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(AusBiz) | How to Stay Secure in an AI-Driven Software World | The Last Call

In an era of AI-powered development, how do teams move fast and stay secure? JFrog SVP APAC, Sunny Rao, joined AusBiz’s The Last Call to break it down — from securing the software supply chain to why end-to-end visibility is now essential for every tech organization. Discover why this matters for the future of software and AI-driven innovation.

Beyond Models: JFrog AI Catalog Evolves to Detect Shadow AI and Govern MCPs

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

Securing Vibe Coding: JFrog Introduces AI-Generated Code Validation

A fundamental shift in software development is already here. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants – a massive leap from less than 10% in early 2023. While this AI-driven speed creates a competitive advantage, it also opens a dangerous new front in the battle for software supply chain security.

Introducing JFrog Fly: The World's First Agentic Artifact Repository

AI has created a paradigm shift in software development. AI-native development teams – from small startups to enterprises like Goldman Sachs and Google – are adopting agentic development tools like Cursor and Copilot to increase the speed of code generation to a pace we’ve never seen before. But with all this new code comes a big challenge: how do you manage all these potential new releases and get the right ones deployed?

The Power of JFrog Artifactory as Your Model Registry

In my previous blog, we demonstrated how the FrogML SDK streamlines the process of integrating custom-built or publicly sourced models from your IDE into JFrog Artifactory. Now that your models are securely stored, versioned, and managed, the natural next question arises: “Ok, so you have some models in JFrog Artifactory, now what?” This is where the real power of the JFrog Platform comes into play.

Enhancing JFrog Internal Operations with Near Zero Downtime Migration

Data migrations have long been a significant source of anxiety for businesses and IT teams alike. The thought of moving critical databases often conjures images of prolonged downtime, service interruptions, and the ever-present risk of data loss. Indeed, statistics show that “90% of businesses experience unexpected downtime during database migrations, leading to significant revenue loss and customer dissatisfaction”.

Frog-Proof Security: Streamlining The Sec In DevSecOps

What’s in store for Software Supply Chain security in 2026? With the types of software entering organizations ever-changing, and the volume ever-increasing, DevSecOps teams are facing new, and complex questions at macro and micro levels: How can teams effectively control and curate what enters systems? How can remediation be accelerated, while ensuring accuracy? How will the rising use of AI impact our threat landscape and can DevOps and Security teams truly share ownership of this emerging reality without adding friction?

JFrog and ServiceNow: Accelerate Trusted Software Application Development

Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that is rooted in your software pipeline.

FrogML SDK: the Gateway to Model Governance

Data-driven decisions are critical. And to support high-stakes decision-making – from fraud detection in credit card transactions to demand forecasting in retail – organizations are increasingly relying on complex models. According to McKinsey, 78% of organizations report using AI in at least one business function, highlighting just how embedded AI and ML models have become in operational and strategic decision-making.

The Innovation vs. Control Syndrome: Unlocking Enterprise AI's Full Potential

From optimizing supply chains to personalizing customer experiences, artificial intelligence and machine learning models are no longer statistics-based revenue initiatives; they’re foundational to modern business strategy. Organizations are pouring resources into developing and deploying AI, driven by the promise of unprecedented efficiency, insight, and competitive advantage. Yet, beneath this surging wave of innovation lies a growing tension: the Innovation vs. Control Syndrome.