Data Lineage Graph

Establishes the origin of truth by linking every data point back to its raw source system (e.g., a specific IoT sensor, a transactional database row, or a third-party API endpoint). This creates an unbreakable chain of custody for audit purposes, verifying that data has not been illicitly modified or injected. It answers the critical question: 'Where did this data actually come from?'

Visually and programmatically tracks every Extract, Transform, Load (ETL) operation applied to the data. This includes:

• Filtering logic (excluded records)
• Aggregation functions (sum, average)
• Joins with other datasets
• Feature engineering code for ML models This granularity allows engineers to trace a specific anomaly in a report back to a faulty line of Python code in a preprocessing script.

Provides a forward-looking view to assess the blast radius of a change. If a schema is deprecated or a source system goes offline, the graph instantly identifies all downstream assets—dashboards, machine learning models, and operational APIs—that will break. This prevents business disruption by enabling proactive communication with stakeholders before a change is implemented.

Moves beyond table-level tracking to map the lineage of individual columns or JSON attributes. It shows exactly how a specific field, like customer_risk_score, is calculated by combining credit_history and transaction_frequency. This is essential for debugging model bias and fulfilling Right to Explanation requests under GDPR, where a user asks for the logic behind a specific automated decision.

Captures the state of the data pipeline at specific points in time. A lineage graph is not flat; it is a time-series of metadata. It allows auditors to replay the exact transformations that were in effect last quarter, even if the code has since changed. This supports deterministic replay for regulatory investigations and ensures that historical reports remain reproducible.

The complete, verifiable lineage of an AI-driven outcome. It binds together the input data snapshot, model version, inference fingerprint, and any human overrides into a single auditable record. While a data lineage graph tracks the data's journey, decision provenance links that data to a specific automated outcome, establishing accountability for regulatory review.

A documented, unbroken record of every entity that has handled a piece of data or evidence. In AI governance, this preserves legal integrity by proving data hasn't been tampered with between collection and inference. Key elements include:

• Sequential timestamps for each transfer
• Identity verification of handlers
• Integrity checks at each custody point

A storage architecture where data is retrieved by its cryptographic hash (e.g., SHA-256) rather than a physical location. This guarantees data integrity and enables deduplication across lineage graphs. If a single bit changes, the hash changes, immediately breaking the lineage and flagging tampering. This is foundational for immutable audit trails.

The process of converting data structures into a canonical byte stream that always produces the exact same output for logically equivalent inputs. Formats like Canonical JSON ensure that hashing is consistent across systems. Without deterministic serialization, two identical lineage records could produce different hashes, breaking verifiability.

A composite hash that uniquely identifies a specific prediction event. It combines:

• Model version hash
• Input data snapshot hash
• Configuration parameters
• Environment metadata This fingerprint serves as a foreign key linking the prediction back to every node in the data lineage graph.

The Coalition for Content Provenance and Authenticity specification defines how to attach cryptographically verifiable provenance metadata to digital content. It creates a tamper-evident chain of assertions about how content was created and modified. In AI governance, C2PA manifests provide a standardized format for expressing data lineage to external auditors and regulators.