Faiz Gouri is a Lead AI Engineer at Microsoft, specializing in AI-driven cybersecurity for large-scale cloud infrastructure. He works on real-time anomaly detection and threat mitigation and is an active IEEE researcher with influential papers on optimizing distributed systems. His work sits at the critical intersection of cutting-edge AI and enterprise-level security.

In this episode, Faiz explains how his role at Microsoft organically combined AI, machine learning, and cybersecurity, emphasizing that security is a built-in feature, not an enhancement. He breaks down the high-level architecture of a real-time AI anomaly detection system, using a practical example of detecting suspicious login attempts from different locations. Faiz discusses his IEEE research on adaptive indexing, where machine learning dynamically adjusts database indexing based on query patterns, leading to significant performance improvements in distributed systems handling petabytes of data. He also explores the balance between deterministic rules and probabilistic AI in high-stakes cybersecurity, noting the critical importance of real-time threat mitigation to prevent costly downtime.

Faiz identifies data privacy in training models as a major pitfall for data scientists, using the example of AI chatbots in healthcare. For students and early-career professionals, he recommends cultivating intense curiosity and a commitment to continuous learning above any single technical skill. Reflecting on his own career, Faiz shares the advice to "take notes of everything" to retain knowledge in a rapidly evolving field. He expresses hope for AI's potential to create a more secure digital future, provided that security is core to its design. Finally, Faiz defines innovation as building something novel to solve a problem, often by drawing inspiration from and combining existing systems in unique ways.

In this episode, you’ll discover:

· How AI, machine learning, and cybersecurity converge in large-scale cloud infrastructure.

· The high-level architecture of real-time AI anomaly detection systems.

· How adaptive indexing with machine learning optimizes distributed databases.

· The balance between rule-based systems and AI in high-stakes security.

· The critical challenge of data privacy when training AI models with sensitive information.

· Why curiosity and continuous learning are the most important skills for an AI career.

· The career-defining advice to "take notes of everything" to retain knowledge.

· The potential of AI to create a more secure digital future.

· Faiz's definition of innovation as building novel solutions inspired by existing systems.

Connect With Faiz Gouri:

· LinkedIn: https://www.linkedin.com/in/faizgouri/

Chapters:

00:00 Welcome Faiz Gouri: Lead AI Engineer at Microsoft

01:51 The Intersection of AI, Machine Learning, and Cybersecurity at Microsoft

04:13 Architecture of a Real-Time AI Anomaly Detection System

07:08 IEEE Research: Adaptive Indexing for Distributed Systems

09:43 Balancing AI and Rule-Based Systems in High-Stakes Security

11:34 AI vs. Traditional Systems in Threat Detection

12:38 Major Pitfall: Data Privacy in Training Production AI Models

15:28 Foundational Skill for an AI Career: Curiosity and Continuous Learning

17:35 Career Advice: The Importance of Taking Notes

19:35 The Future of AI in Creating a Secure Digital World

20:37 Innovation Defined: Building Novel Solutions from Existing Inspiration

21:41 Connect with Faiz Gouri on LinkedIn

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