Skip to main content
Risk Demonstrations / Training Data Poisoning
Risk Domain: R-DQ Explore Related Risks

Training Data Poisoning – Controls Simulation

Poisoned Clean Flagged (Detected)
Phase 1 – Uncontrolled ingestion

Sources

Web, Vendors, Internal
Open Web TS: ?
Partner Feeds TS: ?
Internal Data TS: ?
User Uploads TS: ?
Trust Score (TS) will be learned and applied in Phase 2.

Controls

Preventive, Detective, Governance
Source validation & trust scoring
Preventive

Scores each source, filters low-trust inputs before training.

Data anomaly detection
Detective

Flags and quarantines unusual patterns indicative of poisoning.

Data versioning & lineage
Governance

Immutable lineage, rollbacks, and reproducible training snapshots.

TRUST FILTER
ANOMALY CHECK

Risk Level

High
Poisoned inputs –%
Flagged & removed –%
Effective quality –%

Versioning & Lineage

Dataset: v0.9
Recent snapshots
  • v0.9 – baseline
Model output
⚠️ Unstable predictions…
Phase 1 – Risk without controls: poisoned data flows into training.
Looping every ~45 seconds

About This Demonstration

This interactive simulation shows how training data poisoning attacks work and demonstrates the effectiveness of preventive, detective, and governance controls in mitigating data quality risks.

Risk Domain: R-DQ

Data Quality risks encompass threats to the integrity, accuracy, and reliability of training data, including poisoning attacks, data drift, and contamination scenarios.