Description

In this episode, hosts Nic Fillingham and Natalia Godyla speak with Arie Agranonik, a Senior Data Scientist in the Microsoft Defender ATP Research team, about building models using deep learning to protect against malicious attacks. It’s complicated work, requiring huge computing power and even larger amounts of data, and it could be the future of threat protection. 

They also speak with Holly Stewart, a Principal Research Lead at Microsoft, on how building a security team with different perspectives helps to better understand and stop threats. Plus, her journey from the Peace Corps to Microsoft, and how that informs her decision-making. 

In This Episode, You Will Learn:  

• The difference between deep learning, machine learning and AI 
• Why it’s so difficult to program a computer to think like a human 
• How adversarial models learn from each other to prevent attacks 
• Why the best security teams are made up of those with different perspectives 
• How data science can train machines to find things humans were not thinking about 

Some Questions We Ask:  

• What is deep learning?
• Does a neural network mimic the way the human brain functions?
• How are behavioral observations evolving to combat sophisticated attacks? 
• How do AI and ML factor into solving complicated security problems?
• What’s next on the horizon for data science? 

Resources:

Microsoft Security Blog 

Arie’s blog post

View Arie Agranonik on LinkedIn

View Nic on LinkedIn 

View Natalia on LinkedIn 

Related:

Listen to: Afternoon Cyber Tea with Ann Johnson

Listen to: Security Unlocked: CISO Series with Bret Arsenault 

Security Unlocked is produced by Microsoft and distributed as part of The CyberWire Network.