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In the last few years, the rapid advent of AI has transformed how organizations operate, store knowledge and make decisions. With the focus squarely on speed of AI implementation and improving productivity, many organizations have rolled out AI initiatives without proper risk analysis.

Now, if you are a business leader and have invested in implementing AI across your organization, you just might have created a backup vulnerability your IT team is not prepared to handle. Traditional backup systems rarely have the flexibility to back up agent logic, learned behavior and even elementary stuff like prompt configurations and embedded indexes.

The new layer that organizations seem to overlook

Most organizations today, including some large enterprises, have failed to upgrade their traditional backup strategy despite going the whole hog on AI.  The reluctance to think beyond their time-tested 3-2-1 backup strategy owes to their approach of focusing only on AI output.

However, they seem to ignore non-traditional data that the AI builds up, like embedded indexes that were updated basis of weeks of ingested data. Or say a series of prompt chains that your teams used to generate ideal output or custom-trained retrieval systems primed for your company-specific data.  These data are typically not available at a specific location, and most standard backup processes blissfully ignore them.

Why traditional backup systems fail to cover AI workflows

For decades, traditional backup processes relied on the time-tested 3-2-1 backup strategy. Essentially, the plan involved making three backup copies of the data, storing them in at least two different media types and keeping one copy outside the premises, say on the cloud. It worked perfectly and still works perfectly for most systems, ranging from ERP to CRM.  However, AI seems to fundamentally go against the concept of capturing a state of data and recovering it if a data incident occurs.

AI systems are designed to learn and improve, and these context embeddings are critical for their effective performance. As models learn, they develop fine-tuned behavior, build customized data pipelines and work through vector databases. A traditional backup system cannot map these AI assets to a file or even a location and thus fails to preserve them.

At times, IT teams cannot even figure out what went wrong after restoring an AI system. As opposed to a ransom attack or disk failure, for example, there is no scary screen popping up or bad sectors coming up in scans. The restored AI systems may seem to work perfectly in normal fashion, but their output has suddenly become error-prone.

Agentic systems seem to compound the problem

With autonomous AI agentic systems getting introduced in organizations, the drawbacks of legacy backup systems became abundantly apparent. Agentic systems are entirely a different beast because they are not just producing some standardized output that a legacy system can attempt to back up. Instead, they are generating data in different forms and accumulating contexts in a continuous mode.

At times, multiple AI agents are interacting with each other, and the accumulated intelligence itself becomes a critical business asset. Traditional backup systems have no way to restore the intelligence once a data loss event has occurred, nor were they ever built to access the different formats and compute environments where AI agents operate.

Building a modern backup strategy for AI systems

To ensure AI systems work perfectly after a recovery event, it is critical to make sure backup systems are able to cover everything that allows the system to generate learned output. Also, organizations need to make a fundamental effort towards enabling discipline when dealing with AI systems.

To start with, companies need to maintain an AI asset inventory, which serves as a dynamic catalogue of every component that is involved in AI functioning. This would include model version, training datasets and enhancements made on them, prompt libraries used by different teams, configuration files related to agents, indexes, vectors, and many other related elements.

Once assets have been listed, an effort should be made to cover every crucial element in a planned manner. Training data and model customizations should be versioned and stored with track changes. In fact, the backup frequency of this data should be high, and AI itself can be forced to trigger backups on a specific change signal. Snapshots based on daily checkpoints or even in real-time should be enabled for critical elements like vector indexes.

Next up, companies need to have detailed discussions with AI vendors about data access and backup portability when model fine-tuning constructs are present in vendor infrastructure. A clear-cut operational plan must be put in place to deal with contingencies.

The success of restoration should be measured by baseline performance and not simply getting the service back online again.

The risk associated with backup gaps in your organization with respect to AI can be significant. In case of a recovery event, you may find your AI systems sharing poor or inaccurate results. In critical processes and decision systems, such gaps can lead to qualitatively inferior output that can cause actual losses to your stakeholders. Thus, business leaders should act proactively and invest in modern backup paradigms that can effectively handle AI operational assets and artifacts.