AI Security & Governance
Audit & MLSecOps
Embed security throughout your AI production chain
Securing the chain, not just the model
An AI system is more than a model: it relies on data, training pipelines, open-source dependencies, model registries and inference environments. Every link is a potential target.
MLSecOps applies DevSecOps principles to the AI chain: building in security from design and automating it across the entire model lifecycle.
Reference framework:
Our audits rely on the NIST AI RMF and software supply chain security best practices (SLSA) applied to AI models.
The scopes we audit
ML Pipelines
Security of model training, evaluation and deployment chains.
Model Supply Chain
Provenance and integrity of models, datasets and third-party dependencies.
Registries & Artifacts
Access control and integrity of model registries and artifacts.
Inference Environments
Hardening of services exposing models in production.
Secrets & Access
Management of secrets, keys and access to training data.
Traceability
Logging, versioning and end-to-end auditability.
Our MLSecOps approach
Mapping
Modeling of your AI chain and its exposure points.
Technical Audit
Security review of pipelines, registries and environments.
Supply Chain Analysis
Verification of the provenance and integrity of components.
Security Integration
Insertion of automated controls into your MLOps lifecycle.
Improvement Plan
Prioritized recommendations and implementation support.
Use cases
Secure your AI chain
Our experts audit and strengthen the security of your entire AI production lifecycle.
Schedule an audit