Overcome your trust issues! In a world of fake data, Data Provenance FTW
How can you trust your data when AI can fake anything? This talk introduces a 'verify then trust' model for the modern digital supply chain.
#1about 6 minutes
Understanding the risks of the modern software supply chain
Vulnerabilities in common dependencies like Log4j demonstrate how the entire software ecosystem is at risk from a single point of failure.
#2about 12 minutes
Moving beyond software to address the data supply chain
The problem of trust extends from code to data, where AI-generated images and fraudulent documents pose significant business risks.
#3about 4 minutes
The failure of traditional perimeter-based security models
Despite record spending on cybersecurity, high-profile breaches like stolen signing keys show that traditional security approaches are no longer sufficient.
#4about 4 minutes
Introducing a new trust model based on data provenance
A shift to a "verify then trust" model using data provenance provides a more robust way to ensure integrity in a highly connected world.
#5about 4 minutes
Core principles of the IETF standard for integrity
The SCITT standard establishes trust through three key principles: strong identification, immutability, and transparency to prevent equivocation.
#6about 4 minutes
Implementing data provenance with APIs and distributed ledgers
An open source API uses distributed ledger technology to create verifiable, offline receipts for any data attestation, from IoT to SBOMs.
#7about 9 minutes
A live demonstration of creating a verifiable data claim
A simple API call attests a piece of data, creating an immutable and publicly verifiable record on a distributed ledger.
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