How can secure deployment of AI models be achieved through image provenance and container security?

• A. Signed container images
• B. Image scanning
• C. Verifiable build pipelines
• D. All of the above, including least privilege runtime environments

Answer: (D)

Secure deployment of AI models benefits from a defense-in-depth approach that covers provenance, integrity, vulnerability management, and runtime risk. Signed container images provide traceable provenance and integrity by confirming the image came from a trusted source and has not been tampered with since signing. Image scanning adds an early warning system, detecting known vulnerabilities and outdated components before the model container runs in production. Verifiable build pipelines ensure the image is produced in a reproducible, auditable process, so you can demonstrate exactly how the image was built from trusted inputs and reproduce it if needed. Implementing least-privilege runtime environments restricts what the container can do at runtime, reducing the impact of any potential compromise by limiting capabilities, network access, and resources. When you combine these practices, you address the full spectrum of secure deployment: provenance and integrity of the image, visibility into and control over its vulnerabilities, verifiable and auditable construction, and minimized risk during execution. This is why all of the above, including least-privilege runtime environments, is the strongest, most comprehensive approach.