JFrog has introduced JFrog Shadow AI, a new capability designed to give enterprises visibility into unmanaged AI models and external API calls appearing inside their software supply chains. As organizations continue adopting AI tools at a rapid pace, this feature aims to improve governance and reduce the risks associated with unauthorized or unmonitored AI integrations.
Many companies now rely on generative AI and third-party models, yet these tools often enter development environments informally. As a result, security teams struggle to track how data flows through applications. Because of this gap, shadow AI presents compliance, operational and regulatory concerns. JFrog’s new detection layer attempts to close that gap by offering consistent oversight.
How JFrog Shadow AI works inside the enterprise
With JFrog Shadow AI, the platform automatically scans development environments and inventories every internal AI model and external AI API in use. This includes both approved and unapproved services from providers such as OpenAI, Anthropic and other vendors. Moreover, the feature adds each detected item to a centralized AI catalog so teams can evaluate usage patterns in one location.
Once the system identifies hidden AI interactions, enterprises can apply governance policies. For example, teams may restrict access to only approved providers, enforce compliance rules or define which workflows may use external APIs. Because everything is tracked, organizations gain an audit trail for internal and regulatory reporting.
Why JFrog Shadow AI matters for software supply chain security
As AI becomes more deeply embedded in production systems, organizations face several new risks. Unmanaged AI usage may expose sensitive data, create compliance violations or introduce vulnerabilities through unverified external endpoints. Furthermore, rapid adoption often means that AI appears in pipelines long before governance frameworks are in place.
Consequently, JFrog Shadow AI arrives at a moment when enterprises need clearer visibility across their AI dependencies. Since global AI regulations continue to evolve, companies must ensure that they understand what models they rely on and how those models interact with internal data. With this feature, JFrog positions its platform as a single system of record for both software and AI assets.
Additionally, AI adoption frequently mirrors trends seen in earlier phases of software development. Teams once struggled with unmanaged open-source dependencies; today, they face similar challenges with AI tools. JFrog argues that supply chain security must now extend to AI components as naturally as it does to packages or binaries.
Governance features added through JFrog Shadow AI
The JFrog Shadow AI capability introduces several governance mechanisms that help enterprises formalize their AI usage. For instance, organizations can:
- define access controls for approved models and APIs
- track data flows to prevent unintentional exposure
- monitor usage patterns to maintain regulatory compliance
- enforce AI policies across development and deployment stages
These features work together to reduce blind spots. As a result, companies can apply consistent oversight even when teams experiment with new AI services.
Industry context: growing interest in AI governance tools
The launch of JFrog Shadow AI reflects a broader shift toward enterprise AI governance. Other vendors are exploring similar ideas. For example, ModelOp provides an “AI control tower” for lifecycle management and compliance. Because it focuses on governance rather than model development, the platform targets organizations that must track AI usage across different business units.
Similarly, Aurva offers a security-focused solution that monitors AI and ML workloads in real time. Its tools analyze agentic behavior, data access patterns and API activity. Since it helps detect unauthorized AI usage, Aurva shares common goals with JFrog’s new capability. As interest in AI security grows, these platforms demonstrate how quickly the ecosystem is expanding.
How JFrog Shadow AI strengthens the AI supply chain
By integrating Shadow AI into its existing ecosystem, JFrog extends its platform beyond traditional artifact management. The AI Catalog now becomes a unified reference point for both software and AI components. Consequently, teams gain consistent visibility into model usage alongside standard software dependencies.
This consolidation matters because enterprises increasingly treat AI models as first-class supply chain components. Not only must organizations secure their code, but they must also understand how AI interacts with it. Since many AI models rely on external providers, JFrog’s approach helps reduce uncertainty around compliance, data transmission and endpoint reliability.
Regulatory and compliance pressures shaping demand
Growing regulatory attention also drives interest in tools like JFrog Shadow AI. The EU AI Act introduces new obligations for transparency, documentation and risk classification. Meanwhile, US initiatives highlight the need for model provenance, auditability and consistent monitoring. In addition, cybersecurity frameworks such as NIS2 expand reporting expectations across complex environments.
Because these regulations evolve quickly, enterprises need adaptable tools that maintain oversight without slowing development. Shadow AI detection supports that need by providing immediate insight into AI interactions that may otherwise remain undiscovered.
Availability and rollout timeline
JFrog plans to roll out JFrog Shadow AI as part of its existing AI Catalog, with general availability expected in 2025. The company will introduce capabilities in phases, allowing customers to begin testing the detection and governance features ahead of full release.
As AI usage accelerates across industries, demand for visibility tools is likely to increase. JFrog’s strategy positions the platform to serve organizations seeking a unified solution that covers both software and AI supply chains.
Conclusion: JFrog Shadow AI enhances visibility and governance in enterprise AI adoption
The introduction of JFrog Shadow AI marks a significant step in addressing the challenges of hidden AI usage. By identifying unmanaged models and API calls, the feature gives enterprises the visibility they need to apply governance, reduce risk and comply with emerging regulations. As AI becomes a core part of enterprise development, these capabilities will play a critical role in maintaining security and operational integrity.
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