At MindGrid, security and compliance are not afterthoughts, they are embedded into the core of how our solutions are built, deployed, and maintained. Robotics intelligence systems operate in safety critical environments, often with direct human interaction, which demands strict guarantees around data integrity, privacy, and operational reliability.
Data Security
Encrypted transmission: All API traffic is secured via TLS 1.3 and mutual authentication protocols.
Access control: Role-based permissions and token-based authentication ensure only authorized users can access or control deployed solutions.
Audit logs: Every request and response is timestamped and logged to enable full traceability.
Privacy
Edge-first architecture: Where possible, data is processed locally on-device, reducing exposure of sensitive information to external servers.
Selective offloading: Only non-sensitive, high-compute workloads are routed to cloud APIs.
Compliance alignment: Solutions are engineered to comply with GDPR, SOC2, and other relevant frameworks, ensuring safe deployment in regulated industries.
Model Reliability
Deterministic fallbacks: If confidence in a model’s output drops below threshold, predefined fallback routines are triggered to maintain safe operation.
Sandboxing: New updates are rolled out in controlled environments before deployment to production hardware.
Safety filters: Models are layered with safety constraints to avoid unintended actions or unsafe outputs.
Regulatory Readiness
Cross-sector compliance: Solutions are designed to integrate into industries with high regulatory oversight, logistics, manufacturing, healthcare, and consumer robotics.
Transparent reporting: Tokenized solutions include performance and telemetry dashboards, providing investors and companies with verifiable data streams.
Independent validation: Key modules undergo external benchmarking and certification to validate safety and accuracy claims.
Governance
Security and compliance extend into our tokenization model. Each fractionalized solution on the marketplace is accompanied by transparent data on performance, revenue generation, and lifecycle status, ensuring stakeholders can trust both the robotics layer and the financial layer of MindGrid.
