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This is a broad, still-forming category; strong answers give a stage-by-stage breakdown of what's shipped versus planned rather than a blanket 'we secure your ML pipeline' claim.
Cryptographic signing/verification of model artifacts (similar to software supply-chain signing) is a concrete, verifiable capability; a vague claim of 'model governance' without a specific integrity-verification mechanism is weaker.
Look for a concrete detection methodology rather than a general claim; training-data poisoning is a difficult, evolving detection problem and a vendor should be specific about their actual technical approach.
Runtime adversarial-input protection needs differ substantially by model type; ask the vendor to be specific about which model types/architectures are actually covered rather than accepting a generic 'adversarial-robust' claim.
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Verified buyers can suggest criteria (anonymized before pooling).
Security embedded into existing MLOps tooling gets materially better adoption than a bolt-on separate review process that data science teams route around under delivery pressure.
Integration with existing enterprise IAM (not a siloed permission system) reduces the risk of orphaned or over-privileged access to production models, which is a real, underappreciated risk as ML deployment scales.
MLSecOps is an early, fast-moving category; insist on production references and be explicit about which capabilities are proven in practice versus aspirational, since marketing often runs ahead of what's actually deployed and tested.
Look for transparent, predictable scaling economics; a vendor unable to project cost at meaningfully more models/training runs creates real budget risk for a growing ML program.
Strong answers describe a real forensic and rollback capability with a customer example — this is the highest-stakes use case (a model already in production making compromised decisions), not just pre-deployment scanning.
AI-specific regulation is genuinely new and evolving — ask for a specific, actively-maintained regulatory mapping rather than a static one-time assessment that will quickly go stale.
Trend-over-time reporting is a distinct capability from a per-model security check — confirm this exists as a maintained, exportable report.
Model artifacts and training data are often significant IP in their own right — role-based access control over this specific asset is an often-overlooked consideration beyond just the security findings.
Ask for an honest per-platform coverage breakdown; uneven ML-platform coverage is a common real gap a vendor should disclose rather than obscure.
LLMs and classical ML models have meaningfully different attack surfaces — a vendor should give a specific answer on depth for each rather than implying uniform coverage.
Third-party pretrained models are an increasingly common and under-scrutinized supply-chain risk — a vendor should give a specific answer on provenance verification for externally-sourced base models, not just protection of internally-trained models.