The world of blockchain spans from total decentralization (referred to as “crypto-minimalism) to federated distributed ledgers to private cryptographically-secured databases. Across all of these use cases, digital trust is a core benefit of employing the crypto-based elements of a blockchain ledger. For an enterprise data environment, DLTs can lock transactions in a cryptographically-secured database in such a way that information can be verified and traced back to its originator with ease.

How Does Blockchain Provide Trust, Proof, and Provenance to the Information?

Every update to a ledger-backed database is timestamped and embedded into a “block” of data, cryptographically secured by a hashing process that incorporates the hash of the previous block, and joins the ledger as the next chronological update. 

In the same way that blockchain provides digital trust for transactions between third parties, distributed ledger technologies leverage cryptography and hashing to build an immutable, verifiable audit trail for records in a database or application. These benefits can be leveraged practically for a developer building an app or integration, for departments of highly-regulated businesses conducting audits, or for data scientists building ethical and explainable AI. 

Let’s look at 3 practical applications for ledger-backed data management for organizations:

1. Non-Repudiation

Non-repudiation is a legal concept that means someone cannot deny the authenticity of something – a piece of information, a document, a signature, etc. Widely used in industries where trust is essential between parties, such as law, finance, or insurance, non-repudiation is an essential information security practice that provides proof of the origin and authenticity of information as it is exchanged between parties. 

Blockchain provides a perfect platform for businesses to practice non-repudiation. The use of digital signatures and public/private key cryptography allows for information to be digitally verified by parties sending or receiving messages. As a result, organizations can adopt better non-repudiation mechanisms for digitally signing documents or retrieving verified messages, which increases the speed and scale of their third-party business dealings. 

2. Change-Data Capture and Trusted Data Audit

Having an immutable log of data changes provides immense benefits – developers can identify, review, and pull all of the changes to a database and make use of the historical information in another system. Because blockchain preserves every state to a ledger database, developers can capture those changes for review analytically or for use in another system. Developers can begin to ask the ledger for historical information, such as “What did the data look like at this point in time, and how has it changed since then?” 

A blockchain ledger can also provide professionals in highly-regulated industries with data provenance guarantees and replayability for instant data audit and reporting. Financial services, healthcare, and other regulated systems can use ledger databases to reproduce data alongside a timestamp to prove compliance.

3. Explainable and Secure AI

In the fields of machine learning and artificial intelligence, data scientists are increasingly concerned with the concept of “explainable AI,” a process in which humans can review, comprehend, and trust the output results of an algorithm. Explainable AI practices help data scientists retrace and explain the results of an AI algorithm, which allows for fairness and transparency in AI development.

If input data existed in a blockchain database system, AI professionals could build tools to audit the algorithm thoroughly to reveal important information on explainability. By providing an immutable and cryptographically-secured approach to data management, a blockchain structure delivers a historical lens for data scientists to review and assess. 

Blockchain can also provide a defense against adversarial attacks to AI algorithms, fighting against “data poisoning” in which attackers will manipulate input data in order to modify the outcome of an algorithm. By leveraging blockchain’s source of truth and anti-manipulation cryptography, AI developers can guarantee that an algorithm is using authentic data from verified sources.

Conclusion

Leveraging a blockchain-secured database can provide organizations with a foundation for trusted and traceable information management. The applicability spans far and wide – from generalized data audit capabilities to industry-specific use cases such as digital evidence management systems (DEMs), AI Explainability systems, or compliance reporting. Whichever the scenario, blockchain can help move organizations away from “black-box” reporting and into a more transparent, accurate, and trusted system for data management.