Rise of the Machines: Achieving Data Security and Analytics with AI
summary
CISO-led program removes production records from dev/test by generating production-like synthetic datasets using tools like Memesis, Gretel synthetics and algorithms GAIN/GANs, VAEs and SMOTE run from Jupyter/Python; implementation lets researchers access data without legal agreements, enabling monetization tiers and cutting breach severity in analytics environments to near zero. Estimated exposed liability falls from roughly $1.3B across apps; vendor agreements allow model inspection for ethical AI validation, and the pipeline is live and already reducing sharing delays.
AI-written summary · about
chapters (19)
- 0:00Intro
- 0:31Disclaimer
- 1:01MARVEL vs DC
- 1:35The Hero's Journey
- 1:53The Quest
- 5:41Preparation
- 6:25Machine Learning, Artificial Intelligence, and Data (MAD) Tool Landscape
- 6:50Artificial Intelligence In the Context of Humans
- 10:34Synthesis from real data
- 11:37Synthesis without real data
- 14:05Algorithms for Generating Synthetic Data
- 17:24Applications of Synthetic Data
- 20:06Commonly Used Synthetic Data Resources
- 21:00Evaluating the Solutions
- 22:49The Abyss
- 25:18The Battle Begins!!
- 28:07Implementation
- 37:29Moravec's Paradox
- 38:19Journey's End
related talks
49mGenAI and Cybersecurity: Safeguarding the Digital Age
36mArchitecting Data: A Deep Dive Into the world of Synthetic Data
28mInsights, Not Secrets: How Synthetic Data Empowers Privacy
20mSynthetic Data for Privacy, AI Fairness and Democratizing Data Access
18mCommit Secure AI: Embedding Security in Code, Pipelines, and Production
26mSafeguarding AI: How to Manage AI and its Potential Risks
20mSynthetic Data: The new Super Power for AI
18mAI Is Coming For Our Secrets