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:
- aimoderationtools.com
- aisecbench.com
- aisecreviews.com
- bestaisecuritytools.com
- bestllmscanners.com
- guardml.io
About This Publication
AI Defense covers the engineering side of securing AI systems — guardrail architecture, classifier ensembles, model hardening, output filtering, and the detection logic that actually catches adversarial inputs in production.
Security engineers, ML platform teams, and blue teamers responsible for deploying AI systems safely. The focus is on implementation: concrete architectures, measurable detection rates, and honest failure analysis.
What we cover
- Guardrail design and LLM safety layer architecture
- Adversarial input detection and classifier ensembles
- Model hardening and robustness techniques
- Output filtering and policy enforcement
- MITRE ATLAS-aligned threat modeling for AI systems
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