Verifiable Data: How Walrus Solves the Quality Crisis That Costs Billions

The industry faces a silent but devastating problem: the inability to verify whether the data we rely on is truly trustworthy. From machine learning to digital advertising, critical systems are built on information whose authenticity can never be confirmed. The solution requires data to be verifiable from its source.

In a world where AI manages credit decisions, hiring, and medical diagnoses, the risk is exponential. An astonishing 87% of AI projects never reach production, and the blame is not on the algorithms but on the poor quality of the data feeding these systems. For an industry moving $200 billion, this figure represents a massive failure.

The impact goes beyond AI. Digital advertising, a $750 billion annual market, loses nearly a third of its investments to fraud and inaccuracies, mainly because transactions can never be reliably audited. Even tech giants like Amazon have had to abandon entire projects after investing years in development, discovering that training data replicated discriminatory biases. When an automated system makes a critical decision, there is rarely a way to trace and verify the integrity of the data that originated it.

The Hidden Cost of Unverifiable Data in Critical Industries

Faulty data not only breaks algorithms; it amplifies their flaws on a massive scale. A model trained with biased, corrupted, or inaccurate information does not make random errors but systematically replicates and worsens the biases in its training data.

Amazon’s case illustrates this reality. Its recruiting tool was not designed to discriminate but “learned” to do so by being fed historical records dominated by male hires. There is no algorithm sophisticated enough to overcome a fundamentally contaminated dataset.

The challenge goes beyond incorrect data itself. Training datasets are collected and processed without leaving a verifiable trail of their origin, modifications made, or changes in their integrity. When these data train systems that decide on loans, diagnoses, or promotions, there is no mechanism to demonstrate where they came from or if they were altered.

Cryptographic Verification as a Foundation of Trust

Building reliable AI requires something no larger data center or faster processor can provide: data whose authenticity can be verified from the first byte. Walrus implements exactly this, enabling end-to-end data verification.

Under this model, each file gets a unique and verifiable identifier. Every change is recorded in the chain of custody. Developers can cryptographically prove where their data came from, who modified it, and whether it remains intact. When a regulator questions a fraud detection model’s decision, it’s possible to present the blob’s identifier (a unique hash generated from the data itself), show the Sui object that documents its storage history, and cryptographically verify that the training data was never altered.

Walrus integrates with the Sui stack to coordinate on-chain programs, establishing a trust layer where data is reliable and verifiable by design, not by good faith assumptions.

Success Stories: From Amazon to Alkimi

Digital advertising is another sector devastated by unverifiable information. Advertisers invest in a $750 billion market but face inaccurate reports, systematic fraud, and bot-generated impressions. Transactions are fragmented across multiple platforms, and performance measurement systems are the same ones that benefit from inflated numbers.

Alkimi is redesigning the landscape of programmatic advertising by making all data verifiable. Every impression, bid, and transaction is stored in Walrus with immutable records. The platform incorporates encryption for sensitive customer data and processes reconciliations with cryptographic proof of accuracy, making it the ideal solution for industries where data must be, above all, verifiable.

The Future of Verifiable Data in DeFi and AI

What begins in AdTech only scratches the surface of possible applications. AI developers could eliminate biases by selecting datasets whose provenance is cryptographically verifiable. DeFi protocols could tokenize verified data as collateral, similar to how AdFi transforms proven advertising revenue into programmable assets. Decentralized data markets could thrive as users monetize personal information while maintaining privacy guaranteed cryptographically.

All of this is possible because, for the first time, data no longer requires blind trust: it can be mathematically proven. WAL ($0.09 at the time of writing) forms the economic backbone of this ecosystem, incentivizing participants to maintain data integrity within the Sui network.

When Data Becomes Verifiable by Default

Incorrect data has paralyzed entire industries for too long. Without the ability to verify the reliability of our data, we cannot move forward with the innovations this century promises: from trusted AI systems to DeFi protocols that prevent fraud and exclude malicious actors in real time.

Walrus establishes the infrastructure that makes this change possible. By building on a platform where data is verifiable from its creation, organizations can trust from day one that their systems are built on solid, objective foundations. The future will not be faster or bigger, but more verifiable.