David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · AI SECURITY LLC
AI Security LLC
AI Product Security in the Age of Mythos
A practical AI product-security framework for agentic systems, governance evidence, excessive agency, RAG authorization, and continuous threat modeling.
Created a flagship AI product-security framework explaining how agentic AI changes the product-security operating model: inventory becomes the first control, threat modeling becomes continuous, prompt injection becomes a...
Client
AI Security LLC / Independent Research
Engagement Type
Research Product
Period
2026
Role
Author / AI Product Security Architect
Focus Areas
AI Product Security, Agentic AI Threat Modeling, Prompt Injection as Product Security, Excessive Agency
The Research Narrative
Strategic Problem
The hardest part was avoiding another generic AI security checklist. The framework needed to explain why AI changes the product-security operating model and then translate that into concrete controls:...
What David Did
The work reframed AI security around product authority. Prompt injection became a product-security bug. Excessive agency became the new overprivileged service account. RAG became an...
What Became Clearer
The result is a reusable flagship asset for AI product-security advisory work. It supports portfolio storytelling, consulting offers, executive education, control-plane design, assessment...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
AI product security is becoming a product requirement, not an optional research topic. Agentic systems retrieve context, call tools, make decisions, and create runtime behavior that traditional AppSec programs were not designed to govern. Mythos was written to explain that shift in language product and security leaders can act on.
The Challenge
The hardest part was avoiding another generic AI security checklist. The framework needed to explain why AI changes the product-security operating model and then translate that into concrete controls: inventory, threat modeling, permissions, tool reach, context authorization, supply chain, evidence, and execution.
What I Did
The work reframed AI security around product authority. Prompt injection became a product-security bug. Excessive agency became the new overprivileged service account. RAG became an authorization system. Governance became useful only when it generated evidence and backlog movement instead of theater.
- •Framed Mythos as a capability threshold rather than a single product launch, emphasizing the structural security implications of agentic AI
- •Defined the product-security shift created by AI-assisted attackers and AI-assisted product features
- •Separated AI product-security concerns from generic model safety, traditional AppSec, and performative governance
- •Established inventory as the first control for AI systems, models, prompts, agents, tools, data flows, RAG sources, and automated actions
- •Recast threat modeling as a continuous process because prompts, context, tools, workflows, and retrieval behavior change faster than traditional release cycles
- •Positioned prompt injection as a product-security bug rooted in authority confusion, tool reach, and untrusted input handling
- •Defined excessive agency as the AI-era equivalent of an overprivileged service account
- •Explained why RAG and context systems must be treated as authorization systems rather than search conveniences
The Outcome
The result is a reusable flagship asset for AI product-security advisory work. It supports portfolio storytelling, consulting offers, executive education, control-plane design, assessment work, and job-market positioning around AI security leadership.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Executive Visibility
Built dashboards leaders could trust for decision-making
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Executive Reporting
Security data translated for leadership
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
Dashboard Development
Operational and executive views
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Key Deliverables
- •AI Product Security in the Age of Mythos framework
- •Long-form report / handbook-style content asset
- •Chapter structure and narrative architecture
- •AI product-security control-plane model
- •Prompt-injection-as-product-security-bug explanation
- •Excessive-agency control framing
- •RAG-as-authorization-system framing
- •AI supply-chain security section
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.