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Key Takeaways
- AI data has become valuable intellectual property, and failure to protect it can cause catastrophic losses to an organization.
- To protect your AI data, you must first identify and classify your “crown jewel” assets. Then choose your strategic backup architecture.
- You should also move your organization away from manual backups and opt to integrate backups with machine learning operations.
The rapid pace at which AI is being deployed in enterprises poses enormous challenges for executives and corporate boards. Unlike traditional IT systems, AI data and related ecosystems, which include everything from LLM models and training data to custom prompt data, have emerged as valuable intellectual property. They often represent millions of dollars of investment and months or even years of engineering effort.
Any loss of AI data can cause catastrophic losses to an organization, especially those that have integrated critical processes, such as decision-making and risk analysis, with AI systems. If AI systems are compromised or the integrity of their results is questioned, it can lead to a loss of both customer trust and revenue.
In some edge cases you may even have to rebuild everything from scratch. So executives must make critical decisions about securing AI data and enabling business continuity.
In this guide, we provide a comprehensive framework for leaders charged with implementing AI initiatives, with a core focus on strategic decisions.
Step 1: Identify and classify your “crown jewel” AI assets
As a leader, the first action you should take is to have your team conduct a comprehensive audit of what actually needs to be secured. It is important to realize the full scope of the AI infrastructure and its complexity.
Typically, the backup strategy must take into account different types of asset classes. For starters, maintaining native training datasets is crucial as they form the foundation. Losing them can cause irreparable damage as they are often cleaned and put together over the years.
Tuned models are the next asset type because they are tailored to specific use cases and baked-in domain expertise. Prompt libraries that contain compound instructions should also be retained, as they have been refined through constant experimentation. Finally, pipeline codes and workflow data must also be preserved.
When it comes to prioritizing your backup investments, ask yourself and leadership what the impact is of losing a certain type of asset. Not all data is equally valuable and you must make a conscious decision to robustly protect critical data.
Step 2: Choose your strategic backup architecture (the 3-2-1 rule)
The proven gold standard for data protection, which involves keeping three copies of your data, on two different media types and one externally, holds true even in the AI age. When it comes to AI data, the primary data copy would be in the live production environment. The second copy can be kept on a network-attached backup or on local storage for quick recovery. The third copy can be kept off-premise in the cloud, in a different geographic area.
While it may seem quite simple, as an executive you have to make decisions regarding the type of cloud storage you choose, especially given the prevalence of massive data sets. Enforcing secure encryption and opting for private clouds may also be on your plate.
Step 3: Automate and orchestrate – “set it and control it”
As an executive, you must clearly steer your organization away from manual backups, which are prone to human carelessness. Instead, choose to integrate backups with machine learning operations (MLOps). Systems should be set up that trigger backups after specific events, such as training runs or new data ingestion.
Once the process is in place, ensure that the appropriate audits and testing mechanisms are in place. KPIs should be implemented to monitor recovery performance, and simulated recovery exercises should be conducted regularly.
Common managerial pitfalls to avoid
When it comes to implementing AI data backup, even the most technologically mature companies can fall short. Organizations encounter four typical pitfalls during this journey, of which the first is the most surprising. Organizations wisely backup the AI data being created, but fail to backup the metadata related to the model version or the related environment parameters. This leads to model drift when you perform a restore operation. Although the data is available, the exact model behavior is missing.
The second pitfall occurs when companies fail to retain online learning data from live production systems. AI models tend to improve iteratively based on their interactions with users, and missing out on backing up the critical improvements after implementation is a big miss.
The third pitfall involves treating AI backups the same as IT backups, without considering the unique challenges associated with data complexity, scale, and constant data flow.
Last but not least, the inability to properly own a cross-functional activity involving data engineering, technology, and leadership teams is often noted. Make sure you assign explicit accountability and give the leader an executive mandate to bridge the gap between different teams.
As AI systems and the data they encompass increasingly become a key differentiator of competitive advantage, investing in AI resilience becomes a critical organizational goal.
It would be wise for you to task your CTO or Data Lead to review current practices against this framework and identify gaps. Thorough analysis and subsequent remediation are critical to protecting your valuable AI data. The cost of building advanced backup infrastructure and robust processes is trivial compared to potential data loss scenarios that could leave you losing more than just revenue. An advanced AI backup strategy is a failsafe against loss of consumer trust and a hallmark of a resilient organization.