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Space ISAC Publishes White Paper on Machine Learning Security Operations Framework

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Space ISAC Publishes White Paper on Machine Learning Security Operations Framework
machine learning

A Space Information Sharing and Analysis Center white paper offers considerations for using machine learning security operations and trustworthy artificial intelligence systems to protect the functionality of national space assets.

Space ISAC said Tuesday the center’s AI and ML community published the white paper on the MLSecOps framework.

Max Spolaor, senior engineering specialist at the Aerospace Corp. and co-author of the paper, described MLSecOps as a game-changing framework that offers the tools and best practices needed to establish and sustain “trust, security and reliability by capturing the data-centric rather than code-centric operational philosophy of AI/ML technologies.”

“Fortifying our machine learning systems with MLSecOps will move us toward greater space resiliency and open new possibilities as the demand for secure, reliable AI technologies in space continues to grow,” said Michelle Archuleta, director of data science at RS21 and one of the report’s co-authors.

Established in 2019, Space ISAC serves as a source of threat security data for space organizations in both public and private sectors. Its founding members include Kratos Defense & Security Solutions, Booz Allen Hamilton, the Aerospace Corp., MITRE, Lockheed Martin, Northrop Grumman, Parsons, Microsoft, Deloitte, L3Harris Technologies and the Johns Hopkins University Applied Physics Laboratory.

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