Local Private Assistant

Problem

Local execution can reduce data egress, latency, and dependency on hosted services, but the word local does not by itself guarantee privacy or security. Model downloads, update channels, telemetry, crash reports, plugins, remote retrieval, cloud fallback, operating-system indexing, and shared-device storage can still move or expose information. Device variability also means the runtime must handle memory pressure, accelerator availability, thermal behavior, model compatibility, and partial offline operation.

Why a runtime layer is needed

The local application runtime owns the boundary between device-only processing and any networked capability. It selects a compatible model and execution provider, controls local storage and memory, obtains consent for hosted fallback, mediates plugins and file access, and records whether a response was produced locally or remotely. The inference engine handles loading and token generation; the application runtime enforces the user-facing privacy contract.

Reference architecture

A capability probe detects operating system, architecture, accelerator, available memory, supported model formats, storage, and power state without transmitting sensitive device details by default.
A signed model manager verifies model packages, licenses, manifests, checksums, compatibility, and rollback metadata before activation.
The local inference engine uses bounded CPU/GPU/NPU resources and exposes deterministic health, memory, context-window, and cancellation signals.
A context broker mediates local files, clipboard, calendar, browser history, or other sources through explicit permissions and purpose-scoped access.
Local memory is encrypted where the platform permits, namespaced by user/profile, bounded by retention, and controllable through inspect/delete operations.
Hosted fallback is disabled by default or governed by an explicit policy that describes triggers, transmitted fields, provider, region, retention, and user confirmation.
Update and telemetry services are separable from inference and operate with minimization, signature verification, and offline-safe behavior.

Request flow

Resolve the local user/profile, task, requested data sources, current connectivity, and privacy mode.
Check device capability and select a compatible local model/runtime configuration.
Request only required local permissions and assemble context in process or through approved local brokers.
Run local inference with cancellation, memory, thermal, and deadline controls.
When local capability is insufficient, follow the configured fallback policy rather than silently sending data to a hosted service.
Validate output, label the execution location, and apply the local memory decision.
Store minimized local evidence and expose controls to inspect or delete retained data.

Contracts

Runtime request: user/profile, task, local data permissions, privacy mode, allowed execution locations, model constraints, deadline, and memory policy.
Model package manifest: model identity/version, format, hash, size, license reference, required operations, minimum memory, supported accelerators, and signing metadata.
Hosted fallback policy: eligible conditions, prohibited classifications, destination, redaction, confirmation, retention, and response labeling.
Local memory policy: namespace, encryption, maximum size, expiry, deletion, export, and synchronization rules.

Failure modes and recovery

Detection, behavior, retry, idempotency, recovery, and evidence
Failure	Detection	User-visible behavior	Retry and idempotency	Recovery and evidence
Model incompatible with device	Capability or operator check fails before activation.	Offer a compatible local model or explain the unsupported requirement.	Retry only after selecting a different package/runtime. Activation is atomic and versioned.	Keep the last known-good model and configuration. Evidence: Capability summary, rejected package version, and fallback selected.
Memory or thermal limit reached	Allocator, OS pressure signal, accelerator error, or thermal policy triggers.	Cancel cleanly, preserve the user input locally, and suggest a smaller configuration.	Eligible with reduced context/model/threads after resources recover. No external side effect is repeated.	Unload model, release caches, and restore last stable configuration. Evidence: Resource limits, cancellation point, and selected mitigation.
Model update verification fails	Signature, hash, manifest, or compatibility verification fails.	Keep the installed version and show a clear update error.	Retry only after obtaining a verified package. Downloads are staged and content-addressed.	Delete the untrusted staging package and alert on repeated failures. Evidence: Package identity, verification checks, and rollback status.
Hosted fallback requested but not authorized	Fallback policy or user confirmation denies the transfer.	Remain local, provide a limited response, or explain that the task cannot be completed.	Do not retry remotely without a new explicit authorization. No network payload is sent before authorization.	Select an approved local alternative or stop. Evidence: Fallback trigger, denied fields/classification, and user decision.
Local memory write fails	Storage full, permission denied, encryption failure, or database corruption.	Return the answer but clearly state that memory was not saved when safe to do so.	Retry only after storage recovery; do not duplicate entries. Memory entries use deterministic IDs and transactions.	Roll back partial writes, repair or reset storage, and preserve user control. Evidence: Entry ID, failure category, rollback, and final memory state.

Security considerations

Document all network-capable components; local inference does not make update, telemetry, plugin, or fallback traffic local.
Verify signed model and runtime updates and retain a rollback path to a known-good version.
Use operating-system permission brokers and sandboxing rather than granting broad filesystem or accessibility access.
Encrypt and namespace local memory, provide deletion, and avoid indexing sensitive content into globally searchable stores.
Prevent plugins or local tools from inheriting unrelated user credentials or unrestricted network access.
Label local versus hosted execution in the product UI and evidence so users can verify the privacy boundary.

Observability

Collect local health, latency, resource, model-version, fallback, and error metrics with opt-in or minimized telemetry policy.
Keep sensitive request content on-device; use coarse error categories and local diagnostic export when support is needed.
Record model package and runtime versions, execution provider, memory decisions, and network-fallback decisions.
Alert locally on update verification failures, storage corruption, repeated capability fallback, and unauthorized network attempts.

Evaluation

Test quality and latency across representative low-, mid-, and high-capability devices rather than one developer workstation.
Verify offline operation, permission denial, model update rollback, storage-full behavior, and absence of silent hosted fallback.
Measure privacy-boundary compliance through network capture and local data-flow inspection.
Evaluate smaller-model refusal and limitation behavior, not only task success.

Metrics

Local completion rate by device class
Time to first token, tokens per second, and peak memory
Battery and thermal impact for sustained use
Hosted-fallback rate and user-confirmation rate
Model update success, rollback, and verification-failure rates
Local memory failure and deletion-completion rates
Unexpected network egress incidents (target: zero)

Implementation checklist

Define the product’s precise local-processing and network-egress contract.
Probe capability and provide tested smaller-model or CPU fallbacks.
Sign and verify model/runtime packages and exercise rollback.
Broker local data access through least-privilege permissions.
Make hosted fallback explicit, reviewable, and disabled for prohibited data.
Test offline, low-memory, thermal, storage-full, shared-device, and update-failure conditions.

Sources

Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

National Institute of Standards and Technology · Government framework · 2026-06-23T00:00:00Z

Risk identification, measurement, governance, and lifecycle controls for generative AI systems.
OWASP Top 10 for LLM Applications 2025

OWASP Foundation · Security guidance · 2026-06-23T00:00:00Z

Threat categories for prompt injection, sensitive information disclosure, excessive agency, and insecure output handling.
ExecuTorch Documentation

PyTorch Foundation · Official project documentation · 2026-06-23T00:00:00Z

Edge deployment concepts, model export, hardware backends, and constrained-device execution.
LiteRT Overview

Google · Official project documentation · 2026-06-23T00:00:00Z

On-device model execution, delegates, acceleration, and mobile deployment considerations.

Find runtime definitions and implementation guidance