Why Protecting Your AI Data Should Be a Top Priority

The breakneck pace of AI deployment across enterprises is creating a monumental challenge for executives and company boards. In contrast to traditional IT systems, AI data and related ecosystems, which encompass everything from LLM models and training data to custom prompt data, have emerged as valuable intellectual property. They often represent millions of dollars in investment and months or even years of engineering effort.

Any loss of AI data can cause catastrophic loss for an organization, especially those that have integrated crucial processes, such as decision-making and risk analysis, with AI systems. If AI systems get compromised or the integrity of their results comes into doubt, it can cause loss of both customer trust and revenue.

In some edge cases, you may even need to build everything up from scratch. Thus, executives need to make crucial decisions around securing AI data and enabling business continuity.

In this guide, we offer a comprehensive framework for leaders entrusted with implementing AI initiatives with a core focus on strategic decisions.

Step 1: Identify and classify your “crown jewel” AI assets

As an executive, the first action that you need to undertake is getting your team to perform a comprehensive audit of what actually needs to be secured. It is important to realize the full scope of AI infrastructure and its complexity.

Typically, the backup strategy should make allowances for different kinds of asset categories. To start, preserving proprietary training data sets is crucial as they form the foundation. Losing them can cause irreparable loss as they are often cleaned and compiled over the years.

Tuned models form the next asset type as they are fine-tuned by specific use cases and ingrain domain expertise. Prompt libraries that include curated instructions also need to be preserved, as they were refined through continuous experimentation. Finally, the pipeline codes and workflow data also need to be preserved.

When it comes to prioritizing your backup investments, you should ask yourself and the leadership about the impact of losing a particular asset type. Not all data is equally valuable, and you need to take a conscious decision to robustly protect critical data.

Step 2: Choose your strategic backup architecture (the 3-2-1 rule)

The time-tested gold standard of data protection, which involves maintaining three copies of your data, in two different media types and one off-site, holds even in the AI era. When it comes to AI data, the primary data copy would live in the live production environment. The second copy can be kept on a network-connected backup or on-premises storage for fast recovery. The third copy can be kept off-premise on the cloud, located in a different geographic area.

While it may seem quite straightforward, as an executive, you need to make decisions with respect to the type of cloud storage you choose, especially given the prevalence of huge datasets. Enforcing secure encryption and opting for private clouds may also fall on your plate.

Step 3: Automate and orchestrate — “set it and audit it”

As an executive, you should clearly steer your organization away from manual backups, which are prone to human carelessness. Instead, opt for backups to be integrated with machine learning ops (MLOps). Systems should be put in place that trigger backups after specific events, like training runs or new data ingestion.

After the process is set, ensure proper audits and testing mechanism is put in place. KPIs for checking recovery performance should be implemented, and simulated recovery exercises should be regularly performed.

Common executive pitfalls to avoid

When it comes to implementing AI data backup, even the most technologically mature companies can come up short. Four typical pitfalls befall organizations in this journey, with the very first being the most surprising. Organizations judiciously back up the AI data being created, but fail to back up the metadata related to the model version or the related environment parameters. This leads to a model drift when you perform a restore operation. While the data is available, the exact model behavior is missing.

The second pitfall is noticed when companies fail to preserve the online learning data from live production systems. AI models tend to improve iteratively based on their interaction with users, and missing out on backing up the critical improvements post-deployment is a major miss.

The third pitfall involves treating AI backups in the same vein as IT backups without factoring in the unique challenges related to data complexity, expansive scale and constant data flux.

Last but not least, failure to assign proper ownership of a cross-functional activity that involves data engineering, tech and leadership teams is commonly noticed. Make sure you assign explicit responsibility and empower the leader with an executive mandate to bridge gaps 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 crucial organizational goal.

It would be prudent for you to task your CTO or Data Lead to review the current practices against this framework and identify gaps. A thorough analysis and subsequent remedial action are vital for protecting your valuable AI data. The cost of building sophisticated backup infrastructure and robust processes is trivial against possible data loss scenarios where you may end up losing more than just revenue. A cutting-edge AI backup strategy is a failsafe against loss of consumer trust and a hallmark of a resilient organization.

The breakneck pace of AI deployment across enterprises is creating a monumental challenge for executives and company boards. In contrast to traditional IT systems, AI data and related ecosystems, which encompass everything from LLM models and training data to custom prompt data, have emerged as valuable intellectual property. They often represent millions of dollars in investment and months or even years of engineering effort.

Any loss of AI data can cause catastrophic loss for an organization, especially those that have integrated crucial processes, such as decision-making and risk analysis, with AI systems. If AI systems get compromised or the integrity of their results comes into doubt, it can cause loss of both customer trust and revenue.