David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · CONFIDENTIAL CONSULTING PROJECT
Confidential Consulting Project
B2B API Repository Security & Refactoring Automation
A pre-Splunk consulting project applying SAST, DAST, linting, secrets extraction, AST transforms, automated refactoring, and genetic-programming research...
Conducted a consulting and research project focused on large-scale analysis and automated refactoring of GitHub-hosted B2B API repositories, including Salesforce, Marketo, Mailchimp, and similar API ecosystems. The work combined...
Client
Confidential / Independent Consulting
Engagement Type
Consulting / Research Buildout
Period
Circa 2012; exact dates require confirmation
Role
Security Automation Consultant / JavaScript Refactoring Engineer / API Integration Researcher
Focus Areas
API Security, B2B API Ecosystems, GitHub Repository Mining, Static Analysis
The Research Narrative
Strategic Problem
The source material was messy. Salesforce, Marketo, Mailchimp, and similar API ecosystems exposed developers to loose JavaScript examples, duplicated code, weak structure, hardcoded secrets, and inconsistent...
What David Did
David applied SAST, DAST-style review, linting, secrets extraction, AST parsing, and automated refactoring to hundreds of GitHub-hosted B2B API repositories. The transformation goal was to...
What Became Clearer
The project created a deep pre-Splunk foundation in program analysis, repository mining, secrets detection, AST transformation, and automated remediation. It helps explain why David's...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
Before modern AI coding agents, B2B API ecosystems already had a version of the same problem: thousands of examples, SDK snippets, partial integrations, and GitHub repositories that were useful but inconsistent, insecure, and hard to reuse.
The Challenge
The source material was messy. Salesforce, Marketo, Mailchimp, and similar API ecosystems exposed developers to loose JavaScript examples, duplicated code, weak structure, hardcoded secrets, and inconsistent model boundaries. The challenge was to analyze, clean, and transform that material at scale.
What I Did
David applied SAST, DAST-style review, linting, secrets extraction, AST parsing, and automated refactoring to hundreds of GitHub-hosted B2B API repositories. The transformation goal was to convert loose examples into cleaner JavaScript classes and MVC-style model structures.
- •Collected and reviewed hundreds of GitHub-hosted B2B API repositories, SDK examples, and integration samples
- •Applied SAST-style static analysis to identify unsafe coding patterns, insecure API usage, weak input handling, hardcoded credentials, and structural risks
- •Applied DAST-style thinking where API behavior, request/response flows, authentication patterns, and runtime assumptions needed validation
- •Used linting and code-quality checks to normalize style, identify brittle constructs, and prepare repositories for transformation
- •Performed secrets extraction and credential pattern detection across public repository material, with emphasis on hardcoded tokens, API keys, passwords, and environment leakage
- •Studied API examples from Salesforce, Marketo, Mailchimp, and similar B2B SaaS platforms to identify recurring integration patterns and model structures
- •Used AST-based analysis to parse loose JavaScript examples and infer candidate classes, API clients, data models, controllers, and reusable request methods
- •Automated refactoring of unstructured scripts into JavaScript class structures and MVC-oriented design, especially model-layer abstractions for API entities
The Outcome
The project created a deep pre-Splunk foundation in program analysis, repository mining, secrets detection, AST transformation, and automated remediation. It helps explain why David's later AI-security and code-generation work is grounded in practical static-analysis and refactoring experience rather than prompt-only automation.
Research Outcomes
Alert Trust
Reduced noise and improved signal quality for IAM and access-control alerts
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
SIEM Alert Debugging
Noise reduction and signal validation
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
Dashboard Development
Operational and executive views
Executive Reporting
Security data translated for leadership
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Public-Safe Evidence
Shareable insights without sensitive data
Key Deliverables
- •GitHub B2B API repository analysis workflow
- •SAST and DAST-style review patterns for API examples
- •Linting and code-quality normalization workflow
- •Secrets extraction and credential-pattern detection
- •AST parsing and transformation pipeline
- •Automated JavaScript refactoring patterns
- •JavaScript class generation from loose API examples
- •MVC-oriented model extraction and API-client structuring
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.