AI Supply Chain Security

AI and ML supply chains combine software dependencies, training data, model artifacts, containers, prompts, and cloud infrastructure. That creates a larger attack surface than traditional CI/CD: a compromised dataset, unsigned model artifact, poisoned base image, or over-permissive inference endpoint can undermine the entire system even when the application code looks clean.

This pillar collects the Red Team guidance for securing AI/ML workloads and software supply chains on AWS. Start with model and data provenance, then add build-pipeline controls, SBOM generation, artifact signing, and runtime monitoring.

Start Here

• AI Supply Chain Security: Model Poisoning Defense – threat model AI model provenance, data poisoning, model backdoors, and third-party AI risk.
• Secure AI/ML Workloads on AWS: 2026 Guide – secure Bedrock, SageMaker, IAM, encryption, network isolation, and monitoring.
• Secure AWS AI/ML Pipelines: DevSecOps Checklist – apply DevSecOps controls from data preparation through model deployment.
• AI Threat Detection on AWS: GuardDuty Analytics Guide – use GuardDuty, behavioral analytics, and automated response for cloud threat detection.

Software Supply Chain Controls

• Software Supply Chain Security on AWS: SBOM Generation with Syft, Grype, and CodePipeline – generate SBOMs and vulnerability findings in AWS delivery pipelines.
• Container Image Signing and Verification: Cosign and Sigstore on AWS – sign images, verify provenance, and reject unsigned artifacts in EKS.
• AWS DevSecOps Pipeline Security: Complete Automation Implementation Guide – integrate scanning, approvals, and security automation in AWS pipelines.
• IaC Security Scanning: Checkov and tfsec vs AWS-Native Controls – enforce infrastructure guardrails before deployment.

Implementation Path

1. Establish Provenance

• Require known sources for datasets, foundation models, containers, and training code.
• Record checksums for model artifacts and datasets.
• Store model lineage in SageMaker Model Registry or an equivalent controlled registry.
• Track who approved the model, data source, and deployment target.

2. Secure the Build Pipeline

• Scan ML containers and dependencies before training.
• Generate SBOMs for model-serving images and supporting services.
• Require signed containers and immutable tags for production workloads.
• Block deployment when critical vulnerabilities, missing attestations, or unsigned artifacts are detected.

3. Harden Runtime Access

• Apply least-privilege IAM boundaries for Bedrock, SageMaker, ECR, S3, and KMS.
• Isolate training and inference workloads in private subnets.
• Log model access, prompt activity, data access, and deployment changes.
• Monitor for unusual inference volume, data exfiltration, model drift, and suspicious API usage.

4. Close the Feedback Loop

• Review model and supply-chain alerts with the same incident process used for application security.
• Feed lessons learned into policy-as-code, CI gates, and registry controls.
• Reassess third-party model and dataset risk before major releases.

Suggested Next Articles

• Model provenance and attestations for SageMaker Model Registry.
• Securing the AI build pipeline with SBOMs, signatures, and approval gates.
• Threat modeling prompt, retrieval, and model supply chains for Bedrock applications.

For broader consulting work, writing, and project context, visit jonprice.io.