Data Integrity

The assurance that data correctly represents the real-world object, event, or condition it describes. Accuracy is maintained through input validation constraints, referential integrity checks in databases, and schema enforcement that rejects malformed records.

• Verified via checksums and parity checks during transmission
• Enforced through type constraints and range validators at ingestion
• Degraded by human entry errors, sensor drift, or transformation bugs
• Distinct from precision: a value can be precise (many decimal places) yet inaccurate

The property that data maintains identical values across all replicas, caches, and derived views within a distributed system. Consistency is governed by the CAP theorem trade-offs and enforced through consensus protocols like Raft or Paxos.

• Strong consistency: All reads return the most recent write
• Eventual consistency: Replicas converge over time if no new updates occur
• Causal consistency: Operations that are causally related are seen in order
• Violations manifest as stale reads, phantom records, or split-brain scenarios

The guarantee that no material records, fields, or transactions are missing from a dataset. Completeness is validated through record count reconciliation, null-value threshold monitoring, and gap detection in sequential identifiers.

• Measured as the ratio of present values to expected values per attribute
• Compromised by dropped messages in streaming pipelines or failed batch jobs
• Critical in financial ledgers where missing transactions break audit trails
• Addressed through exactly-once semantics and dead-letter queue reprocessing

The property that data is available within the expected temporal window required for its intended use. Timeliness is a function of pipeline latency, scheduling frequency, and clock synchronization across distributed nodes.

• Real-time systems require sub-second freshness guarantees
• Batch analytics may tolerate hourly or daily update cadences
• Violated by backpressure, resource contention, or network partitions
• Monitored through watermark tracking and lag metrics in event-streaming platforms

The constraint that no duplicate records exist for entities that should appear exactly once within a given scope. Uniqueness is enforced through primary key constraints, unique indexes, and deduplication logic in ingestion pipelines.

• Violations cause inflated counts, skewed aggregations, and double-processing
• Addressed via upsert operations (insert or update on conflict)
• Requires deterministic identity resolution across disparate source systems
• Often validated through hash-based record fingerprinting and merge strategies

The conformance of data to defined business rules, domain constraints, and syntactic formats. Validity is enforced through schema registries, regular expression patterns, and enumerated domain checks that reject out-of-bounds values.

• Syntactic validity: correct data type and format (e.g., ISO 8601 dates)
• Semantic validity: meaningful within business context (e.g., age > 0)
• Referential validity: foreign key values resolve to existing parent records
• Validated at ingestion, on write, and periodically through data quality scans

The foundational mechanism for data integrity verification. A hash function (like SHA-256) produces a fixed-size, deterministic digest of data. Any alteration to the input—even a single bit—produces a completely different hash, making unauthorized modification immediately detectable.

• Content Addressability: Artifacts are identified by their hash, not a mutable name
• Avalanche Effect: A minor input change cascades into a radically different output
• Collision Resistance: Computationally infeasible to find two different inputs with the same hash

A tree data structure where every leaf node is labeled with the hash of a data block, and every non-leaf node is labeled with the cryptographic hash of its child nodes. This enables efficient and secure verification of large datasets.

• Allows verification of a single record without downloading the entire dataset
• Used in transparency logs like Rekor to prove inclusion
• Forms the backbone of blockchain integrity and distributed version control

A mathematical scheme for verifying the authenticity and integrity of digital messages or documents. A valid signature gives a recipient reason to believe the message was created by a known sender and was not altered in transit.

• Generated using a private key; verified with the corresponding public key
• Provides non-repudiation: the signer cannot deny authorship
• Standards include ECDSA, Ed25519, and RSA-PSS

A security mechanism that uses digital signatures to allow a system to verify both the integrity and the publisher of a specific piece of content. In container and model registries, content trust ensures only authorized, untampered artifacts are deployed.

• Notary is the reference implementation for the TUF specification
• Enforces signature validation at pull time, not just at push time
• Prevents rollback attacks by requiring fresh, timestamped metadata

A cryptographically signed statement that asserts a verifiable fact about a software artifact. In the context of data integrity, attestations provide a tamper-proof record of the provenance, build process, and vulnerability scan results for model artifacts.

• in-toto defines a standard layout for supply chain attestations
• SLSA Provenance attests to the build platform, source repo, and entry point
• Enables policy engines like OPA to make deploy-time decisions based on verified claims

An append-only, cryptographically verifiable ledger that records the issuance of digital signatures and certificates. By publishing signed records to a transparency log, the act of signing becomes publicly auditable and retroactive forgery becomes detectable.

• Rekor is the transparency log in the Sigstore ecosystem
• Uses Merkle trees to efficiently prove inclusion of a specific entry
• Enables keyless signing by binding ephemeral keys to verified identities