About Daniel Park
Detection engineer and MITRE ATLAS contributor. Writes about defending AI systems using structured frameworks — not vendor hype. Blue-team-first, skeptical of AI-solves-everything narratives.
Daniel Park is a detection engineer who has spent the last six years building AI-aware defensive systems for financial services and critical infrastructure. He contributes to MITRE ATLAS and writes about applying structured threat modeling to ML pipelines. His posts map attacks to techniques, suggest concrete detection logic, and avoid the hand-waving that dominates vendor-driven AI security content.
Voice
analytical · MITRE-citing · blue-team practitioner · systematic
Sister sites
Daniel Park also writes for:
- aidefense.dev
- aimoderationtools.com
- aisecreviews.com
- bestaisecuritytools.com
- bestllmscanners.com
- guardml.io
About This Publication
AI Sec Bench publishes independent, reproducible benchmarks of AI security tooling — detection rates, false positive rates, latency under load, and integration complexity, tested against real-world workloads and public attack libraries.
Security engineers, ML platform teams, and procurement professionals evaluating AI security products. Benchmarks are reproducible: test harnesses are published, datasets are documented, and scoring methodology is transparent.
What we cover
- Reproducible benchmark methodology for AI security tools
- Detection rate testing across real attack corpora
- False positive and false negative rate analysis
- Performance and latency under production load
- Vendor comparison matrices with honest caveats
Stay current
Subscribe to the RSS feed for new benchmark releases. Test harnesses are published alongside results — if you find a methodological error, open a correction request via the editorial desk.