I work in security engineering and incident response, focused on understanding how real systems fail under attack and analyzing complex telemetry to surface meaningful signal from noise.

My work sits at the intersection of security, data analytics, and applied AI, where investigation, statistical thinking, and automation come together to improve decision-making under real-world constraints.

• Analyzing high-volume telemetry to identify patterns, anomalies, and high-signal events
• Building data-driven workflows for investigation, triage, and decision support
• Applying machine learning for anomaly detection, classification, and prioritization in noisy, real-world datasets
• Developing LLM-powered capabilities for summarization, classification, and structured insight extraction
• Designing production-oriented systems: APIs, pipelines, and decision-support workflows under real-world constraints
• Improving observability, telemetry quality, and signal extraction in complex distributed environments
• Applying security-driven thinking, including threat modeling, adversary simulation, and resilient system design
• Designing policy-driven, auditable controls that integrate directly into engineering workflows

Data-driven system for security alert analysis and triage (Python, Jupyter)

• Applies text classification, feature extraction, and structured model evaluation to improve alert triage
• Designed to support analyst decision-making with transparent, reproducible workflows
• Focused on measurable signal quality and explainable outcomes over black-box automation

➡️ https://github.com/texasbe2trill/AlertSage

AI-assisted knowledge system for extracting insight from personal reading data (Python, Streamlit, NLP, LLMs)

• Transforms KoboReader.sqlite into structured, queryable intelligence across highlights, notes, and reading behavior
• Implements NLP and LLM-based pipelines for summarization, classification, and pattern detection
• Focused on surfacing patterns, themes, and insights from reading behavior
• Designed as a local-first system with transparent, explainable outputs

➡️ https://github.com/texasbe2trill/KoNotes

Basketball analytics project exploring player and team performance (R, data analysis)

• Analyzes player and team statistics to identify trends, efficiency patterns, and performance drivers
• Applies data exploration and visualization techniques to uncover actionable insights
• Focused on translating raw sports data into clear, interpretable analysis

➡️ https://github.com/texasbe2trill/hooplyticsR

Policy-as-code system for secure workflows and access decisioning (Go, CLI)

• Defines and enforces access policies across services and sensitive resources using version-controlled policy definitions
• Evaluates requests with deterministic outcomes (allow, deny, require_approval) for consistent, auditable decisions
• Standardizes how access and privilege boundaries are enforced across engineering environments
• Produces traceable decision artifacts supporting audit, compliance, and data-driven investigation
• CLI workflows for policy validation, simulation, and impact analysis before deployment

➡️ https://github.com/texasbe2trill/policyforge

Context-aware macOS security assessment tool (Python, CLI)

• Performs fast trust evaluation across applications, launch items, and system controls
• Reduces false positives by recognizing legitimate vendor and administrative patterns
• Built for practitioners who need accurate, explainable results under time pressure

➡️ https://github.com/texasbe2trill/macos-trust

• Languages: Python, Go, R, Bash, Swift
• AI Systems & Machine Learning: PyTorch, NLP, embeddings, LLMs, RAG, model evaluation, applied statistics
• Data & Analytics: Data exploration, statistical analysis, feature engineering, visualization, decision support
• Security Engineering: Threat modeling, incident response, adversary simulation
• Governance & Controls: Policy-as-code, control design, risk-based decisioning, auditability, compliance alignment (NIST, ISO, SOC 2)
• Systems: Linux, APIs, distributed systems, observability, telemetry, automation

I build data, security, and AI-driven systems with a focus on:

• Evidence-driven decisions — signals, models, and controls should be measurable, testable, and auditable
• Clarity from complexity — turning noisy data into actionable, trustworthy insight
• Engineering-aligned governance — controls should integrate into real workflows
• Operational resilience — systems must hold up during incidents, audits, and scale
• Practical simplicity — solutions should be understandable, enforceable, and maintainable