Are you tired of using security tools that generate endless results, making it impossible to identify actual risks? Do you struggle with inefficient prioritization due to a lack of context, making the process of assessing and remediating vulnerabilities a time-consuming nightmare? Look no further than JFrog’s Contextual Analysis, available as part of the “jf audit” command in the JFrog CLI.

Every organization has a process for building and releasing software. Smaller organizations may run a few automated tests before releasing, while larger organizations may have 100s of scans, validations, and approvals spanning everything from technical to legal. Whatever the process is, the end goal is the same: software that’s mature enough for release. The challenge is that this process is complicated, messy, and often created in an ad hoc way, changing as organizations evolve.

It’s no surprise that AI is rapidly becoming the most powerful tool for developers. The ease of use and accuracy of such tools can even generate code snippets for a website written entirely on a napkin! While the potential of AI is exciting, it is crucial to address the potential vulnerabilities that bad actors can exploit within AI systems.

Smart notifications and nudges are table stakes tools for developers looking to streamline their work and stay focused on building improvements. These automatic alerts are key to a more efficient workflow, freeing us from the burden of repetitive, overwhelming, and time critical tasks — aka, toil.

For DevOps, 2023 is the year to reduce tool sprawl and start tool consolidation efforts. Sprawl is often seen as a natural result of the flexibility and empowerment of dev teams to choose their own tools, but organizations now understand the need for a single, streamlined system. While flexibility to choose the right tool for the job has enabled teams to move quickly, the result is a complex web of systems and processes to deliver software.

Amid an AI boom and developing research, machine learning (ML) models such as OpenAI’s ChatGPT and Midjourney’s generative text-to-image model have radically shifted the natural language processing (NLP) and image processing landscape. Due to this new and powerful technology, developing and deploying ML models has quickly become the new frontier for software development.

In an ideal world CI pipelines would never fail and deployments would be easy to navigate. The reality is that the journey from commit to production can fail in subtle ways that can be hard to understand. And this problem is multiplied by the number of pipelines in your system.