Jon Geater

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.

Overcome your trust issues! In a world of fake data, Data Provenance FTW
#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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Jobs that call for the skills explored in this talk.

AI Security Engineer

AI Security Engineer

Databricks
Amsterdam, Netherlands

Intermediate
C++
Python
PyTorch
TensorFlow
Machine Learning