAI_API_KEY="enterprise-zero-retention-key"

File Access Controls

# Restrict Claude Code to specific directories
cd /safe/project/directory
claude-code --restrict-access

# Review files before AI processing
claude-code --confirm-file-access

Policy Controls

Organizational Policies

Clear AI tool usage guidelines
Data classification and handling procedures
Approval workflows for sensitive projects
Regular privacy impact assessments

User Training

Privacy awareness training
Data handling best practices
Incident reporting procedures
Regular policy updates and reviews

Compliance & Regulatory Considerations

Global Privacy Regulations

🇪🇺 GDPR Compliance (European Union)

graph TD A[EU Personal Data] --> B{GDPR Assessment} B -->|Personal Data Identified| C[GDPR Protections Required] B -->|No Personal Data| D[Standard Processing] C --> E[Lawful Basis Required] C --> F[Data Subject Rights] C --> G[Data Protection Impact Assessment] E --> H[Legitimate Interest/Consent] F --> I[Access, Rectification, Erasure] G --> J[Risk Mitigation Measures] style A fill:#f0f9ff,color:#000 style C fill:#fefce8,color:#000 style D fill:#f0fdf4,color:#000

GDPR Considerations for LLM Usage

Personal Data Definition: Any information relating to identified or identifiable individuals
Processing Basis: Requires legitimate interest or explicit consent for AI processing
Data Subject Rights: Must provide access, correction, and deletion mechanisms
Cross-Border Transfers: Special requirements for data leaving EU/EEA
Impact Assessments: Required for high-risk AI processing activities

Industry-Specific Regulations

Healthcare (HIPAA)

High Risk

PHI cannot be sent to most LLM providers
Requires HIPAA-compliant AI services
Business Associate Agreements needed
Strict access controls and audit trails

Mitigation

Use HIPAA-compliant AI services
Data anonymization before processing
Secure, private cloud deployments
Regular compliance audits

Financial (SOX/PCI)

Moderate Risk

Financial data requires special handling
Audit trail requirements
Access control documentation
Change management procedures

Controls

Separate environments for financial systems
Enhanced logging and monitoring
Regular security assessments
Compliance documentation

Government/Defense

Highest Risk

Classified data cannot use external LLMs
Strict data residency requirements
Security clearance implications
FedRAMP compliance needed

Requirements

On-premises or government cloud only
Security clearance for all personnel
Continuous monitoring
Regular security assessments

Security Best Practices

Implementation Guidelines

Comprehensive Security Framework

Organizational Level

Data Classification: Identify and classify all data types before AI processing
Risk Assessment: Regular evaluation of AI tool risks and benefits
Policy Development: Clear guidelines for AI tool usage and data handling
Training Programs: Regular education on privacy and security best practices
Incident Response: Prepared procedures for privacy and security incidents

Individual Level

Data Awareness: Understand what data you're sharing with AI tools
Tool Configuration: Properly configure privacy and security settings
Access Controls: Use strong authentication and limit tool access
Regular Reviews: Periodically review AI tool usage and data exposure
Incident Reporting: Immediately report suspected privacy or security issues

Technical Implementation

Recommended Practices

Network Security

Use corporate VPN for all AI tool access
Implement network monitoring and logging
Regular security updates and patches
Certificate pinning where possible

Data Management

Regular data classification reviews
Automated sensitive data detection
Secure deletion procedures
Backup and recovery planning

Access Control

Multi-factor authentication
Role-based access controls
Regular access reviews
API key rotation

Common Pitfalls

Security Risks

Using AI tools on unsecured networks
Sharing API keys between team members
Processing sensitive data without classification
Ignoring privacy settings and defaults

Compliance Issues

Lack of data processing documentation
Insufficient impact assessments
Missing consent mechanisms
Inadequate data subject rights implementation

Technical Problems

Outdated security configurations
Unmonitored AI tool usage
Inadequate logging and auditing
Poor incident response procedures

Monitoring & Audit Framework

Continuous Monitoring

Monitoring Strategy

Usage Monitoring

AI tool access patterns and frequency
Data volume and type analysis
User behavior and compliance monitoring
Service provider usage tracking

Security Monitoring

Network traffic analysis for AI services
Anomaly detection in usage patterns
Security incident detection and response
Regular vulnerability assessments

Compliance Monitoring

Privacy policy adherence checking
Regulatory requirement compliance
Data retention policy enforcement
User rights request handling

Audit Requirements

Regular Audits

Quarterly Reviews

AI tool usage patterns
Privacy policy compliance
Security control effectiveness
User training completion

Annual Assessments

Comprehensive privacy impact assessment
Security posture evaluation
Regulatory compliance review
Third-party service provider evaluation

Documentation

Required Records

Data processing activities
Privacy impact assessments
Security incident reports
User consent and opt-out records

Audit Trails

AI tool access logs
Data transfer records
Configuration changes
Policy updates and communications

Reporting

Regular Reports

Monthly usage summaries
Quarterly compliance reports
Annual privacy assessments
Incident response summaries

Stakeholder Communication

Executive dashboard updates
Team training reports
Client privacy notifications
Regulatory filing requirements

📞 Privacy Support & Contacts

Privacy Questions & Support

Knowcode Ltd Privacy Team
General Privacy Questions: privacy@knowcode.co.uk
Data Subject Rights Requests: privacy@knowcode.co.uk
Privacy Impact Assessments: privacy@knowcode.co.uk

Privacy Incident Reporting
Immediate Response: security@knowcode.co.uk
Security Breaches: security@knowcode.co.uk

External Resources
Anthropic Privacy Center: https://privacy.anthropic.com
OpenAI Privacy Policy: https://openai.com/privacy
GDPR Information: https://gdpr.eu

We're committed to maintaining the highest standards of privacy protection and transparent communication about data handling practices.

Privacy Policy Summary

This LLM Processing Privacy Policy provides complete transparency about data flows, network boundaries, and privacy protections when using AI tools, enabling informed decision-making and maintaining compliance with privacy regulations and organizational policies.

The result: Clear understanding of what data goes where, robust privacy protections, and comprehensive compliance framework for AI-powered operations.

Document Control Information

Classification: Internal Use / Compliance
Distribution: All teams using AI tools, Legal, Security, Compliance
Review Authority: Privacy Officer, Legal Department, Security Team
Next Review: 2026-01-12 (or upon significant service changes)
Document Version: 1.1
Related Policies: IP Ownership Framework, Claude Code Data Handling