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Achievement: Arctic Code Vault ContributorAchievement: StarstruckAchievement: Pull Shark

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w8mej/README.md

SecureSQL — Digital Highways

John Menerick (W8MEJ)

Staff/Principal Security Engineer — AI/ML Confidential / Trusted Compute • Detection & Response • Integrity-first telemetry • Zero Knowledge Multiple Party Compute • Zero Trust

Cypherpunk pragmatist: I turn messy operational reality into resilient operational excellence.

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Creator note: I stream incident response labs + resilient comms builds.

⚡ What I do

  • Ship detection & response systems that hold up under real adversaries, real outages, and real constraints.
  • Build tamper-evident / privacy-preserving telemetry for regulated, high-sensitivity environments.
  • Secure AI/ML compute pipelines using strong isolation boundaries, policy enforcement, and verifiable integrity.

📌 Proof points (sanitized)

  • Reduced alert MTTR by ~78% by tightening detection quality, test coverage, routing, and on-call runbooks—paired with Zero Trust guardrails (identity-aware access, scoped credentials, and continuous verification) across global-scale, multi-cloud environments.
  • Led incident response for events impacting 1,000s–1,000,000s of services and 10K–90M+ users (severity triage → containment → postmortem-driven hardening), including compromised-credential and trust-boundary failure modes typical of Zero Trust threat models.
  • Built detections covering 20,000+ tactics/behaviors across endpoint + on-prem + identity + cloud telemetry, with measurable precision/recall tuning—plus privacy-preserving correlation approaches (tokenization, secure aggregation, and ZK/MPC-inspired analytics) to enable cross-domain insights without raw-data exposure.

🧭 Right now

  • Building: MPC/ZK integrity primitives, IR workflows, emergency ops comms
  • Learning: Morphogenesis & developmental dynamics
  • Exploring: non-terrestrial edge compute (NTN / satellite) + constrained environments

🧪 Featured builds

  • zk-Autograd — verifiable / integrity-minded zero-knowledge multi-party compute for ML training experiments
  • Mimir — security research + systems thinking, packaged as a real repo
  • GhostTelemetry — tamper-evident telemetry pipelines & trust primitives
  • persuadable-defender — adversarial/behavioral security experiments with assured agentic alignment
  • InfoSec Blueprints — practical, copy/paste-ready security playbooks & blueprints

🛠️ Tools I actually reach for

🎙️ Community / signal

📫 Contact

Pinned Loading

  1. ProofOfCoffee ProofOfCoffee Public

    A dangerously functional collection of experiments. This repository exists at the intersection of “I wonder if this works” and “oh no, it worked”. Expect questionable Terraform, overly caffeinated …

    3

  2. InfoSec-Blueprints InfoSec-Blueprints Public template

    Essential playbooks & runbooks for cybersecurity operations. A dynamic resource for security pros to navigate digital threats, with best practices, incident management protocols, and community-driv…

    Jupyter Notebook 13 3

  3. TheTorProject/lepidopter TheTorProject/lepidopter Public

    lepidopter: raspberry pi image for conducting OONI network measurements

    Python 51 20

  4. Threat_Modeling_Bank Threat_Modeling_Bank Public

    A curated threat modeling library collection

    24 4

  5. ThreatPlays ThreatPlays Public

    Sharing Threat Hunting runbooks

    25 7

  6. gyoisamurai/GyoiThon gyoisamurai/GyoiThon Public

    GyoiThon is a growing penetration test tool using Machine Learning.

    Python 801 304