How can businesses build cloud applications that are both reliable and secure? Organizations face a dual challenge: they must develop scalable solutions and protect sensitive data. As more businesses migrate to cloud-based infrastructures, they must adopt strong cloud solutions development and security practices. These steps help them maintain operations and preserve customer trust. In this article, we guide you through proven strategies and best practices. We show how you select the right architecture and implement advanced security measures.
Cloud adoption is accelerating, but with it comes new security challenges. In this video, we discuss: The rise of multi-cloud and hybrid cloud strategies. Key security concerns like visibility gaps and the shared responsibility model. How to stay secure while trusting—and verifying—your cloud provider. Takeaways: Understand cloud trends, security risks, and your role in protecting your workloads.
Cloud exploitation grew 95% over the past year, with adversaries becoming even more focused and persistent. Watch how Falcon Cloud Security stops breaches with truly unified agent and agentless protection, from endpoint to cloud. CrowdStrike Falcon Cloud Security: ► Stop cloud breaches and consolidate disjointed point products with the world’s only CNAPP built on a unified agent and agentless approach to cloud security for complete visibility and protection.
Launched as an internal project by Spotify in 2016, Backstage was released under the Apache 2.0 open source license in 2020 to help other growing engineering teams deal with similar challenges. Backstage aims to provide a consistent developer experience and centralize tools, documentation, and services within a single platform.
Last week, Google launched Project Mariner, an AI Agent built for the browser, based on the updated Gemini 2.0. Project Mariner is built on top of Google Gemini and can be used to browse the web on your behalf, taking commands from within a Chrome extension and performing autonomous tasks on your behalf.
Gartner predicts that generative AI (GenAI) will become a critical workforce partner for 90% of companies by next year. In application development specifically, we see developers turning to code assistants like Github Copilot and Google Gemini Code Assist to help them build software at an unprecedented speed. But while GenAI can power new levels of productivity and speed, it also introduces new threats and challenges for application security teams.
Cybersecurity researchers are warning about a new breed of investment scam that combines AI-powered video testimonials, social media malvertising, and phishing tactics to steal money and personal data. Known as Nomani — a play on "no money" — this scam grew by over 335% in H2 2024, with more than 100 new URLs detected daily between May and November, according to ESET's H2 2024 Threat Report.
LLMs are based on neural network architectures, with transformers being the dominant framework. Introduced in 2017, transformers use mechanisms called attention mechanisms to understand the relationships between words or tokens in text, making them highly effective at understanding and generating coherent language. Practical Example: GPT (Generative Pre-trained Transformer) models like GPT-4 are structured with billions of parameters that determine how the model processes and generates language.