Schema Validation

The most fundamental layer of schema validation that verifies each field's data type matches the expected specification. This prevents type confusion attacks where an attacker injects string data into a numeric field to cause parsing errors or buffer overflows in downstream processing.

• Rejects "NaN", "Infinity", or arbitrary strings in numeric columns
• Enforces strict typing: int32, float64, boolean, categorical
• Prevents implicit type coercion that could mask malicious payloads
• Example: A feature expecting uint8 pixel values (0-255) rejects a value of -1 or 256

Defines permissible value intervals for continuous features and valid categories for discrete features. This prevents out-of-distribution poisoning where extreme values skew model weights or create latent backdoor triggers.

• Clamps or rejects values outside [min, max] bounds per feature
• Validates categorical fields against an allowlist of permissible classes
• Detects anomalous sensor readings that exceed physical constraints
• Example: A temperature sensor feature bounded to [-50.0, 150.0] rejects a 9999.0 injection

Validates the topological consistency of complex data structures including nested objects, arrays, and relational references. This prevents schema fragmentation attacks where malformed hierarchical data causes parsing failures or silent truncation.

• Enforces required field presence — rejects records with missing mandatory columns
• Validates array lengths and nested object shapes recursively
• Checks referential integrity between related tables or entities
• Example: A graph edge record missing a target_node_id is rejected before ingestion

Compares incoming data batches against a pre-computed statistical baseline derived from trusted training data. This detects subtle distributional shifts that simple range checks miss, such as a poisoned batch with correct types but anomalous correlations.

• Monitors mean, variance, and covariance drift per feature
• Uses Chebyshev bounds or Mahalanobis distance for multivariate outlier detection
• Triggers alerts when batch-level statistics deviate beyond configured thresholds
• Example: A batch where two normally uncorrelated features suddenly exhibit a 0.95 Pearson correlation is flagged

Ensures data adheres to expected serialization formats, character encodings, and representation standards. This prevents encoding-based injection where malicious payloads exploit parser differences between systems.

• Validates UTF-8 compliance and rejects null bytes or control characters
• Enforces consistent date/time formats (ISO 8601) and coordinate reference systems
• Checks image dimensions, channel counts, and color depth for vision pipelines
• Example: A CSV parser rejects a row containing unescaped delimiter characters that could shift column alignment

Manages changes to validation rules over time while maintaining backward compatibility and audit trails. This prevents schema drift attacks where an attacker exploits version mismatches between training and serving pipelines.

• Immutable schema versions with cryptographic hashes for integrity verification
• Supports forward compatibility for non-breaking additions
• Rejects data that references deprecated or unknown schema versions
• Example: A model trained on schema v3.1 rejects inference requests formatted for v2.0 to prevent silent feature misalignment

A detection technique that assigns a numerical deviation score to each data point based on its distance from the expected distribution. Schema validation handles deterministic rule violations, while anomaly scoring catches subtle, in-distribution poisoning that passes structural checks.

• Z-score analysis for numerical feature ranges
• Isolation Forest and autoencoder-based detectors for high-dimensional data
• Scores above a configurable threshold trigger quarantine before training

The documented chain of custody tracing a dataset's origin, transformations, and ownership. Schema validation verifies the shape of data at ingestion; provenance verifies the source is trustworthy.

• Cryptographic signatures on data batches from approved sources
• Lineage metadata recording every ETL transformation applied
• Enables forensic identification of the compromised source when poisoning is detected downstream

The assurance that training data has not been subject to unauthorized modification between validation and model consumption. Schema validation is a point-in-time gate; integrity controls ensure data remains unaltered after passing that gate.

• Cryptographic hashing (SHA-256) of validated data partitions
• Immutable storage with append-only write policies
• Periodic integrity checks comparing current state against known-good hashes

Continuous monitoring of statistical properties in feature distributions to alert engineers when incoming data diverges from the training baseline. Schema validation catches categorical violations; drift detection catches gradual distributional shifts that may signal an ongoing poisoning campaign.

• Kullback-Leibler divergence between training and production distributions
• Population Stability Index (PSI) for categorical feature monitoring
• Triggers automated model retraining or data pipeline freezes when thresholds are breached

The practice of creating immutable snapshots of datasets at specific points in time. When schema validation rejects a batch, versioning enables instant rollback to the last known-clean state and provides a reproducible audit trail for forensic analysis.

• Git-like semantics for dataset commits, branches, and tags
• Tools like DVC and LakeFS provide data version control
• Enables A/B comparison of model performance against different data versions to isolate poisoning impact