David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · INTERNAL RESEARCH
Internal Research
AI Security Job Market Dataset & Analytics Engine
A labor-market intelligence engine analyzing AI security, product security, AppSec, governance, and emerging agentic-system roles across thousands of ATS...
Built a job-market analytics engine for AI security engineering using thousands of ATS job descriptions, role taxonomies, skill extraction, security-domain clustering, baseline comparisons, and report-ready research narratives....
Client
Internal Research / AI Security Report
Engagement Type
Internal research and product buildout
Period
2025–2026
Role
AI Security Researcher / Dataset Architect / Labor Market Intelligence Analyst
Focus Areas
AI Security Job Market, ATS Job Description Analysis, Role Taxonomy, Skill Extraction
The Research Narrative
Strategic Problem
The challenge was turning job descriptions into evidence. The system needed to normalize postings, extract skills, classify responsibilities, compare baselines, and separate genuine role change from marketing...
What David Did
David built a dataset and analytics model around thousands of ATS job descriptions, with taxonomies for AI security, product security, AppSec, governance, cloud security, and related...
What Became Clearer
The project creates a research and product foundation for the State of AI Security Engineering Report, recruiter intelligence, candidate positioning, job-fit scoring, consulting offers,...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
AI security hiring is noisy. Titles are unstable, job descriptions are inflated, and companies often mix AppSec, product security, governance, ML security, cloud security, and agentic-system risk into the same role.
The Challenge
The challenge was turning job descriptions into evidence. The system needed to normalize postings, extract skills, classify responsibilities, compare baselines, and separate genuine role change from marketing language.
What I Did
David built a dataset and analytics model around thousands of ATS job descriptions, with taxonomies for AI security, product security, AppSec, governance, cloud security, and related hybrid roles.
- •Collected and normalized a large corpus of ATS job descriptions across AI security, product security, AppSec, governance, cloud security, and adjacent roles
- •Designed role taxonomies for AI Product Security Engineer, AI Security Engineer, Product Security Engineer, AppSec Engineer, AI Governance, ML Security, Detection Engineering, and related hybrid roles
- •Created extraction workflows for skills, tools, frameworks, responsibilities, seniority signals, compliance language, product-security requirements, and AI-specific security concepts
- •Compared AI+Security roles against Product Security and AppSec baselines to identify what is genuinely new versus relabeled legacy security work
- •Analyzed role-title fragmentation, skill inflation, governance language, agentic-system references, model-risk terminology, secure SDLC requirements, and customer-trust signals
- •Created concepts such as the Frankenstein Role, Chimera Spec, Unicorn Index, Skill Washing, Evidence Gap, and Probability Pivot to make market patterns memorable
- •Built dataset structures that support report charts, executive summaries, recruiter insights, candidate positioning, and portfolio narratives
- •Connected job-market intelligence to the user's ATS job intelligence platform, psychographic fit engine, recruiting intelligence system, and AI security report strategy
The Outcome
The project creates a research and product foundation for the State of AI Security Engineering Report, recruiter intelligence, candidate positioning, job-fit scoring, consulting offers, and AI security market education.
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
Telemetry Normalization
Consistent and trusted data
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
Executive Reporting
Security data translated for leadership
Operational Reporting
Actionable views for security operations
Key Deliverables
- •AI security job-description dataset
- •Role taxonomy for AI security and product-security roles
- •Skill and responsibility extraction model
- •AI+Security versus Product/AppSec baseline comparison
- •Role archetype classification
- •Skill-washing and role-overload analysis
- •Report chart and narrative data model
- •Recruiter intelligence dataset foundation
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.