In modern network environments focused on cloud technology, organizations have undergone a significant transformation in the development and deployment of their IT assets. The introduction of cloud technology has simplified and expedited the deployment process, but it often lacks centralized change management. The cloud's shared responsibility model enables quick deployment and scaling but can pose security risks if not properly managed and understood.

DevSecOps is an impeccable methodology that combines development, operations (DevOps), and security practices in the Software Development Lifecycle (SDLC). In this methodology, security comes into play from the beginning and is a shared responsibility instead of an afterthought. However, with the ever-evolving digital landscape, and continuous use of third-party and open-source components, DevSecOps teams need to fortify this methodology to minimize the risk and make their software more resilient.

‍ This month's release rollup includes Egnyte's AI-driven document summarization, project dashboard for Android, and Smart Cache file download improvements. Below is an overview of these and other new releases. Visit the linked articles for more details.

We've reported on several Amazon scams, but for once, there is positive news. Amazon sent an email Thursday morning highlighting the top scams your users should watch out for: Prime Membership Scams Per Amazon, "These are unexpected calls/texts/emails that refer to a costly membership fee or an issue with your membership and ask you to confirm or cancel the charge.

In today's digital landscape, where data breaches and security threats are a constant concern, ensuring least privilege access is of utmost importance for companies operating in the cloud. Granting the principle of least privilege to employees helps minimize the risk of unauthorized access, accidental misuse, and insider threats. However, achieving and maintaining least privilege access can be challenging, often resulting in manual processes, delays, and potential security gaps.

Researchers from Carnegie Mellon University and the Center for A.I. Safety have discovered a new prompt injection method to override the guardrails of large language models (LLMs). These guardrails are safety measures designed to prevent AI from generating harmful content. This discovery poses a significant risk to the deployment of LLMs in public-facing applications, as it could potentially allow these models to be used for malicious purposes.