David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · CONFIDENTIAL AI-NATIVE PRODUCT TEAM
Confidential AI-Native Product Team
AI Product Security Control Plane
A compact methodology for connecting AI inventory, threat modeling, prompt injection, agent permissions, RAG authorization, AI supply chain, evidence,...
Framed AI product security as a product-control problem and translated AI risk categories into evidence, backlog, and governance language that product and engineering teams can actually use.
Client
Confidential / AI-Native Product Team
Engagement Type
Consulting / Methodology Design
Period
2025–2026
Role
AI Product Security Architect / Advisor
Focus Areas
AI Inventory, Threat Modeling, Prompt Injection, Agent Permissions
The Research Narrative
Strategic Problem
The challenge was to make AI security operational: turn abstract risk categories into decisions, owners, evidence, and backlog items that product and engineering teams can execute on.
What David Did
The control plane maps AI risks to owners, evidence, and backlog items that teams can execute.
What Became Clearer
The result is a compact, consulting-ready methodology that product and engineering teams can apply directly.
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
AI-native teams need a way to govern product risk without losing engineering velocity.
The Challenge
The challenge was to make AI security operational: turn abstract risk categories into decisions, owners, evidence, and backlog items that product and engineering teams can execute on.
What I Did
The control plane maps AI risks to owners, evidence, and backlog items that teams can execute.
- •Framed AI product security as a product-control problem
- •Converted risk categories into evidence and backlog language
- •Designed the framework for consulting, workshops, and public reuse
- •Kept caveats visible and avoided unsupported maturity claims
The Outcome
The result is a compact, consulting-ready methodology that product and engineering teams can apply directly.
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
Dashboard Development
Operational and executive views
Public-Safe Evidence
Shareable insights without sensitive data
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Executive Reporting
Security data translated for leadership
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Key Deliverables
- •AI product security control-plane framework
- •AI inventory and threat-modeling language
- •Prompt injection and agent-permission control language
- •RAG authorization and AI supply-chain control mapping
- •Evidence and governance backlog translation
- •Consulting-ready case-study JSON
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.