Verifiable AI Data: Why It’s Critical for the Automation Revolution

That’s an enterprise-level existential threat.

In an age of manipulated data, deepfake techniques, and unending data breaches, companies need to know that they are using pristine data in their AI systems.

Toxic data will wreak havoc in business systems and cripple an organization’s faith in AI and its customers’ faith in it.

Before understanding how and why decisions were made, organizations must be able to stand by the integrity of the data and algorithms used by AI.

This might be called Verifiable AI — when an organization can provide immutable proof that the data used by their AI systems is unaltered.

So how do vendors implement Verifiable AI into their products to ensure that their AI algorithms are not handling data that has been tampered with?.And how do companies build Verifiable AI into their systems to verify that they are using safe data?.Two leading pharmaceutical companies — Merck and Company, Inc.

and Amerisource Bergen — have implemented a fast-track project to more efficiently detect counterfeits and irrefutably document compliance at scale.

The project is designed to Track and Trace billions of items throughout their supply chains.

In just a few weeks they were able to ensure: Full regulatory compliance around sellable return verificationsInstant verification of the authenticity of returned itemsNo replication of manufacturer data required for wholesalersMinimal complexity, maximal security Immutable, single source of truth provided to all parties, including regulators Interoperable with existing Track and Trace solutions Scalable-to-consumer scanning at the point of dispense With this project, these companies were able to achieve full data verification at multiple stages across their supply chain.

By extending the real-time Track and Trace ability, they are now able to add a new “Train” element into the process.

The verification that is used to ensure that their supply chain is not handling counterfeit goods can also be used to verify that data used to train AI systems for supply chain automation is also safe.

For these companies, Track, Trace and Train is an additional benefit of a chain of custody solution, but for many other companies, this will be their core safeguard of their AI systems.

Data tampering is now the single greatest cybersecurity threat that organizations face.

From a simple act of revenge by a disgruntled employee, to corporate espionage, or even a nation-state attack, data tampering is an existential threat that cannot be ignored.

While AI automation holds the promise of operational efficiencies, it also has the ability to introduce and replicate toxic data across an enterprise, into business systems and decision-making, and expose customers and partners to those very same risks.

The AI automation revolution is already upon us.

But its success hinges on the authenticity of the data that drives the AI, and the ability to verify this authenticity.

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