# S12 Bonus: Dr. Aqib Rashid, Glasswall

Page: https://stenobird.com/podcast/code-story/s12-bonus-dr-aqib-rashid-glasswall
Text version: https://stenobird.com/podcast/code-story/s12-bonus-dr-aqib-rashid-glasswall.md
Podcast: [Code Story: Insights from Startup Tech Leaders](https://stenobird.com/podcast/code-story)
Published: 2026-04-09T10:00:07+00:00
Episode link: https://codestory.co/podcast/bonus-dr-aqib-rashid-glasswall/
Audio file: https://pdst.fm/e/pscrb.fm/rss/p/audio4.redcircle.com/episodes/54858ca4-497d-4ab1-b26d-47e70711f8fe/stream.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/code-story/episodes/s12-bonus-dr-aqib-rashid-glasswall
Duration seconds: 1790

## Resource

Dr. Aqib Rashid explains how to transition machine learning research from a PhD thesis into a commercially viable cybersecurity product. He details the engineering rigor required to build scalable, trustworthy AI pipelines for malware detection.

## Highlights
- Main idea: Moving from academic proof-of-concept to a production-grade ML development lifecycle (MLDLC) requires rigorous engineering and automated experimentation
- Practical takeaway: To build a sustainable AI product, start with a repeatable methodology for one file type (like PDF) before attempting to scale across all formats
- Failure mode: Building impressive models that perform well on benchmarks but fail under real-world adversarial conditions
- Practical takeaway: Prioritize building products that earn user trust through explainability and usability rather than just technical complexity
- Main idea: Successful engineering teams should prioritize genuine curiosity about the problem domain over mere proficiency in specific ML frameworks

## Topics

Cybersecurity, Machine Learning, Malware Detection, Product Development, Artificial Intelligence, Software Engineering, Startups, MLOps

## Chapters
- 1:00 — Defining the MVP: Dr. Rashid discusses the initial goal of using CDR telemetry to improve malware detection performance.
- 6:40 — The Power of CDR: An exploration of Content Disarm and Reconstruction (CDR) as a foundation for file analysis.
- 9:20 — Bridging Research and Production: The challenge of implementing machine learning models into a commercial product roadmap.
- 12:00 — Achieving Commercial Viability: Balancing true positive rates with the need to minimize false alarms for enterprise customers.
- 17:40 — Scaling the Training Pipeline: Building infrastructure capable of processing millions of files for model training in hours.
- 20:30 — Hiring for Curiosity: Why domain knowledge and curiosity are more vital than technical skills when building a specialized team.
- 34:20 — Advice for Entrepreneurs: A final lesson on building products that deserve trust and avoiding easily replicable AI features.

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## Transcript

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