A future-proof AI strategy can position enterprises to capitalize on the business value of their data for years to come. However, they also need to ensure they don’t lose control over that data in the process. That’s why data governance is an essential part of any effective AI strategy. With the right approach to data governance, you can ensure data privacy and avoid unexpected regulatory penalties.
If data is the fuel that moves your enterprise AI strategy, then you must use the right data in the right places. For one thing, emerging regulations may include requirements to track the lineage of data used for training AI models. You must grow your AI datasets while also meeting these requirements. Using a federated learning model, where ecosystem partners exchange data with one another on a neutral platform, is one way to do it.
You also need to build your distributed AI data architecture to align with your regulatory requirements. For instance, deploying AI inference pods at the edge can help ensure inference data stays in the right locations to meet residency and sovereignty requirements. However, inference pods also need to be linked with the rest of your global AI infrastructure via dedicated, private connections that keep AI data protected while in motion.
Finally, every enterprise has some data that’s simply too sensitive to trust to public infrastructure. This is where private AI can help. Your business can build AI models for your own private use, running on infrastructure that you control. You can feed your proprietary datasets into these models, uncovering the business value in that data without letting it leave your security domain.
To help you better understand why governance should be an essential part of your AI-ready data strategy, we brought together four industry experts to share their insights on the topic. Read on to learn their thoughts.
Meet data governance requirements while maximizing the value of AI
“With private AI, your sensitive data stays close to the source and secure within your domain. That means you can build and use AI models on private infrastructure, unlocking value from your proprietary data without moving it to external locations.”
– Aaron Delp, Director – AI Technical Solutions, Equinix
Hear more from Aaron in this video:
Establish clear data governance policies that adapt to evolving regulations
“To future-proof your AI data strategy regarding governance, ensure robust privacy and compliance frameworks are integrated into your systems. Establish clear data governance policies that adapt to evolving regulations, such as GDPR or CCPA. Prioritize data quality and transparency, incorporating explainability for AI models and decision-making processes. Continuous monitoring and auditing are employed to detect and address potential risks and biases. Regarding data location for AI workloads, consider adopting a flexible residency strategy that complies with local data sovereignty laws. Utilizing cloud services with region-specific data centers can help maintain compliance, optimizing data storage and processing locations based on regulatory requirements and operational needs.”
— Zeus Kerravala, CEO & Principal Analyst, ZK Research
In an evolving regulatory environment organizations should prioritize implementing transparency and control over their data
“AI’s ability to extract deep insights from data increases the need for strict data residency and data governance policies. In an evolving regulatory environment organizations should prioritize implementing transparency and control over their data. At Alembic, we maintain a rigorous US-based data residency, and do not store any personally identifiable information. This approach streamlines compliance and ensures the proper safeguards are in place.”
— Abby Kearns, CTO, Alembic
Businesses need a simpler, faster path to accessing AI infrastructure
“Enterprises committed to AI need their own AI “factories” to scale application development leveraging tools, accelerated computing, and facilities optimized for these new demands. Businesses therefore need a simpler, faster path to accessing AI infrastructure that’s data-proximal, securely hosted, and maintained within facilities that are multicloud interconnected, while ensuring sovereignty of their data and intellectual property.”
— Tony Paikeday, Senior Director, AI Systems, NVIDIA
Learn more about how to get AI-ready
Check out the Equinix Indicator for more expert insights and resources about how you can future-proof your AI-driven data strategy. In addition to data governance, you’ll also learn how to deploy AI-ready infrastructure in all the right places and take advantage of cloud connectivity and multicloud environments to ensure flexibility in your AI strategy.
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