Model Supply Chain Security

The documented lineage and origin of a dataset, including its creation, transformation, and chain of custody. Provenance tracking captures metadata about who collected the data, how it was processed, and every modification applied.

• Uses standards like W3C PROV to model entities, activities, and agents
• Enables auditing to detect unauthorized tampering in the supply chain
• Critical for verifying that fine-tuning datasets have not been poisoned before ingestion

A nested inventory listing all software components, libraries, and dependencies used in an ML pipeline. An SBOM provides transparency into the supply chain, allowing teams to rapidly identify vulnerable or compromised packages.

• Formats include SPDX and CycloneDX
• Essential for vulnerability management when a dependency is found to contain malicious code
• Extends to container images, Python packages, and pre-built inference servers

An automated, inline filtering system that inspects, validates, and blocks anomalous or potentially poisoned training samples before they reach model training. The firewall applies statistical checks, schema validation, and anomaly detection at ingestion time.

• Detects distributional shift between incoming data and the trusted baseline
• Flags samples with extreme feature values or suspicious label patterns
• Acts as a first line of defense against clean-label poisoning attacks

The practice of analyzing a trained model's internal weights, decision boundaries, or feature attributions to detect anomalies indicative of a backdoor or poisoning attack. Inspection does not require access to the original training data.

• Techniques include Neural Cleanse for trigger reconstruction
• Activation clustering separates clean and poisoned samples by analyzing hidden layer representations
• Spectral signatures identify statistical outliers in feature space that correlate with backdoor triggers

A structured adversarial assessment where a security team simulates real-world poisoning, evasion, and supply chain attacks against an ML system. Red teaming proactively identifies vulnerabilities before malicious actors can exploit them.

• Follows frameworks like MITRE ATLAS for AI-specific adversary tactics
• Tests artifact integrity, dependency poisoning, and backdoor injection vectors
• Produces actionable remediation plans for hardening the supply chain

The documented lineage and origin of every dataset used in the ML lifecycle, tracking creation, transformation, and all intermediate processing steps. Provenance metadata answers: who created this data, when, using what process, and has it been modified?

• Implements verifiable data lineage through cryptographic hashing at each pipeline stage
• Uses W3C PROV standard or ML Metadata (MLMD) for structured tracking
• Enables reproducible audits when a model exhibits unexpected behavior
• Detects unauthorized insertion of poisoned samples by comparing provenance chains

The systematic process of filtering and transforming training data to remove anomalous, mislabeled, or potentially poisoned samples before they corrupt model training. Sanitization operates as a pre-training defense layer that assumes the incoming data stream may be compromised.

• Applies statistical outlier detection to identify samples far from class centroids
• Uses spectral signatures to detect backdoor triggers in feature space
• Implements label consistency checks against a trusted validation set
• Often combined with differential privacy to bound the influence of any surviving poisoned samples

An inline, automated filtering system deployed in the data ingestion pipeline that inspects, validates, and blocks anomalous training samples in real time. Unlike batch sanitization, a quality firewall operates continuously as data flows from sources to the training process.

• Validates schema compliance and expected feature distributions
• Detects distributional shift from the trusted baseline using statistical tests
• Blocks inputs that trigger activation anomalies in a pre-deployed detector model
• Logs all rejected samples for security operations center (SOC) review