Accelerating Recovery with AI-Enhanced Data Resilience

Recent research suggests that the average cost of unplanned IT downtime now averages almost $15,000 per minute, proving once again that time is money. This makes data resilience more important than ever, but it's becoming increasingly difficult to recover quickly and stem losses.

Today's ever-growing amount of business-critical data extends from network edge to data center core to multiple public clouds. What’s more, it’s under constant cyber threat, with a ransomware attack occurring every 19 seconds. As a result, the average time to identify and contain a breach now exceeds 250 days.

However, artificial intelligence (AI) is helping to tip the scales back in favor of efficient, effective data recovery. By leveraging machine learning algorithms and advanced analytics, AI can now eliminate much of the guesswork from the recovery process, turning what was once like finding a needle in a haystack into a precise operation.

Elements of AI-enhanced Data Resilience Strategy

Here are some of the key ways AI is enhancing data resilience to facilitate faster recovery:

Intelligent Asset Discovery and Protection

AI-powered data resilience systems can continuously scan infrastructure to identify unprotected assets, a critical capability in today’s highly dynamic IT environments. These systems can then recommend and even apply tailored protection policies that integrate seamlessly with existing frameworks. This ensures that all critical data receives appropriate protection, closing any potential gaps in a recovery scenario.

Related:5 IT Tips to Ensure Business Continuity

Enhanced Security and Accelerated Threat Response

AI is making cybersecurity smarter and more proactive. AI-enhanced data resilience systems can detect threats faster and with greater accuracy by employing techniques such as hash-based malware tracking and sophisticated blast radius analysis. This acceleration in threat detection translates to quicker comprehensive containment, the first step in the recovery process. In the event of a ransomware attack, AI can rapidly analyze the scope of the infection, identify affected systems, and establish recovery priorities. This targeted approach significantly reduces downtime and potential data loss.

Automated Recovery Blueprints

One of the most promising applications of AI in data resilience is its ability to create, automate, test, and maintain recovery workflows for a multitude of different attack scenarios before cybercriminals strike. These AI-generated plans include possible interdependencies between networks, applications, and data stores. By constantly mapping and re-mapping these relationships, AI-driven data resilience systems can help orchestrate recovery processes that better align with business continuity objectives.

Intelligent Recovery Point Selection

Selecting the right recovery point is crucial for minimizing data loss and ensuring business continuity, but this traditionally involved slow manual analysis and a degree of guesswork. AI modernizes this process by recommending optimal recovery points based on a comprehensive risk engine analysis of data integrity, application consistency, and potential impact on dependent systems. By eliminating the need to manually identify the “last known good copy” of data, AI not only saves valuable time but also reduces the risk of human error.

The Future of Data Resilience 

As AI continues to evolve, expect even more sophisticated data resilience systems that will help further speed up recovery. As agentic AI matures, data resilience systems endowed with proactive reasoning and decision-making capabilities will fuel fully autonomous data management.

It's important to note, however, that AI is not infallible. Its effectiveness relies on quality foundational data, well-defined processes and guardrails, and human expertise to guide and refine its operations. Organizations looking to leverage AI for enhanced data resilience should view it as a powerful tool to augment and accelerate their existing strategies, not as a replacement for sound data management practices.

As we look ahead, the trajectory is clear: the complexity of and threat against data will continue to grow, and the cost of downtime will continue to increase. In this reality, AI-based data resilience systems are quickly becoming a necessity.