In this talk, we will explore various strategies and best practices when rolling out and integrating security into DevOps at scale. We will dive deep into scenarios and roadmaps that allow teams to collaborate effectively and automate security processes without slowing down the development work.
While a lot of organizations utilize 3rd-party APIs and services to build generative AI-powered applications, we are seeing more teams set up their own self-hosted large language models (LLMs). In this session, we will discuss various best practices when building and managing self-hosted LLMs.
One of the practical techniques to secure self-hosted LLMs involves building a vulnerability scanner that checks for vulnerabilities such as prompt injection. In this session, we will discuss how to build a custom scanner to help teams identify security issues specific to their self-hosted LLMs.
More companies globally have started to utilize Retrieval Augmented Generation (RAG) to significantly enhance their AI-powered applications. In this session, we will take a closer look at how RAG works, and we will demonstrate how to implement a RAG-powered chatbot using Python.
In this talk, we will dive deep into the best practices and strategies for securing Infrastructure as Code (IaC.) We'll focus on the relevant techniques for risk mitigation so that we'll be able to protect the infrastructure along with the applications and services running inside these resources.
In this talk, we will explore relevant strategies for securing JavaScript applications. We will dive deep into the various practical solutions when dealing with the different threats, risks, and issues involved building and deploying JavaScript applications.
In this talk, we will discuss several practical strategies for optimizing incident response workflows and leveraging AI-powered solutions for intelligent decision-making. We'll dive deep into how each of these strategies and solutions can ultimately build resilience in incident management processes.
Manually performing security operations and auditing procedures on a regular basis take a lot of skill, time, and discipline. In this session, we will talk about how to design and build different security tools and DevSecOps pipelines to automate different security tasks and responsibilities.
When dealing with machine learning (ML) requirements, most teams generally start using Python or R due to the number of references available using those languages. However, there are cases where companies do not have the bandwidth and budget to learn a new language on top of JavaScript especially if JavaScript and Node.js are being used in most deployed systems. That said,...
Designing and building machine learning systems require a lot of skill, time, and experience. Data scientists, developers, and ML engineers work together in building ML systems and pipelines that automate different stages of the machine learning process. Once the ML systems have been set up, these systems need to be secured properly to prevent these systems from being hacked and compromised....
Taking care of the overall security of systems and applications running in cloud environments is not easy. Manually performing security operations and auditing procedures on a regular basis take a lot of skill, time, and discipline. At the same time, auditing the different processes, systems, and applications used by organizations involves the usage of several tools. For example, scanning...
It is not an easy task to manage state in evolving web applications. We will talk about the different solutions when dealing with state management in React, Angular, and Vue.js apps. These frameworks and libraries have their own ways of managing state. We will talk about the similarities of these options but we will discuss in detail the major differences as well. Careful planning...
Managing infrastructure resources in the cloud becomes more challenging once we start to deal with significantly more resources and complex integration requirements. In this session, we will discuss the different solutions when dealing with production SRE requirements for Kubernetes in the cloud.
It is not an easy task to design and build systems in the cloud that involve Machine Learning and Data Science requirements. It also requires careful planning and execution to get different teams and professionals such as data scientists and members of MLOps teams to follow certain processes in order to have a sustainable and effective ML workflow. In this talk, I will share the different...
It is not an easy task to design and build systems that involve Machine Learning and Data Science requirements. In addition to this, managing the complexity of intelligent systems requires careful planning and execution. In this talk, I will share the different strategies and solutions on how to design, build, deploy, and maintain complex intelligent systems and workflows. I will discuss how...
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