Protecting ML models will secure the supply chain, JFrog releases ML security features 

Cyber attackers have gotten really good at messing with software supply chains, especially when they find weak spots in open-source libraries. But here’s the thing: companies haven’t been quick enough to protect themselves.

Chris Krebs, the first head honcho of the U.S. Cybersecurity and Infrastructure Security Agency (CISA), talked about this at the BlackHat conference. He basically said, “If you’re making software, you’re painting a big bullseye on yourself.” The White House also joined in, saying they want a national cybersecurity plan that focuses on being tough online and makes sure software companies keep their products safe.

Security gets traded for speed – even with new ML model development

DevOps teams are feeling the heat to churn out more applications containing nifty machine learning models in record time. Why? Well, it’s all about boosting those digital-first profits and keeping customers happy.

But here’s the catch: those security checks we’re supposed to do often take a back seat because we’re racing against the clock to get our code out there. Imagine this – in a big company worth $600 million, the average DevOps crew is juggling a whopping 250 projects at once. And guess what? More than 70% of them are all about making sure our digital customers have the best experience and stay safe.

So, security sometimes gets the short end of the stick because we’ve got this massive backlog of cool new apps and ML stuff that needs to see the light of day ASAP. Plus, our security tests are like these distant relatives who barely talk to our DevOps team, and our engineers aren’t exactly pros at making their code super secure right from the get-go.

Sure, we love using open-source code because it saves us time and money, but it’s not all sunshine and rainbows. You see, a whopping 97% of our code is made up of open-source stuff, and a staggering 81% of it has at least one weakness that could be exploited. And wait, there’s more – over half of the code we looked at had some issues with licensing, and most of it hadn’t seen an update in four long years. That’s the reality we’re dealing with in the world of DevOps.

JFrog’s latest release goes all-in on protecting ML models during the development

JFrog, a big player in the world of making sure software supply chains stay safe for DevOps, knows all too well about the challenges we face. Today, they’ve dropped some exciting news at their 2023 swampUP Conference, and the star of the show is their new goodies in ML Model Management.

So, what’s the buzz? Well, they’ve cooked up some cool stuff like checking those machine learning models to make sure they’re following the rules, sniffing out any bad apples in the model bunch, and making sure these models get delivered right alongside your regular software.

Here’s what Yoav Landman, the brain behind JFrog, had to say about it: “Right now, folks like Data Scientists, ML Engineers, and DevOps teams don’t really have a common playbook for getting their software out there. This can lead to some bumpy rides, disagreements between teams, and a lack of clear rules when it comes to managing and keeping everything on the straight and narrow.”

He goes on to explain that machine learning models are like puzzle pieces that need more than just themselves to work correctly. They often rely on things like Python and other bits and pieces, and they usually get served up in these nifty Docker containers. Now, JFrog is the go-to choice for handling artifacts and making sure everything is secure in the world of DevSecOps. And guess what? Data wizards and software whizzes already love using JFrog. So, this new release is like the next step in their journey. It’s all about bringing machine learning model management, security, and playing by the rules into one neat package. This way, they can help folks deliver trustworthy software in a big way, especially in the age of AI.

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JFrog has also rolled out a spanking-new security platform that’s got your back from the very start of the software creation journey all the way to the finish line. We’re talking about everything from writing code to running your software.

Let me break it down for you. The fresh features include SAST scanning, an Open Source Software (OSS) catalog that’s now part of JFrog Curation, and they’ve even got security locked down for those fancy machine learning models. But wait, there’s more! They’ve beefed up their game with some cool stuff like release lifecycle management. This helps you keep a close eye on your software bundles, and they’ve turbocharged their DevOps features, making release bundles super stable and unchangeable.

Now, JFrog has a game plan, and it’s all about bringing the entire software development process under one roof. They want everything to be smooth sailing from start to finish. And guess what? They’ve got the results to back it up. Just look at Hitachi Vantara, for example. JFrog Artifactory has become the go-to “source of truth” for handling software bits and pieces all across the organization. They’ve also got JFrog Xray making sure everything is super secure. And here’s the kicker – by spreading out the key repositories to different locations, JFrog helped Hitachi Vantara speed up their work across multiple sites and put security front and center right from the beginning. It’s like a dream come true for anyone in the software game!

Getting scaling right is core to securing every phase of ML model development

What really stands out in JFrog’s latest announcements is how they’re stepping up the game when it comes to security and code reliability. They’re not just focusing on one part of the software journey; they’re covering the whole shebang – from the moment someone types in that first line of code, all the way through building, testing, deploying, and running those machine learning models.

Jim Mercer, a wise Research Vice President in the world of DevOps and DevSecOps at IDC, hit the nail on the head when he said, “Getting those ML models into action can be a real head-scratcher. And even when they’re up and running, there are headaches like model performance, model drift, and bias to deal with.” That’s where JFrog’s magic comes in. They’ve got this one-stop-shop, a single system that keeps things ticking. It takes care of everything – from building and managing ML models to making sure everything else that goes into your apps is safe and sound. It’s like a super-efficient way to make everything run like clockwork.

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Now, here’s the kicker – JFrog’s team of DevOps champs, engineers, and product gurus have gone above and beyond. They’ve thrown in some fancy AI and ML tricks to make sure their platform is top-notch. It’s all about making sure the rules are followed, the code is squeaky clean, developers are super productive, and any lurking threats are sniffed out and dealt with. It’s like they’re ticking all the right boxes that the bigwigs in the tech world (CISOs, CIOs, and the folks in the boardroom) look for when it comes to guarding their precious CI/CD pipelines and processes. It’s quite an achievement!

ML model security is a moving target that demands scalable platforms

The world of machine learning is like a wild frontier where threats just keep coming faster and faster. Bad actors out there want to turn AI into a weapon, and they’ll seize every opportunity to do so. And guess what? All those weak spots in software supply chains are like an open door for them. It’s not just a security issue; it’s also messing with the efficiency of teams trying to create and launch ML models for everyday use.

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Now, here’s where JFrog steps in with its forward-thinking approach. They’ve built a platform that’s got all the key elements of DevSecOps. It gives you the big picture and complete control over your ML models. It’s like a sneak peek into the future of secure software supply chains.

Think about it – every Chief Information Security Officer (CISO), DevOps leader, and CEO is putting their money on the fact that ML model security needs to keep evolving to keep up with the bad guys. And platforms like JFrog’s? They’re the ones setting the new standards for securing ML models at a massive scale. They’re at the heart of what the future of secure software supply chains looks like. It’s an exciting path forward!