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Detect Data Poisoning Before It Corrupts AI Decisions
Gain visibility into how training data, validation data, feature pipelines, labels, and upstream data sources can be manipulated. Grafyn helps security teams detect poisoned data, suspicious changes, and compromised pipelines before they influence model behavior or business decisions.
Poisoned Data Can Turn Trusted Models Into Risk
AI and ML models depend on the data they are trained on. If attackers or insiders manipulate records, labels, features, or source datasets, models can learn the wrong patterns, create hidden backdoors, degrade accuracy, or make unsafe decisions. For CISOs, the challenge is knowing whether trusted data has been tampered with before poisoned signals reach production models.
30%
AI cyberattacks expected to involve poisoning, model theft, or adversarial examples.
0.001%
Poisoned training data can be enough to degrade model accuracy in some attack scenarios.
25/28
Organizations lacked the right tools to secure ML systems.
25%
Enterprise GenAI apps are expected to face repeated security incidents.
A Complete Solution to Detect and Reduce Data Poisoning Risk
Grafyn helps security teams monitor training data, feature pipelines, model inputs, labels, embeddings, and data changes so they can detect poisoning attempts before compromised data affects AI systems.
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Map Training Data Lineage
Discover which datasets, sources, pipelines, labels, features, and transformations feed each AI or ML model.
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Detect Poisoned Data Signals
Identify suspicious data changes, label manipulation, feature drift, anomalous records, corrupted samples, and unauthorized modifications that could influence model behavior.
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Reduce Poisoning Blast Radius
Trace which models, applications, agents, users, and workflows depend on compromised data, then isolate affected datasets, restrict risky pipelines, and restore trusted training sources.