Privacy-first Android defense, built around local decisions.
The principles behind Cyber Guardian: on-device analysis, controlled telemetry, encrypted workflows, and auditable threat evidence.
Data Handling
Cyber Guardian is designed to analyze mobile threat signals on the Android handset. The product direction is to avoid uploading raw personal content for first-line detection decisions.
On-Device Detection
App, DNS, file, and behavior scoring are built around local runtime analysis. Cloud synchronization is reserved for controlled model, policy, and evidence workflows rather than raw user-data inspection.
Encryption & Transport
Cloud-connected workflows use encrypted transport and authenticated access. Enterprise export paths are being built toward signed, auditable receipts and privacy-aware redaction.
Federated Learning
Federated learning is intended to improve detection without centralizing raw training data. Current design work includes bounded updates, adaptive weights, and validation gates for model behavior.
Models that improve as a fleet, without centralizing data.
Devices contribute bounded model updates instead of raw telemetry. The fleet's collective intelligence sharpens in real time, and the privacy boundary holds. Adaptive weights and validation gates govern model behavior so updates are auditable and reversible.
Authenticated, encrypted, and minimal by design.
Cloud-connected workflows use encrypted transport and authenticated access. Enterprise export paths are signed and auditable. Anything that does cross the wire is bounded model updates or redacted forensic receipts, never raw user content.