Protect AI Development with Falcon Cloud Security

As AI becomes an integral part of how modern applications are built and deployed, it introduces new risks and new blind spots for security teams. Large language models, machine learning packages, and embedded AI services can be hidden deep in the software supply chain or running unnoticed in production. Without purpose-built visibility, organizations risk exposing sensitive data, shipping vulnerable code, or relying on models they cannot fully govern.

CrowdStrike Falcon® Cloud Security provides end-to-end protection for the modern AI pipeline. It offers real-time detection of AI components during development, comprehensive scanning of AI models across cloud platforms, and continuous runtime inventory of AI workloads in production.

To understand how Falcon Cloud Security delivers this comprehensive protection, it’s helpful to look at how it secures each stage of the AI pipeline, starting with the CI/CD workflow.

Detecting AI Risk in the CI/CD Pipeline

AI is increasingly woven into the fabric of modern applications. Development teams are embedding AI libraries into container images, training custom models during build stages, and pulling in third-party inference services. These additions often happen quickly and without security oversight.

Falcon Cloud Security integrates directly into the CI/CD pipeline to scan container images as they are built and promoted. During these scans, it includes a specialized detection step to identify AI components. Falcon Cloud Security flags:

• Whether an image uses AI functionality
• Which packages are AI-related
• Known vulnerabilities (CVEs) tied to those packages

From the Falcon Cloud Security console, teams get clear insight into which container images contain AI components, what those components are, and whether they introduce risk. This visibility extends beyond static images — Falcon Cloud Security connects build-time detections to live containers so teams can see what is running in production.

For example, if a container is flagged for including an outdated AI library, security teams can quickly pinpoint the base image, understand the exposure, and involve developers to resolve the issue before it reaches production. Because Falcon Cloud Security is integrated directly into the CI/CD workflow, this all happens in real time without slowing down delivery.

But the AI security journey doesn’t stop at the development stage. As organizations increasingly rely on cloud-hosted and third-party AI services, Falcon Cloud Security extends protection further upstream.

Scanning AI and Machine Learning Models Across Cloud Platforms

As organizations scale their use of cloud-native AI services, security visibility must expand to cover these new environments. Whether teams are building their own models or leveraging foundation models from providers like AWS, Azure, or Google Cloud, the risk surface is growing and often hidden from view.

For homegrown models, Falcon Cloud Security surfaces training data, model artifacts, experiment scripts, and package dependencies. This level of detail helps teams track model provenance, enforce governance, and reduce shadow AI.

For off-the-shelf capabilities like Bedrock or Vertex AI, Falcon Cloud Security identifies the APIs and services being accessed. It brings these embedded models, which are often abstracted away from developers, into focus so teams can uncover what risks may be running behind the scenes.

Still, identifying risks in code and cloud platforms isn’t enough. For lasting protection, organizations must know what’s running at any given moment.

Maintaining a Real-Time Inventory of AI Workloads

Even with proper scans and policies in place, AI workloads in production can drift. Containers scale dynamically, teams experiment with new models, and services evolve quickly. That is why runtime visibility is critical.

Falcon Cloud Security delivers a real-time inventory of AI-related assets running across AWS, Azure, and Google Cloud environments. This includes Kubernetes clusters, containers invoking AI services, and models running via cloud-native APIs.

This runtime inventory ties back to earlier pipeline detections. Teams can easily trace risk from build time to runtime — if a container flagged for AI vulnerabilities during development is now actively running in production, Falcon Cloud Security connects the dots. This enables teams to take swift, informed action.

By capturing which models are live and where they are running, Falcon Cloud Security gives organizations the context they need to maintain control over their AI footprint, especially in fast-moving, distributed environments. This real-time context allows security and DevOps teams to support AI innovation safely and at scale.

Enabling Safe AI Innovation

AI adoption is moving fast, and so are the risks. From unmanaged dependencies to invisible inference services, today’s development pipelines introduce complexity that traditional tools cannot track.

Falcon Cloud Security empowers security teams to secure AI pipelines, from code to container to cloud. It embeds AI detection into the CI/CD workflow, scans models across major cloud platforms, and maintains a live view of what is running in production. With this comprehensive coverage, organizations can move quickly with AI while maintaining the security and control needed to stay ahead of evolving threats.

Additional Resources

• Read about how CrowdStrike was named a Leader in Cloud and Application Runtime Security.
• Download the Cloud Detection and Response Survival Guide for the SOC.
• See Falcon Cloud Security in action.