Which practice improves data integrity for historian data stores by detecting tampering?

Which practice improves data integrity for historian data stores by detecting tampering?

• A. Implement integrity checks
• B. Encrypt data at rest
• C. Restrict access by role
• D. Secure connections

Answer: (A)

Detecting tampering and preserving data integrity in historian data stores relies on implementing integrity checks. These checks create a verifiable fingerprint of the data, such as cryptographic hashes or checksums, at the moment data is captured and later re-verified to confirm nothing has changed. By storing baseline digests in a secure, tamper-evident location and periodically recalculating and comparing hashes, you can quickly detect any alteration. Techniques like append-only or WORM storage, combined with tamper-evident logs or auditable records, make changes visible and support reliable audit trails. For large amounts of time-series data, scalable approaches like Merkle trees or batched hash manifests help verify integrity without slowing down access. This focus on verifying that stored data remains exactly as it was, over time, is what enables prompt detection of tampering. Encrypting data at rest protects confidentiality, not necessarily the ability to detect changes after the fact. Restricting access by role reduces the chance of unauthorized modification but doesn’t provide ongoing verification of data integrity. Securing connections protects data in transit, but the stored data’s integrity over time requires these fingerprinting and verification mechanisms.

Detecting tampering and preserving data integrity in historian data stores relies on implementing integrity checks. These checks create a verifiable fingerprint of the data, such as cryptographic hashes or checksums, at the moment data is captured and later re-verified to confirm nothing has changed. By storing baseline digests in a secure, tamper-evident location and periodically recalculating and comparing hashes, you can quickly detect any alteration. Techniques like append-only or WORM storage, combined with tamper-evident logs or auditable records, make changes visible and support reliable audit trails. For large amounts of time-series data, scalable approaches like Merkle trees or batched hash manifests help verify integrity without slowing down access. This focus on verifying that stored data remains exactly as it was, over time, is what enables prompt detection of tampering. Encrypting data at rest protects confidentiality, not necessarily the ability to detect changes after the fact. Restricting access by role reduces the chance of unauthorized modification but doesn’t provide ongoing verification of data integrity. Securing connections protects data in transit, but the stored data’s integrity over time requires these fingerprinting and verification mechanisms.