Privacy Policy for Kimi K2

Effective Date: January 15, 2025
Last Updated: January 15, 2025
Version: 1.0

Introduction
Information We Collect
How We Use Your Information
Agentic AI and Autonomous Processing
Data Sharing and Third-Party Services
Data Security and Protection
Your Privacy Rights
Data Retention Policies
Cookies and Local Storage
Open Source and Community
Updates to This Policy
Contact Information

Introduction

Welcome to Kimi K2, MoonshotAI's revolutionary 1 trillion parameter mixture-of-experts model that transforms artificial intelligence from responding to truly autonomous acting. This Privacy Policy explains how Kimi K2 ("we," "us," or "our") collects, uses, processes, and protects your information when you use our agentic AI model and related services.

Our Commitment to Privacy in Agentic AI

At Kimi K2, we believe in empowering developers and researchers with autonomous intelligence while maintaining the highest standards of privacy and data protection. This policy demonstrates our commitment to:

Transparency in our agentic AI data practices
Minimal data collection - only what's necessary for agentic functionality
User control over personal information and AI interactions
Security in autonomous data handling and processing
Open source principles and community trust
Responsible AI development and deployment

By using Kimi K2, you agree to the data practices described in this Privacy Policy. If you do not agree with these practices, please do not use our services.

Information We Collect

1. Agentic AI Interaction Data

When you use Kimi K2's autonomous capabilities, we may collect:

Autonomous Task Requests

Natural language prompts you submit to the agentic AI
Task specifications and execution parameters
Contextual information necessary for autonomous problem-solving
Multi-step reasoning patterns and decision trees
Autonomous action logs and execution results

Agentic Performance Data

Success rates of autonomous task completion
Error patterns and failure modes in agentic operations
Reasoning quality metrics and optimization data
Tool usage patterns in autonomous workflows
Performance benchmarks for continuous improvement

2. Model Interaction and Usage Data

AI Model Communications

Queries and responses with the 1 trillion parameter model
Context windows utilized (up to 128K tokens)
Expert network activation patterns in mixture-of-experts architecture
Inference parameters and model configuration preferences
Session continuity data for multi-turn interactions

Technical Metrics

Model performance statistics and response times
Resource utilization in trillion-parameter processing
Expert routing efficiency in MoE architecture
Token processing patterns and optimization metrics

3. Account and Authentication Data

For enhanced agentic AI features:

User Profile Information

Email address (for account management and notifications)
Username and preferences for AI interaction style
API keys and authentication tokens for model access
Subscription status and usage tier information
Research affiliation (optional, for academic users)

Access Control Data

Authentication sessions and security tokens
Device identifiers for security and session management
Multi-factor authentication settings (strongly recommended)
Access patterns for security monitoring

4. Research and Development Data

With explicit consent for advancing agentic AI:

Anonymized Research Data

Aggregated usage patterns for AI research
Performance metrics for model improvement
Failure case analysis for safety enhancement
Benchmark performance data for academic research
Community feedback on agentic capabilities

Open Source Contributions

Code contributions to Kimi K2 ecosystem
Documentation improvements and examples
Bug reports and feature requests
Community discussions and knowledge sharing

How We Use Your Information

Primary Agentic AI Services

Autonomous Task Execution

Process agentic requests to provide autonomous problem-solving
Maintain context across multi-step autonomous operations
Learn patterns to improve autonomous decision-making quality
Execute complex workflows with minimal human intervention

Mixture-of-Experts Optimization

Route computations efficiently through expert networks
Optimize model performance based on usage patterns
Balance computational load across the trillion-parameter architecture
Enhance response quality through expert specialization

Service Enhancement and Safety

Agentic AI Improvement

Enhance autonomous capabilities through usage analysis
Improve safety measures for autonomous operations
Develop new agentic features based on user needs
Train specialized experts using aggregated, anonymized data

Research and Development

Advance agentic AI research through academic collaboration
Improve model architectures and training methodologies
Enhance safety protocols for autonomous AI systems
Contribute to open science in artificial intelligence

Communication and Community Support

User Assistance

Provide technical support for agentic AI usage
Share updates about model improvements and new capabilities
Communicate safety guidelines and best practices
Facilitate community learning and knowledge sharing

Research Community Engagement

Support academic research using Kimi K2
Coordinate open source development and contributions
Organize educational events and workshops
Build resources for responsible agentic AI development

Agentic AI and Autonomous Processing

How Agentic Intelligence Works

Autonomous Decision Making

Our agentic AI processes your requests through:

Intent understanding to comprehend your goals and constraints
Multi-step planning to break down complex problems autonomously
Expert network routing through our mixture-of-experts architecture
Autonomous execution with real-time adaptation and learning
Result evaluation and iterative improvement

Data Handling in Agentic Operations

Real-time processing - Autonomous tasks are processed with live adaptation
Context preservation - Maintaining state across multi-step operations
Privacy-first design - Minimal data retention for agentic processing
Secure execution - Isolated processing environments for safety

Autonomous Context and Privacy

Local vs. Cloud Processing

Hybrid architecture - Optimal balance of local and cloud processing
Privacy preservation - Local processing when possible for sensitive data
Cloud efficiency - Cloud processing for computationally intensive tasks
User control - Clear indication of processing location

Agentic Safety Measures

Autonomous oversight - Built-in safety mechanisms for autonomous operations
Human-in-the-loop - Optional human oversight for critical decisions
Rollback capabilities - Ability to undo autonomous actions when needed
Transparent logging - Clear logs of autonomous decision-making processes

Model Training and Improvement

Responsible AI Development

Ethical training - Training data curation with ethical considerations
Bias mitigation - Active measures to reduce bias in autonomous decisions
Safety alignment - Training for beneficial and safe autonomous behavior
Transparency - Open documentation of training methodologies

Privacy-Preserving Learning

Federated learning - Learning from distributed data without centralization
Differential privacy - Mathematical privacy guarantees in model updates
Data minimization - Using minimal data necessary for improvements
Consent-based learning - Opt-in participation in model improvement

Data Sharing and Third-Party Services

Limited Data Sharing for Agentic AI

We may share information only in specific circumstances:

Research Collaborations

Academic institutions for advancing agentic AI research
Open source community for model development and improvement
Safety research organizations for AI safety and alignment research
Standards bodies for developing agentic AI guidelines

Technical Infrastructure

Cloud providers for scalable trillion-parameter model hosting
Security services for protecting against AI misuse
Performance monitoring for ensuring optimal agentic AI operation
Backup services for data protection and disaster recovery

Legal and Safety Requirements

Legal compliance with applicable AI regulations and laws
Safety interventions to prevent potential harm from autonomous operations
Rights protection to defend legitimate interests and intellectual property
Regulatory cooperation with AI governance and oversight bodies

Third-Party AI Safety Integrations

Safety and Monitoring Tools

AI safety platforms for monitoring autonomous behavior
Content filtering services for preventing harmful outputs
Bias detection tools for ensuring fair and equitable AI behavior
Transparency tools for explainable agentic AI operations

Data Protection Standards for Partners

All partners must meet stringent requirements:

AI safety compliance with established safety frameworks
Privacy preservation with state-of-the-art protection methods
Audit requirements - Regular safety and privacy audits
Incident response - Rapid response protocols for AI safety incidents

Data Security and Protection

Advanced Security for Agentic AI

Model Security

Secure model serving - Protected infrastructure for trillion-parameter models
Access control - Granular permissions for different agentic capabilities
Model versioning - Secure tracking and deployment of model updates
Inference protection - Safeguards against model extraction and misuse

Autonomous Operation Security

Sandboxed execution - Isolated environments for autonomous operations
Action validation - Multi-layer validation of autonomous decisions
Rollback mechanisms - Secure ability to reverse autonomous actions
Audit trails - Comprehensive logging of all agentic operations

AI Safety and Alignment

Safety-First Architecture

Constitutional AI - Built-in principles guiding autonomous behavior
Value alignment - Training for beneficial and helpful autonomous actions
Harm prevention - Multiple layers of safeguards against potential misuse
Human oversight - Mechanisms for human intervention when needed

Continuous Safety Monitoring

Real-time monitoring - Continuous assessment of autonomous AI behavior
Safety metrics - Quantitative measures of AI safety and alignment
Community reporting - Channels for reporting concerning AI behavior
Rapid response - Quick action protocols for safety incidents

Your Privacy Rights

Enhanced Rights for Agentic AI Users

Agentic AI Control Rights

Autonomous operation control - Granular settings for autonomous behavior
Task approval levels - Choose required approval for different action types
Data usage preferences - Control how your data improves agentic capabilities
Model interaction logs - Access to your interaction history with the AI

Transparency Rights

Explainable AI - Understanding how autonomous decisions are made
Model behavior insights - Information about how the AI processes your requests
Training data influence - Understanding of training data's impact on responses
Bias reporting - Information about potential biases in AI responses

Regional Compliance for AI Systems

EU AI Act Compliance

High-risk AI system classification and compliance measures
Transparency obligations for AI system operation and capabilities
Human oversight requirements for autonomous AI operations
CE marking compliance for AI systems in regulated domains

Emerging AI Regulations

Proactive compliance with developing AI governance frameworks
Regular updates to reflect evolving AI regulation landscape
Multi-jurisdictional compliance for global AI deployment
Industry standards adherence to responsible AI development practices

Exercising Your Rights in Agentic AI Context

To exercise your privacy rights regarding agentic AI:

Email us at privacy@moonshot.cn
AI Rights Portal - Dedicated interface for AI-related privacy requests
Community channels - Public forums for transparency and accountability
Response commitment - Prompt response within legal timeframes

Data Retention Policies

Agentic AI Specific Retention

Autonomous Operation Data

Task execution logs - Retained for 90 days for debugging and safety
Decision trees - Anonymized patterns retained for model improvement
Safety incidents - Extended retention for safety research and prevention
Performance metrics - Aggregated data retained for research purposes

Model Training Data

User interactions - Minimal retention for immediate service improvement
Anonymized patterns - Long-term retention for research and development
Safety training - Extended retention for AI safety research
Community contributions - Retained according to open source licensing

Deletion and Data Lifecycle

Automated Data Management

Intelligent purging - AI-driven identification of data for deletion
Privacy-preserving retention - Automatic anonymization of older data
Secure destruction - Cryptographic deletion of sensitive information
Compliance monitoring - Automated compliance with retention requirements

Open Source and Community

Open Source Commitment for Agentic AI

Transparent Agentic Development

Open source core - Core agentic AI functionality available for inspection
Public research - Open publication of safety and capability research
Community governance - Democratic processes for major model decisions
Transparent roadmap - Public visibility into development priorities

Community Data Practices

Community privacy - Strong protection for community member privacy
Contribution attribution - Proper recognition while preserving privacy
Open science - Sharing research data with appropriate privacy protections
Collaborative safety - Community involvement in AI safety initiatives

Updates to This Policy

AI-Specific Policy Evolution

Regulatory Adaptation

AI regulation tracking - Continuous monitoring of evolving AI laws
Proactive updates - Policy updates anticipating regulatory changes
Community input - Involving community in policy development
Expert consultation - Regular consultation with AI ethics and law experts

Technology Evolution

Capability updates - Policy updates reflecting new agentic capabilities
Safety improvements - Updates incorporating new safety measures
Research findings - Integration of latest AI safety research
Best practices - Adoption of emerging industry best practices

Contact Information

Privacy and AI Safety Inquiries

For privacy-related questions about agentic AI:

Primary Contact

Email: privacy@moonshot.cn
AI Safety Team: safety@moonshot.cn
Response time: Within 24 hours for urgent safety matters
Languages: English, Chinese, Japanese

Specialized Contacts

Research Ethics: ethics@moonshot.cn
Security Issues: security@moonshot.cn
Community Support: community@moonshot.cn
Developer Relations: developers@moonshot.cn

Academic and Research Collaboration

Research Partnerships: research@moonshot.cn
Academic Access: academic@moonshot.cn
Ethics Review Board: ethics-board@moonshot.cn

AI Governance and Compliance

For regulatory and compliance matters:

Compliance Officer: compliance@moonshot.cn
Data Protection Officer: dpo@moonshot.cn
AI Ethics Committee: ai-ethics@moonshot.cn

Mailing Address

MoonshotAI Privacy and AI Safety Team
[Company Address]
Beijing, China

Last Updated: January 15, 2025
Effective Date: January 15, 2025
Version: 1.0

This Privacy Policy reflects our commitment to responsible development and deployment of agentic AI technology. We are committed to maintaining the highest standards of privacy protection while advancing the frontier of autonomous artificial intelligence for the benefit of humanity.

Kimi K2 represents the future of AI - where artificial intelligence becomes truly agentic, autonomous, and beneficial. Our privacy practices evolve with this technology to ensure your trust and safety remain our highest priorities.