Senior Software Engineer · Cloud & Security Architect · DevOps Leader
I build secure cloud, compliance, and AI systems — and document the evidence that they work.
GitHub · LinkedIn · npm · MCP Registry
- About Me
- Current Focus
- Selected Work
- MCP Servers
- Open Source
- Tech Stack
- Let's Connect
- Repository Verification
I work at the intersection of cloud security, federal compliance, and applied AI. My public repositories are reference implementations built to be run, tested, and audited — not just read.
Recent work includes AEGIS (multi-cloud FedRAMP High, NIST 800-53 Rev. 5, DoD Cloud SRG, and Zero Trust controls), an AI Safety & Governance Platform, a NIST 800-53 evidence-driven scanner, a Secure Generative AI platform on AWS, and six MCP servers for agent observability, cost, data, evaluation, legal, and health workflows.
The larger reference implementations include a docs/evidence/verification-report.md with commands, test results, screenshots, and explicit non-goals. If a capability was not verified, it is labeled that way.
class Engineer:
def __init__(self):
self.focus = ["cloud security", "federal compliance", "AI governance"]
self.method = ["threat model", "automate", "test", "show evidence"]
self.stack = ["Python", "TypeScript", "Terraform", "AWS · Azure · GCP"]- Federal compliance automation — NIST 800-53 Rev. 5, FedRAMP High, CMMC 2.0 / NIST 800-171, OSCAL, and policy-as-code.
- AI governance and safety — model registry, RBAC, monitoring, privacy and regulatory checks, and secure RAG.
- MCP developer tooling — local-first servers for tracing, cost, data, evaluation, legal, and health workflows.
| Project | What it does |
|---|---|
| AEGIS | Multi-cloud federal security framework aligned to FedRAMP High, NIST 800-53 Rev. 5, DoD Cloud SRG, and NIST 800-207; Terraform modules plus authorization artifacts. Stack: Terraform · AWS/Azure/GCP · LocalStack |
| AI Safety & Governance | Full-stack model governance: JWT/RBAC, model registry, host monitoring, GDPR/HIPAA/CCPA checks, audit trail, and Docker deployment. Stack: Python · FastAPI · React · TypeScript · SQLite/PostgreSQL |
| NIST 800-53 Scanner | Evidence-driven Rev. 5 assessment: read-only collectors, versioned policy-as-code rules, OSCAL output, and Prometheus/Grafana monitoring. Stack: Python · Flask · OSCAL · Docker |
| Secure GenAI on AWS | Bedrock + RAG reference platform with Guardrails, PII redaction, WAF/GuardDuty, and SAM-local end-to-end tests. Stack: Python · AWS SAM · Terraform · Bedrock |
| ICRAF | Browser-only CMMC 2.0 / NIST 800-171 dashboard: 110-practice tracker, risk register, and IaC/CI control verifier. Stack: React 19 · TypeScript · Vite |
| Federal Document Triage | LangGraph workflow for document intake, classification, routing, and approval, with optional Bedrock/Comprehend/Textract integrations. Stack: Python · LangGraph · AWS |
Each repository states what is verified and what is not; see its docs/evidence/verification-report.md where present.
Also shipped:
- Label Check (live) — human-in-the-loop alcohol label verification.
- Enterprise Checklist Dashboard (live) — eight-domain progress tracker.
- Cloud Security Best Practices (live) — FedRAMP/NIST documentation hub.
- Mandarin Pathways (live) — Mandarin learning PWA and Flutter app.
Six local-first servers, published on npm and listed in the official MCP registry. Each repository documents its tools and configuration. Node.js 20.19+ is required (trace-inspector requires 22.13+, eval-runner 22.5+).
Run any package with npx -y <package>@latest (replace <package> with the server name).
| Server | What it does |
|---|---|
| mcp-agent-trace-inspector · npm | Local-first tracing, cost estimates, diffs, and audit logs for MCP agent workflows. |
| mcp-cost-tracker-router · npm | Offline token/cost tracking, budget alerts, and model-routing suggestions. |
| mcp-data-pipeline-connector · npm | Cross-source SQL over CSV/JSON, PostgreSQL, and REST via embedded DuckDB. |
| mcp-eval-runner · npm | YAML-driven regression tests for MCP servers and agent workflows. |
| mcp-legal-doc-analyzer · npm | Local clause extraction, risk flagging, compliance checks, and version diffs. |
| mcp-server-health-monitor · npm | MCP-native health checks, schema-drift detection, and local history. |
- Merged: microsoft/generative-ai-for-beginners #698 — grammar and function-calling documentation fixes.
- Proposed: microsoft/data-formulator #90 (Docker support), MetaGPT #1735 (Ollama URL wrappers), and ScoutSuite #1695 (recursion fix); these PRs were closed without merge.
| Area | Tools |
|---|---|
| Languages | Python · TypeScript · JavaScript · SQL · HCL · Bash |
| Cloud & platforms | AWS · Azure · GCP · Docker · Kubernetes · Terraform · GitHub Actions |
| Security & compliance | NIST 800-53 · FedRAMP · CMMC 2.0 · NIST 800-171 · Zero Trust · OSCAL · OPA · SBOM |
| AI/ML | PyTorch · TensorFlow · scikit-learn · LangGraph · Amazon Bedrock · RAG |
- GitHub: @dbsectrainer
- LinkedIn: donnivis-baker-cissp-pmp
- npm: ~dbsectrainer
- MCP Registry: published servers
The best way to reach me is through GitHub or LinkedIn.
Verification & maintenance commands
This repository is the source for my GitHub profile README. There is no application server or build step: GitHub renders README.md directly.
Prerequisites: Python 3.12+; Playwright 1.55+ for screenshots; Node.js 20.19+ only for the optional MCP package audit.
python3 -m pip install -r requirements-dev.txt
python3 -m playwright install chromium
make check # unit tests + README structure, assets, secrets, and links
make preview # render README through GitHub's Markdown API
make screenshots # capture desktop/mobile evidence with PlaywrightThe link and registry checks require network access. No secrets or environment variables are required. GITHUB_TOKEN is optional and only raises GitHub API rate limits for make preview.
Validation report, screenshots, and residual risks: docs/evidence/verification-report.md.
Secure by design. Verified by evidence.