Domino Data Lab achieves ISO 27001:2013 certification to help global enterprises deliver data science with greater confidence

The enterprise MLOps platform provider is raising the security bar to integrate data science into as many business processes as possible.

San Francisco, August 24, 2022 /PRNewswire/ — Domino Data Lab, provider of the leading enterprise MLOps platform trusted by more than 20% of the Fortune 100, proudly announced that it has achieved ISO 27001:2013 certification for its information security program in its first attempt. ISO 27001:2013 exceeds the information security requirements established by privacy legislation and raises the bar for end-to-end security expectations for companies involved in data science for their enterprise MLOps platforms.

“As companies increasingly use machine learning models to drive core business processes, security in their MLOps platform is critical,” said. Nick AlprinCo-founder and CEO at Domino Data Labs. “We’re proud to help our customers deliver data science at scale, giving them the confidence and peace of mind that their data and intellectual property are always protected.”

According to Gartner, “Over the past five years, the percentage of boards that consider cybersecurity a business risk has increased from 58% to 88%.”1 ISO 27001:2013 certification is often required for model-driven organizations in highly regulated and sensitive industries. This emerging dynamic requires worry-free, embedded security in every aspect of data science workflows and infrastructure, while the agility, flexibility and scalability data scientists need. Domino’s platform was created to support both the data science innovation needs and security needs of the most sophisticated global companies.

“AI poses substantial data risks as large, sensitive datasets are often used to train AI models and are shared across organizations,” Gartner said. “Access to confidential data needs to be carefully controlled to avoid adverse regulatory, commercial and reputational consequences.”2

ISO 27001:2013 certification is a demanding process that requires documentation and adherence to a strict framework for data security, risk mitigation, staff training, and continuous monitoring and improvement. This certification is a testament to Domino’s strong internal and executive commitment to meeting strict security standards to ensure the confidentiality, integrity, availability and security of its customers’ sensitive data and information.

Domino’s continues to hold itself accountable to the SOC 2 framework, validating controls set by third parties. The company’s third consecutive SOC 2 report by an independent auditor — detailing Domino’s compliance with SOC 2 security, privacy, and availability principles — is now available.

About the Domino Enterprise MLOps Platform

The Domino Enterprise MLOps platform helps data science teams improve the speed, quality and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models reach production faster and operate at peak performance with integrated workflows. Domino also provides the security, governance and compliance that enterprises expect.

About Domino Data Lab

Domino Data Lab is trusted by more than 20% of the Fortune 100 with its leading enterprise MLOps platform to help model-driven businesses. Domino accelerates the development and deployment of data science operations while increasing collaboration and governance. With Domino, global enterprises can develop better medicines, grow more productive crops, build better cars, and much more. Founded in 2013, Domino is backed by Couture Management, Great Hill Partners, Highland Capital, Sequoia Capital and other major investors. For more information, visit

  1. Gartner, 6 Key Takeaways from the Gartner Board of Directors Survey, October 21, 2021
  2. Gartner, “Market Guide to AI Trust, Risk and Security Management,” Unmarried Lytton, Farhan ChaudharyJeremy D’Hoine, September 1, 2021

Source Domino Data Lab

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