All Day DevOps - Securing Data with Local AI Model Execution

Abstract

In our digital world, keeping our data safe is a big concern. As we use more and more AI tools, we need to make sure our sensitive information stays private. This talk will focus on how we can use something called “local AI model execution” to help keep our data safe. We’ll focus on using pre-compiled models from Hugging Face, which makes this process easier. We’ll start by talking about why data security is so important when we’re using AI tools. Then, we’ll show you how to use Hugging Face to run AI models on your own computer, which can help keep your data more secure. But it’s not just about security. We’ll also show you some of the cool things you can do with Hugging Face, like creating pictures from text descriptions or even writing code! In the end, we’ll talk about how using local AI can help us get the benefits of AI while keeping our data safe. We’ll show you that with the right tools, anyone can start experimenting with AI in a safe and secure way. Related topics we’ll cover: - Why data security is important when using AI. - How to use Hugging Face to run AI models on your own computer. - Cool things you can do with Hugging Face, like creating pictures and writing code. - The future of safe and secure AI use

Rewanth Tammana

Senior Security Architect

Rewanth Tammana is a security ninja, open-source contributor, and an independent consultant. Previously, Senior Security Architect at Emirates NBD (National Bank of Dubai). He is passionate about DevSecOps, Cloud, and Container Security. He added 17,000+ lines of code to Nmap (famous as Swiss Army knife of network utilities). Holds industry certifications like CKS (Certified Kubernetes Security Specialist), CKA (Certified Kubernetes Administrator), etc. Rewanth speaks and delivers training at multiple international security conferences around the world including Black Hat, Defcon, Hack In The Box (Dubai and Amsterdam), CRESTCon UK, PHDays, Nullcon, Bsides, CISO Platform, null chapters and multiple others. He was recognized as one of the MVP researchers on Bugcrowd (2018) and identified vulnerabilities in several organizations. He also published an IEEE research paper on an offensive attack in Machine Learning and Security. He was also a part of the renowned Google Summer of Code program.