Securing AI Pipelines
Secure the infrastructure around your models: data provenance, artifact integrity, secrets, least-privilege access, and audit evidence across the ML lifecycle.
Course Overview
Most real compromises of AI systems do not involve clever adversarial mathematics. They involve a training notebook with hard-coded credentials, an S3 bucket of training data readable by the whole organization, a model artifact nobody can prove was produced by the pipeline that claims to have built it, or a service account with far more permission than its job requires. This course secures the pipeline rather than the model.
The through-line is provenance and integrity. You will build a pipeline where the origin of every dataset is recorded, every artifact is signed and verifiable, and the model serving a request can be traced back to the exact code, data, and approval that produced it. That is what makes tampering detectable rather than merely unlikely, and it is what an auditor is actually asking for.
Around that, the course covers the controls that constrain blast radius: least-privilege identity for training and serving workloads, network isolation for training clusters, secrets management that survives notebook culture, and secure handling of the third-party dependencies and pretrained weights that enter your environment from the internet. Each is implemented in a lab, not just specified.
Duration: 3 days|Delivery: onsite, virtual, or hybrid
Prerequisites
- Experience operating ML pipelines in production
- Working knowledge of cloud IAM on AWS, Azure, or GCP
- Familiarity with containers and Kubernetes
- General security background such as application or infrastructure security
Who Should Attend
- Security engineers responsible for approving ML infrastructure
- MLOps and platform engineers who own pipeline security controls
- Compliance and risk teams needing to evaluate AI system controls concretely
- Architects designing ML infrastructure that must pass a security review
Course Outline
- 1Attack surface of an ML pipeline: from data source to serving endpoint
- 2Data provenance: recording origin, transformations, and consent at ingestion
- 3Training data integrity: validation, quarantine, and detecting tampering
- 4Artifact signing and verification: making model provenance cryptographically checkable
- 5Secrets management: eliminating credentials from notebooks, images, and configs
- 6Least-privilege identity for training jobs, pipelines, and serving workloads
- 7Network isolation and egress control for training clusters
- 8Third-party risk: model hubs, pretrained weights, and unsafe file formats
- 9Dependency and container supply chain: SBOMs, scanning, and base image policy
- 10Feature store and vector store access control
- 11Audit logging across the lifecycle: what to capture and how long to keep it
- 12Compliance mapping: SOC 2, ISO 27001, and the EU AI Act as engineering requirements
- 13Incident response: containing and investigating a pipeline compromise
Learning Outcomes
- Map the full attack surface of an ML pipeline including the parts reviews usually miss
- Implement data provenance tracking from ingestion through to serving
- Sign and verify model artifacts so tampering is detectable
- Apply least-privilege identity across training and serving workloads
- Assess and contain the risk of third-party models and pretrained weights
- Produce audit evidence covering the full model lifecycle
- Map technical controls onto the compliance frameworks that apply to you
What You Will Build
- A threat model and control matrix covering your ML pipeline
- An artifact signing and verification implementation
- A least-privilege IAM design for training and serving workloads
- An audit logging schema and retention policy for the ML lifecycle
Frequently Asked Questions
- How does this differ from AI Model Security?
- AI Model Security attacks the model itself through adversarial inputs, poisoning, and extraction. This course secures everything around the model: data provenance, artifact integrity, secrets, identity, and audit. In practice the pipeline is where most real incidents originate, and the two courses are commonly taken together.
- Why does artifact signing matter for models?
- Without it you cannot prove that the weights being served are the weights your pipeline produced. A model file swapped in object storage, whether maliciously or by accident, is invisible. Signing makes provenance verifiable at load time and turns an untestable assumption into a check.
- Are pretrained weights from public hubs safe to use?
- They are third-party code with the same trust implications, and some serialization formats execute code on load. The course covers safe formats, scanning, provenance verification, and building an approval process that does not simply block all external models, since that is not realistic.
- Does this satisfy our SOC 2 or ISO 27001 requirements?
- It covers how to map these controls onto those frameworks and what evidence auditors typically ask for. It is engineering guidance rather than a certification path, and your auditor remains the authority on sufficiency.
- Can this be delivered against our actual architecture?
- Yes. For private cohorts we build the threat model and control matrix against your real pipeline, so the deliverables are directly actionable rather than generic.
Related Courses
AI Model Security
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Enterprise AI Governance and Compliance
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Enterprise RAG: Security and Scale
Multi-tenant RAG with document-level access control, PII handling, prompt injection defense, audit trails, and architectures that hold up under regulatory review.
MLOps for Production AI
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Ready to Get Started?
Contact us to schedule training for your team or inquire about upcoming sessions.