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ML & Model Security
Secure the machine-learning lifecycle, end to end.
AI risk doesn't start at the prompt, it starts in the pipeline. ML & Model Security (MLSecOps) protects the entire machine-learning lifecycle: the integrity of your training data, the supply chain of models and dependencies, the security of deployment, and runtime monitoring for drift and abuse. So the models reaching production are ones you can actually trust.
What We Cover
The specific areas we assess in this practice.
Training Data Integrity
Protect training and fine-tuning data against poisoning and tampering.
Model Supply Chain
Secure the third-party models, weights, and dependencies you build on.
Deployment Security
Harden how models are packaged, served, and exposed in production.
Monitoring & Drift
Detect drift, abuse, and degradation once models are live.
How the engagement runs
A disciplined, repeatable process, so findings are reproducible and fixes are verified.
- 01
Map
Map the ML lifecycle, data sources, and dependencies.
- 02
Assess
Test each stage for integrity and supply-chain risk.
- 03
Harden
Secure data, pipelines, and deployment.
- 04
Monitor
Stand up runtime monitoring for drift and abuse.
Regulatory relevance
Lifecycle assurance supporting:
- ISO 42001
- ISO 27001
- APRA CPS 234
- Essential 8
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