How can developers use AI securely in their tooling and processes, software, and in general? Is AI a friend or foe? Read on to find out.

Artificial intelligence (AI) has seamlessly woven itself into the fabric of our digital landscape, revolutionizing industries from healthcare to finance. As AI applications proliferate, the shadow of privacy concerns looms large. The convergence of AI and privacy gives rise to a complex interplay where innovative technologies and individual privacy rights collide.

How is generative AI transforming trust? And what does it mean for companies — from startups to enterprises — to be trustworthy in an increasingly AI-driven world?

Part of being a part of the cybersecurity industry means looking ahead to the future and anticipating what’s to come. For most of us, we should expect a 2024 that is largely dominated by AI discussion. With the cybersecurity industry growing rapidly, AI is at the forefront of every organization’s cyber plans and plays an integral role in all technological advances.

“Not another AI tool!” Yes, we hear you. Nevertheless, AI is here to stay and generative AI coding tools, in particular, are causing a headache for security leaders. We discussed why recently in our Why you need a security companion for AI-generated code post. Purchasing a new security tool to secure generative AI code is a weighty consideration. It needs to serve both the needs of your security team and those of your developers, and it needs to have a roadmap to avoid obsolescence.

Scientific progress in AI and downstream innovation to solve concrete real-world problems is part of a greater movement toward inventing Artificial General Intelligence (AGI). Broadly speaking, AGI is defined as an intelligent agent that can emulate and surpass human intelligence. Today, we are already familiar with incomplete forms of AGI: Despite these promising innovations moving from the scientific domain to consumer marketplaces, we are still far from achieving AGI.

Splunk Enterprise and Splunk Cloud Platform, along with the premium products that are built upon them, are open platforms, which allow third party products to query data within Splunk for further use case development. In this blog, we will cover using Amazon SageMaker as the ISV product using the data within Splunk to further develop a fraud detection use case to predict future risk scores.

Many are speculating that at long last, OpenAI’s GPT store is set to go live this week. GPT builders and developers received an email on January 4th notifying them of the launch, which has been rumored for months, and likely only delayed due to the drama that has taken place at the company. This blog will summarize what this means for citizen development and how security teams should approach this new technological breakthrough from the AI giant.

You may know Cloudflare as the company powering nearly 20% of the web. But powering and protecting websites and static content is only a fraction of what we do. In fact, well over half of the dynamic traffic on our network consists not of web pages, but of Application Programming Interface (API) traffic — the plumbing that makes technology work.

AI and cybersecurity are top strategic priorities for companies at every scale — from the teams using the tools to increase efficiency all the way up to board leaders who are investing in AI capabilities.