Security

Like many technology organizations, when ChatGPT was publicly released, we wanted to compare its answers to those of a regular web search. We experimented by asking technical questions and requesting specific content. Not all answers were efficient or correct, but our team appreciated the ability to provide feedback to improve responses.

We then got more specific and asked ChatGPT for advice using Kubernetes. ChatGPT provided a list of 12 best practices for Kubernetes in production, and most of them were correct and relevant. But when asked to expand that list to 50 best practices, it quickly became clear that the human element remains extremely valuable.

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Devsecops company JFrog on September 13 introduced ML Model Management, a set of capabilities for the JFrog Software Supply Chain Platform designed to streamline the management and security of machine learning models.

Using ML Model Management and the JFrog Software Supply Chain Platform, organizations can manage their proprietary models in Artifactory and proxy the Hugging Face model repository to cache the open source AI models they rely on, bringing them closer to production and development and protected from deletion or modification.

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CIOs and CISOs have long grappled with the challenge of shadow IT—technology that is being used within an enterprise but that is not officially sanctioned by the IT or security department. According to Gartner research, 41% of employees acquired, modified, or created technology outside of IT’s visibility in 2022, and that number was expected to climb to 75% by 2027. Shadow IT can introduce a whole host of security impacts, for one primary reason: You can’t protect what you don’t know about.

Not surprisingly, we are seeing a similar phenomenon with AI tools. Employees are increasingly experimenting with the likes of ChatGPT and Google Bard to do their jobs. And while that experimentation and creativity can be a good thing, the problem is that these tools are being used without IT or security’s knowledge.

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