Atlassian CTO on realistic AI: Rovo, data privacy and adoption
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Atlassian CTO on realistic AI: Rovo, data privacy and adoption
"Most important of all: Atlassian doesn't train AI models on customer data. In an industry that seems to rely more and more on user information for competitive advantage, this commitment isn't without its challenges. Dive into the world of Atlassian with us in this new episode of Techzine TV. Subscribe to Techzine Talks on Tour and watch and/or listen to our other episodes via Spotify, Apple , YouTube or another service of your choice."
"Central to Atlassian's AI strategy is the Teamwork Graph, a knowledge graph with extra capabilities. It maps who works with whom on which documents at which point in time. Unlike traditional knowledge graphs, this technology maintains real-time and historical connections across an organization's entire work ecosystem, according to Rajan.. The graph integrates data from over 80 applications, including Google Docs, Office, Slack, and Zoom, while respecting existing permission structures."
"The graph integrates data from over 80 applications, including Google Docs, Office, Slack, and Zoom, while respecting existing permission structures. When users search through Rovo, they only see results they're authorized to access. "If there's a sensitive financial report that only the CFO can see, it doesn't show this data in the search results to other people," Rajan notes. This permission-aware approach operates in real-time, using customer data for the customer's benefit without training proprietary models on it."
Atlassian maintains a pragmatic AI approach that forbids training proprietary models on customer data. The company treats customer data as the customer's data and positions itself as its custodian. The Teamwork Graph maps real-time and historical relationships across an organization's work ecosystem, integrating data from over 80 applications while respecting permission structures. Rovo surfaces search results only for authorized users, ensuring sensitive items remain hidden from unauthorized eyes. The permission-aware design operates in real time to use customer data for the customer's benefit without compromising privacy or using that data to train models.
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