pymayfly
Identity-Per-Transaction credential library for regulated data pipelines — "like a mayfly, these credentials live for exactly one transaction." Published to PyPI.
View on GitHub →Staff Data Engineer · Founder/PI, Deterministic Systems Lab
Tristan McKinnon
I build zero-trust data pipelines for the most regulated environments in healthcare, fintech, and federal government — and speak and write about what actually breaks in production.
Featured Projects
See all →AetherForge
Generates Unreal Engine levels from natural-language prompts: a Go orchestration service turns intent into a deterministic scene recipe (Poisson-disk scatter + spatial-hash overlap rejection), paired with a UE5 editor plugin that streams the result into the viewport.
View on GitHub →ghost-driver
Security research demonstrating 96% re-identification of "anonymized" fleet telemetry via a multi-pass spatiotemporal sieve algorithm, with a synthetic dataset for reproducibility.
View on GitHub →Speaking Highlights
See all →PyCon US 2026 · May 16, 2026
Zero Trust in 200ms: Implementing Identity-Per-Transaction with Python and Serverless
Building a serverless data pipeline that satisfies FedRAMP High while handling Protected Health Information is typically a nightmare of encryption management and rigid access controls. This talk dissects a production federal life-sciences pipeline that replaces static service accounts with ephemeral Python logic: an identity broker that mints a unique, cryptographically scoped IAM credential for every file transaction and destroys it milliseconds later, a streaming de-identification layer built on Python generators and Microsoft Presidio to tokenize PII in-memory before it reaches the data lake, and structured logging patterns that produce immutable, audit-ready JSON trails — showing how Python can solve the "Non-Human Identity" crisis in high-stakes environments.
Learn more →IEEE BigDataSecurity 2026 · May 10, 2026
Zero-Trust Data Engineering: A Reference Architecture for Serverless, FedRAMP-High Healthcare Pipelines
As healthcare research migrates petabyte-scale genomic and clinical data to the cloud, traditional perimeter-based security models violate zero-trust principles and let a single compromised credential expose an entire data lake. This paper proposes a shift from identity-per-user to identity-per-transaction, presenting a serverless zero-trust reference architecture (AWS Lambda/Glue) that uses a just-in-time identity broker and a clean-room, streaming de-identification layer to scope permissions to the lifecycle of a single batch event. Evaluated in a production FedRAMP High environment, the architecture cuts the blast radius of a compromised identity by over 99.9% while preserving linear throughput scalability — proving that rigorous federal compliance doesn't have to come at the cost of cloud agility.
Learn more →USENIX PEPR '26 · June 1, 2026
The Disposable Identity: Eliminating Non-Human Identity Risk in Federal Healthcare Pipelines
Non-human identity — the long-lived, over-privileged service accounts powering automated pipelines — is the fastest-growing and least-examined attack surface in cloud data environments; in federal healthcare systems under FedRAMP High, a single compromised ingestion role can mean bucket-wide access for up to 90 days. This talk presents a production case study of an Identity-Per-Transaction (IPT) pipeline deployed for a federal life sciences agency that issues a unique, cryptographically scoped, ephemeral credential for every file-ingestion event and destroys it milliseconds later, including the operational realities of running it in production — latency, concurrency race conditions, and debugging credentials that no longer exist — plus an honest reckoning with the approach's limits: the identity broker remains a bounded but non-zero root of trust, and eliminating credential-mediated access risk is not the same as solving genomic data anonymization.
Learn more →