Trust Center

Trust our systems: security, privacy, responsible AI, and ESG.

This Trust Center summarizes Learnroll’s institutional posture across enterprise deployment, data protection, responsible AI governance (including open/closed LLM options), and annual carbon footprint reporting.

 

Trust highlights for institutional review

Enterprise deployment
SOC-aligned patterns, partner-supported infrastructure, and rollout controls.
Privacy reliability
Region-aware hosting options, access control, and privacy-first design.
Responsible AI
Guardrails, provider transparency, and open/closed model choices.
ESG reporting
Annual carbon footprint reporting and responsible cloud/AI operations.

Security and enterprise deployment

Deployment posture
  • SOC-aligned operational controls via third-party cloud/IT partners
  • Country/region-based deployment options to support data residency requirements
  • Role-based access control patterns for institutional programs
  • Phased rollout support (pilot → scale) for enterprise adoption
Operational assurance
  • Change management and configuration controls for production environments
  • Logging/monitoring patterns aligned to institutional IT expectations
  • Secure integration posture for approved third-party services
  • Documentation available for institutional security review

Privacy and data handling

Privacy-first design
  • Minimal data collection aligned to training/workflow needs
  • Controlled access for institutional admins and authorized users
  • Region-aware hosting options via partners when required
  • Clear intended-use boundaries for training vs. clinical care
Data governance
  • Program-controlled configurations for content and usage
  • Documented data flows to support institutional review
  • Secure handling for any explanation/analytics workflows
  • Partner/subprocessor transparency for cloud and AI services

Responsible AI governance

Guardrails and intended use
  • AI supports learning, reflection, and training workflows
  • Not designed for autonomous clinical decision-making
  • Content and workflows designed to reduce ambiguity and misuse
  • Institutional policy alignment for sensitive use cases
AI supply chain transparency
  • Visibility into cloud, GPT/LLM services, and enabled integrations
  • Configurable use of open and closed LLMs based on program needs
  • Documentation patterns for integrations/plugins used in workflows
  • Operational reporting aligned to responsible AI expectations

Explainable AI (XAI) with human-in-the-loop

How we approach XAI for clinicians

Explainability in clinical learning can be complex. XAI can use inherently interpretable models (e.g., decision-tree logic) and post-hoc methods (e.g., LIME/SHAP-style approaches) to provide human-understandable explanations.

  • Feature importance summaries and example-based explanations
  • “What-if” analysis to explore alternative scenarios
  • Human review aligned with differential diagnosis reasoning
Privacy and secure explainability
  • Controlled access for explanation workflows and outputs
  • Confidential handling of prompts, context, and examples
  • Governance-friendly logging and review patterns
  • Designed to keep sensitive workflows private and secure

ESG and carbon footprint reporting

Learnroll reports its carbon footprint annually—even as a small business—to promote responsible digital operations.

  • Cloud and AI usage are part of operational accountability
  • We track and document impacts as tools and providers evolve
  • We prioritize equitable, accessible learning experiences as part of ESG commitments

Subprocessors and integrations

Institutions often require transparency into third-party services used for hosting, analytics, and AI-enabled workflows. Maintain this table as a living registry.

Provider / Partner Purpose Data category Region / Residency Notes / Link
Cloud provider-Multi Cloud Hosting / storage/US/EU/Asia Data Centers via Service Providers Platform data US / EU / Country-based Approved Partners and Services API/SDK - Meta Quest Store/SDK, Meta Business MDM, Arbor XR MDM, AWS Cloud, EC2, S3, GCP
LLM provider - Open AI , Google AI/Notebook LLM, LLAMA/In-House LLM,Claude,Vertex, Whisper, Real Time Voice API GPY 4o, Open source Whisper AI learning support Prompt/context (per policy) Configurable Open/closed model option include Closed LLM like Open AI/Google whose content is controller by 3rd part while Open LLM are self hosted/partner hosted private LLM like LLAMA
Analytics - Google, Meta, LTI/3rd Party Open Badges Usage analytics Non-PHI telemetry Configurable Optional / can be disabled
Please review the ESG Policies in for the various technical vendors.

Need documentation for procurement or compliance review?

Request security and privacy documentation, deployment options, and responsible AI governance details.
Request documentation
Learnroll platforms support education and training workflows and do not provide medical diagnosis or treatment.