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Machine Learning Data Integrity

When artificial intelligence (AI) or machine learning (ML) is employed for mission-critical spacecraft or ground system operations, the integrity of the training data set is a foundational security requirement, not merely a data quality concern. Data poisoning attacks introduce or modify training samples, labels, or other data-pipeline inputs to alter resulting model behavior. Poisoning may cause general performance degradation, targeted misclassification, or backdoor behavior that appears only when specific operational inputs or triggers are present. Remediation may require identifying and removing affected data, retraining or fine-tuning from a trusted dataset, or rolling back to a validated model. Because remediation can be difficult and may not identify every affected behavior, controls should prioritize preventing unauthorized data changes and detecting suspicious inputs before training while maintaining recoverable trusted dataset and model versions. Detection and prevention techniques include validity checking of data sets to identify statistically anomalous or out-of-distribution inputs, statistical analysis to detect injected samples that deviate from expected data distributions, manual analysis of flagged inputs, and regression testing over time to identify model behavioral drift that may indicate successful historical poisoning. These controls must be applied as part of a continuous data governance process spanning the full training pipeline, from data collection and curation through model validation and operational deployment.

ID: CM0049
Tier: II
Onboard SV CM 
Created: 2022/10/19
Last Modified: 2026/08/06

Pre-Operations Government

Acquisition requirements should mandate that any AI/ML component used for mission-critical functions be accompanied by a documented data integrity assurance plan covering the full training data lifecycle, including data provenance, collection controls, validation procedures, and the specific techniques used to detect and prevent data poisoning before training occurs. Requirements should specify that training data sets used for mission-critical models be subject to formal provenance controls that record the origin, collection method, and chain of custody for each data source, enabling traceability of any anomalous model behavior back to potentially compromised training inputs. Contract language should require that model developers evaluate data-integrity controls using representative, threat-informed poisoning scenarios. Testing must measure whether attempted poisoning is prevented, detected, or limits model impact; document false-positive and false-negative behavior; and identify residual poisoning risks that the implemented controls do not reliably detect. Evaluation criteria should assess offerors' experience with AI/ML data security for safety- and mission-critical applications, their proposed data governance architecture, and the maturity of their statistical and automated anomaly detection capabilities for training data validation. Verification should include review of data integrity assurance documentation, examination of validation test results, and assessment of regression testing plans that will monitor model behavior for drift over the operational lifecycle.

Pre-Operations Developer/Supplier

Training data governance architecture must be established before data collection begins, defining the approved data sources, collection procedures, ingestion controls, and validation pipeline that all training data must pass through before being used in a model training cycle. Data provenance tracking should be implemented at the individual sample level where feasible, providing a traceable record that enables investigation of which data sources contributed to a model if behavioral anomalies are detected post-deployment. When item-level provenance is unavailable, source- or batch-level lineage and the resulting traceability limitations must be documented. Automated validity checking should be applied at the data ingestion boundary, flagging inputs that fall outside the expected statistical distribution of the training domain for manual review before they are incorporated into the training set. Risk-appropriate statistical and analytical techniques should be applied before each training or retraining release to identify suspicious samples, labels, clusters, source concentrations, or distribution changes. The selected methods must be evaluated against representative poisoning strategies, and their limitations must be documented because stealthy, targeted, or clean-label poisoning may remain consistent with the expected data distribution. Trained and retrained models must be evaluated against integrity-protected validation and security test sets with documented provenance before release. Testing should include mission-performance requirements and representative poisoning or backdoor-trigger scenarios. Post-deployment regression testing should occur at mission-defined intervals or following relevant changes. Detected behavioral drift must trigger investigation but must not, by itself, be attributed to training-data poisoning.