SurePath AI - AI Use Disclosure Statement

Effective Date: Oct 31, 2025

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Our Commitment to Your Data Privacy

At SurePath AI, we understand that trust is fundamental to our partnership with you, and that a key value proposition of our platform is controlling which 3rd parties have access to your most sensitive and critical data that might train that data into AI models.

We're committed to maintaining the highest standards of data privacy and security in our use of artificial intelligence technologies. This comprehensive disclosure outlines exactly how we handle your data in relation to AI systems to ensure your data is always protected and only leveraged by the parties you approve, including ourselves.

Our Data Protection Principles

Zero Customer Data Training. We do not train AI models on customer data. Period.

What this means

  • Your proprietary business data, documents, communications, and any information you upload or process through SurePath AI will never be used to train, retrain, or improve any AI models by SurePath AI
  • This prohibition extends to all forms of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and transfer learning
  • We maintain strict technical and operational safeguards to ensure customer data cannot inadvertently be included in any training datasets

Technical Controls

  • Customer data is stored in isolated, encrypted environments separate from any AI training infrastructure
  • Our systems include security controls that prevent customer data from being accessed by training pipelines
  • We never send customer data to third-party AI models outside of SurePath AI's direct management or control
  • All data flows are monitored and audited to ensure compliance with this commitment
  • All AI models developed, trained, or fine-tuned by SurePath AI are classification models designed for specific policy enforcement tasks, not content generation

Why this matters

  • Prevents your sensitive business information from being embedded in AI models that could potentially be accessed by unauthorized parties
  • Ensures your competitive advantages and proprietary information remain exclusively yours
  • Eliminates the risk of your data being used to benefit other customers or third parties

Aggregate Data Usage

We also want to be clear about how we use Aggregate Data.  As a SaaS platform, we continuously improve our services and ensure optimal performance through the use of Aggregate Data. Aggregate Data means any data that is derived or aggregated in de-identified form across the aggregate of all users of the platform.

How We Use Aggregate Data

  • Statistical Aggregation: Individual data points are combined into statistical summaries that cannot be reverse-engineered
  • Market Insights & Threat Intelligence: Sharing insights of broad AI usage trends and risk patterns observed to proactively assist in securing AI risks
  • Performance Optimization: Improving system response times and processing efficiency
  • Feature Development: Understanding broad usage patterns to guide product roadmap decisions
  • Quality Assurance: Identifying and resolving system issues before they impact customer experience

Technical Controls

  • Data Anonymization: No raw user inputs or model responses are ever directly used in our Aggregate data
  • Statistical Aggregation: Individual data points are combined into statistical summaries that cannot be reverse-engineered
  • Threshold Requirements: We only use aggregate data where sample sizes are large enough to prevent re-identification
  • Regular Review: Our data science team regularly audits aggregate data to ensure continued anonymity

Examples of Aggregate Data

  • System Performance Metrics:
    • Response times across different types of queries
    • Processing throughput and capacity utilization
    • Error rates and system reliability statistics
    • Resource consumption patterns
  • Usage Patterns:
    • Market trends of AI services adoption and usage
    • Common product usage patterns and user journey analytics
    • Feature adoption rates and usage trends
    • Geographic distribution of service usage (at regional/country level only)

Summary

This disclosure reflects our commitment to transparency and the protection of your data, and the technical controls we maintain to ensure that commitment. 

As part of this continued commitment we will:

  • Update this statement as the AI market continues to evolve
  • Maintain our core commitment to your data privacy regardless of these technological advances