Service — ML Pipeline Security
AI/ML Pipeline & Model Security
Every model you ship inherits the security of the data it learned from, the artefacts it was built out of, and the infrastructure that serves it. Most organizations have never audited any of those three.
Why it matters
A poisoned dataset ships a backdoor into production.
Training and fine-tuning pipelines pull from data sources, public model hubs, and third-party packages that are rarely reviewed with the same rigour as production code. A tampered dataset, a malicious model artefact, or an over-permissive training role can compromise a model long before it ever answers a prompt — and behave normally until a specific trigger appears.
Where it applies
Anywhere a model is trained, fine-tuned, stored, or served.
- Custom-trained and fine-tuned production models
- Pipelines using public datasets or model hubs
- MLOps platforms and experiment-tracking infrastructure
- Model registries and artefact storage
- GPU clusters and inference-serving infrastructure
- Vector databases and embedding stores
- Third-party model and API integrations
Our methodology
We follow the model from raw data to live inference.
The pipeline is a supply chain, and we treat it like one — reviewing each stage for tampering, exposure, and excess privilege, then testing the deployed model for what it leaks.
- 01
Pipeline & Data Lineage Mapping
Tracing where training data originates, who can modify it, and what validation exists before it reaches a training run.
- 02
Data Poisoning & Integrity Review
Assessing the pipeline's exposure to poisoned or manipulated inputs, and whether that manipulation would be detectable.
- 03
Model Supply Chain Review
Reviewing third-party models, weights, and dependencies for provenance, integrity verification, and unsafe deserialization.
- 04
MLOps Infrastructure Assessment
Testing access control, secrets handling, and network exposure across training infrastructure and the model registry.
- 05
Model Extraction & Inversion Testing
Probing deployed endpoints for model theft, membership inference, and training-data leakage through inference alone.
- 06
Reporting & Pipeline Hardening
Prioritized remediation across data governance, artefact signing, and infrastructure controls, with a retest after fixes.
Standards & compliance
Aligned to AI and supply-chain security frameworks.
- MITRE ATLAS
- NIST AI RMF
- SLSA Supply Chain Levels
- ISO 27001
- SOC 2
- OWASP ML Security Top 10
Others in ai security
Do you know what actually went into your model?
Walk us through your training and deployment pipeline, and we'll scope a review.
Within one business day · NDA on request