- AI engineering — designing and shipping LLM/agent features: tools/MCP connectors, RAG, prompts/
AGENTS.md, evals, and pre-/post-model validation gates - Published an 8-week AI Engineer Roadmap — project-driven path from Python to production RAG, agents, MCP, evals, and deployment (runs offline by default)
- AI security & governance — access control, app-scoped credentials/PATs, blast-radius analysis, threat overlays, and assessments against the NIST AI RMF Playbook
- Built custom AI security automations on Amazon Bedrock and configured Wazuh dashboards for detection / SOC visibility
- Authored a custom Linux Sysmon config for developers building with AI (Sysmon for Linux / eBPF telemetry for agent & MCP activity, with auditd companion coverage)
- Software development — TypeScript/Python systems with production habits: logging, monitoring, least privilege, and clear ownership — including AWS CDK infrastructure-as-code
- Comfortable from local-first / on-prem tooling to cloud and productized AI in real applications
- Open to collaboration on agent platforms, secure AI integrations, and governance automation
| Area | What I work on |
|---|---|
| AI Engineering | Agent runtimes, tool calling, RAG, model routing, instruction layers, eval harnesses |
| AI Security | Prompt injection (direct/indirect), tool-abuse containment, scoped tokens, output trust boundaries |
| Security Automations | Custom AI security workflows on Amazon Bedrock; Wazuh dashboards; Linux Sysmon configs for AI/dev toolchains (agents, skills, MCP) |
| Governance / RMF | Inventory → measure → manage workflows mapped to NIST AI RMF (GOVERN / MAP / MEASURE / MANAGE) |
| Development | Full-stack delivery, APIs, AWS CDK IaC, desktop/LAN services, CI, observability |
semantic-fraud-association · ai-engineer-roadmap · nist-ai-rmf-skill · sentinel-agent · soc-agent-skills · camera-weapon-detect · ai-standardization-matrix · player-and-base-live-data
| Project | Lens | Description |
|---|---|---|
| semantic-fraud-association | AI / Security / Payments | Agent skill for payment-fraud investigations and vendor-neutral SIEM detection design. Connects payments, IP locations, and contextual evidence; includes guarded Stripe CLI collection, execution receipts, and 18 synthetic regression tests. |
| ai-engineer-roadmap | AI · Education · Engineering | 8-week, project-driven curriculum from Python to production AI: RAG architectures, LangGraph, agents, MCP, evals, and deployment. Runs locally without a paid API. |
| sentinel-agent | AI · Security · SOC | SOC investigation agent. It accepts one alert, investigates with a closed set of tools, and writes a structured report. A human must approve the conclusion; it does not execute remediation. A local Floci walkthrough stores a synthetic GuardDuty finding and records approval only. Demo mode runs without an API key. |
| soc-agent-skills | AI · Security · SOC | Fail-closed agent skills: look up IOCs without detonating lures, ingest advisories only with tools that exist, RAG over an operator corpus, route alerts to Slack/webhooks |
| nist-ai-rmf-skill | AI · Security · Governance | Cursor skill for full 72/72 NIST AI RMF Playbook assessments of product AI/LLM systems (inventory, blast radius, PATs, validators) |
| ai-standardization-matrix | AI · Security · Evals | Maturity matrix for inspectable AI automation (schema, tools, RAG, bounds, human review). Walkthrough: github.io |
| camera-weapon-detect | AI · CV · Ops | Local camera review pipeline with YOLO (optional LLM crop review) |
| player-and-base-live-data | Development · Systems | Live JSON telemetry stream for Arma Reforger server state |
| Linux Sysmon for AI toolchains | AI · Security · Endpoint | Custom Sysmon for Linux config + viewers/bridge patterns for developers building with AI (process/network tripwires for agents & MCP; auditd companion for sensitive reads) |
Standards credit: assessments and governance work build on NIST AI RMF · AI 100-1 · Playbook · AI 100-2 · AI 600-1. Independent project — not NIST-endorsed.

