David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · CONFIDENTIAL AI AUTOMATION PLATFORM
Confidential AI Automation Platform
Browser-Native Agentic AI Security Control Plane
A product-security architecture for governing browser extensions, Tauri sidecars, MITM interception, local AI, schema normalization, agent authority, and...
Designed a browser-native AI security control plane connecting Chrome extension automation, persistent offscreen workers, WebLLM, Transformers, Rust/WASM engines, Tauri sidecar processing, authorized MITM request/response...
Client
Confidential / Internal AI Automation Platform
Engagement Type
Consulting / Internal Buildout
Period
2025–2026
Role
AI Product Security Architect / Browser-Native Automation Engineer / Rust-WASM Systems Architect
Focus Areas
AI Product Security, Browser-Native Automation, Agentic Authority Boundaries, Chrome Extension Security
The Research Narrative
Strategic Problem
The challenge was defining authority. Content scripts should not own persistent workers. Agents should not inherit unlimited tool reach. MITM capture should not be invisible. Local model output should not...
What David Did
David designed a control plane connecting Chrome extension workers, WebLLM, Transformers, Rust/WASM engines, Tauri sidecar processing, request/response capture, WSS listeners, schema...
What Became Clearer
The result was a reusable security architecture for high-authority AI systems operating across browser and native desktop surfaces. It shows how powerful agentic automation can be made...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
Browser-native AI automation sits at a dangerous intersection: web pages, extension APIs, local models, native sidecars, credentials, application traffic, WebSocket streams, and agents that can act. A pile of capabilities is not a secure product architecture.
The Challenge
The challenge was defining authority. Content scripts should not own persistent workers. Agents should not inherit unlimited tool reach. MITM capture should not be invisible. Local model output should not become action without review. Every boundary needed a control.
What I Did
David designed a control plane connecting Chrome extension workers, WebLLM, Transformers, Rust/WASM engines, Tauri sidecar processing, request/response capture, WSS listeners, schema normalizers, event envelopes, and agent permissions.
- •Defined a control-plane architecture spanning browser extension surfaces, persistent workers, local model execution, native Tauri sidecar processing, MITM capture, and downstream agent workflows
- •Separated content scripts from orchestration authority so page-level scripts could observe and request work without directly controlling persistent workers or privileged automation
- •Used persistent offscreen workers to host longer-lived model and automation workloads while respecting Chrome extension lifecycle constraints
- •Embedded local inference options such as WebLLM and Transformers where private, browser-local analysis was appropriate
- •Used Rust/WASM engines for reusable scoring, extraction, normalization, and matching logic that could run across browser, edge, and native surfaces
- •Designed the Tauri sidecar as a native authority boundary for local MITM processing, WebSocket stream handling, LLM chat interception, and schema-normalized event emission
- •Implemented or designed typed event envelopes so raw browser, API, WebSocket, and sidecar events could become stable, inspectable records
- •Built schema normalization patterns to prevent automation pipelines from depending on brittle scraper output or raw vendor payloads
The Outcome
The result was a reusable security architecture for high-authority AI systems operating across browser and native desktop surfaces. It shows how powerful agentic automation can be made inspectable, governable, and useful without pretending the risk disappears.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Security Analytics
Signal investigation and event analysis
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Public-Safe Evidence
Shareable insights without sensitive data
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Dashboard Development
Operational and executive views
Executive Reporting
Security data translated for leadership
Key Deliverables
- •Browser-native agentic AI control-plane architecture
- •Chrome extension authority-boundary model
- •Persistent offscreen worker execution model
- •Local WebLLM and Transformers execution integration pattern
- •Rust/WASM shared engine strategy
- •Tauri sidecar authority and MITM-processing model
- •Request/response capture governance model
- •WebSocket stream processing and event-listener model
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.