A Deep Dive into Preventing Data Poisoning
Data poisoning attacks exploit training signals, feedback loops, and shared pipelines, often causing gradual quality loss that is difficult to trace back to its source.
To win, your GenAI solutions must detect poisoned signals early, isolate risk across pipelines, and continuously monitor for drift and degradation.
Data poisoning risks are easy to underestimate and hard to reverse once embedded.
• Low visibility into poisoning mechanics: Teams lack a concrete understanding of how malicious data enters and influences GenAI systems.
• Delayed detection: Signal pollution and model drift are discovered only after quality or reliability degrades.
• Entangled pipelines: Training and inference data paths are insufficiently segmented, allowing contamination to spread.
These weaknesses lead to silent model degradation, unreliable outputs, and costly remediation efforts.
In this hands-on workshop, your team analyzes and designs defenses against data poisoning through guided scenarios and applied exercises.
• Explain how data poisoning works across GenAI training and inference workflows.
• Detect malicious data and polluted signals using structured analysis techniques.
• Monitor for model drift and quality loss tied to compromised inputs.
• Design segmented training and inference pipelines to limit blast radius.
• Define strategies to isolate and mitigate poisoned or compromised data sources.
Explaining How Data Poisoning Works in GenAI
Detecting Malicious Data and Signal Pollution
Monitoring for Model Drift and Quality Loss
Segmenting Training and Inference Pipelines
Isolating and Mitigating Compromised Inputs
• Understand the mechanics and risks of data poisoning in GenAI systems.
• Identify signs of malicious data and signal contamination.
• Monitor models for drift and degradation linked to poisoned inputs.
• Apply pipeline segmentation to reduce systemic exposure.
• Leave with clear mitigation strategies for compromised data sources.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Conceptual models, pipeline diagrams, and guided risk analysis exercises