Mobile RASP Reference Architecture
A reference Mobile RASP architecture has four explicit planes: on-device observation, protected signal transport, policy and risk decisioning, and enforcement within both the app and backend.
Read guideDesign the system
Architecture & Trust Design
Reference patterns for signals, risk decisions, backend enforcement, privacy, resilience, and graceful degradation across the Mobile RASP control plane.
12 focused guides2 cornerstone topicsPrimary sources first
Working position
What this section is built to answer
A durable Mobile RASP architecture separates observation, decision, and enforcement. The app can collect high-value runtime evidence, but the backend should verify signed or attested claims, combine them with transaction context, and own irreversible decisions.
- Define the boundary between device evidence and server authority
- Design explicit states for normal, elevated-risk, restricted, and unavailable conditions
- Preserve service reliability when telemetry or attestation dependencies degrade
Complete topic set
Architecture & Trust Design guides
The Signal–Decision–Response Model
The signal–decision–response model prevents teams from confusing an observed runtime condition with a risk judgment or user-facing action. Each stage has different confidence, latency, and ownership requirements.
Read guideOn-Device vs Server-Side Mobile RASP Decisions
On-device decisions are fast and can protect offline flows, while server-side decisions are harder to tamper with and can combine account, transaction, and fleet evidence. Sensitive systems normally need both.
Read guideDesigning a Mobile RASP Policy Engine
A Mobile RASP policy engine converts versioned runtime evidence and business context into an explainable action such as allow, monitor, step up, restrict, or deny.
Read guideMobile RASP Risk Scoring
Risk scoring can combine weak Mobile RASP signals without pretending that any one indicator proves compromise. The score is useful only when calibration, thresholds, decay, and actions are defined.
Read guideMobile RASP Telemetry Architecture
A Mobile RASP telemetry pipeline should deliver compact, tamper-aware, privacy-minimized events from a hostile client into systems that can validate, enrich, store, alert, and support later investigation.
Read guideSecure Backend Enforcement for Mobile RASP
Backend enforcement turns runtime evidence into meaningful protection by refusing to trust client-supplied outcomes, validating freshness and authenticity, and applying restrictions at the system that owns the asset.
Read guideMobile RASP for Offline Applications
Offline workflows cannot depend on a live attestation or policy service, so they need bounded local decisions, cached policy, protected counters or state where appropriate, and clear reconciliation when connectivity returns.
Read guideFail Open vs Fail Closed in Mobile RASP
Fail-open and fail-closed are not application-wide settings. Each dependency and business action needs a response based on safety, transaction value, confidence, recoverability, and legal or accessibility duties.
Read guideMobile RASP Performance Budgets
Mobile RASP consumes startup time, CPU, memory, binary size, network, and battery. Explicit budgets keep security work compatible with product reliability and make regressions visible before release.
Read guidePrivacy-Preserving Mobile RASP Telemetry
Runtime security evidence can reveal device configuration, installed software signals, account activity, and behavioral context. Collection should be necessary, disclosed, minimized, protected, and retained only as long as the purpose requires.
Read guideMobile RASP High Availability and Graceful Degradation
A Mobile RASP service may depend on attestation providers, policy APIs, event ingestion, identity systems, and fraud engines. The application must remain predictable when any dependency is slow, partial, or unavailable.
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Test before trust
Evidence expected from this topic cluster
Validation
- Exercise signal loss, latency, replay, and policy-service outages
- Verify that client-side outcomes agree with backend authorization
- Measure privacy, performance, and availability budgets under peak load
Failure modes to avoid
- Using a binary trusted/untrusted model for ambiguous evidence
- Allowing the client to self-assert a clean state
- Failing closed on low-confidence evidence without a recovery path