AI is everywhere these days, and disaster recovery is no different. IT teams can use AI to mitigate, prevent and recover from disruptions faster than traditional methods.
IT disasters are unpredictable events that can severely affect businesses, causing loss of data, productivity and revenue. They can originate from myriad causes including natural catastrophes, hardware failures, cyberattacks and human errors.
While traditional IT disaster recovery methods provide a reasonable amount of planning, business leaders are learning how to use AI to improve DR processes. The emergence of AI offers new avenues to enhance IT disaster recovery and associated processes. AI can assist disaster recovery teams in six key areas: predictive insights, service recovery, automated response, cybersecurity, resource allocation and DR planning. The integration of AI into IT disaster recovery is not just a trendy addition; it's a significant enhancement that can lead to quicker response times, reduced downtime and stronger business continuity.
By proactively identifying risks, optimizing resources and continuously learning from past incidents, AI offers a forward-thinking approach to disaster recovery that could be the difference between a minor IT hiccup and a significant business disruption.
1. Predictive insights
AI, combined with machine learning algorithms, can predict potential IT failures by analyzing patterns in historical data. By looking through vast amounts of internal data such as logs, documentation and outputs from processes, it can pick up on anomalies that IT teams might have missed in isolation. Anomalies can include unusual patterns in server temperatures or sudden drops of processing power.
AI can put this information into context that could indicate potential future failures because it has more insight into how these issues might present. AI can also often provide remediation suggestions.
Simply put, AI-enabled predictive capabilities can significantly reduce downtime by alerting IT departments to issues before they become critical, enabling them to address problems proactively.
2. Service recovery
Data is the lifeblood of most businesses. AI can expedite data and service restoration processes by identifying the most important systems that need to be restored first, such as databases, communications tools and payment systems. This helps ensure that businesses can get up and running quickly.
This is especially crucial for companies operating in sectors where real-time data access is pivotal. What makes AI very useful in this kind of scenario is that it removes human emotion and questionable decision-making from the loop.
While various teams might be demanding that their application is restored first, an AI system will have worked out in advance what is needed to provide the best path to restoration with the least cost and disruption. AI also excels at finding potentially unforeseen dependencies ahead of time.
Customer service is another area of service recovery where AI can play a role. Chatbots can be used to effectively field and communicate service issues at a large scale with rapid response.
3. Automated response
AI-driven systems can automatically trigger a series of predefined recovery actions when an anomaly is detected, with appropriate safeguards. This can reduce the need for manual intervention from IT personnel, potentially speeding up the recovery process. The response might include backing up data to alternative locations, rerouting network traffic or even initiating failover procedures.
Automated response can considerably reduce recovery time objectives and recovery point objectives, making it a major benefit of AI disaster recovery. Automated response is a highly complex field and is not cheap. That said, when paired with a well-designed resilient infrastructure, it can help reduce the disaster in progress in terms of cost, impact and availability.
AI not only protects against data breaches, but can also help ensure business continuity.
4. Cybersecurity
A significant portion of IT disasters are due to cyberthreats. AI and machine learning can help mitigate these issues by continuously monitoring network traffic, identifying potential threats and taking immediate action to mitigate risks. Most new cybersecurity businesses are using AI to learn about emerging threats. They also use AI to look at system anomalies and block questionable activity.
In the event of a disaster, resources such as bandwidth, storage and compute power can become constrained. AI can optimize the use of available resources, ensuring that critical functions receive the necessary resources first. This optimization can greatly increase the efficiency of the recovery process and help organizations working with limited resources.
6. DR planning and updates
Postdisaster reviews are crucial for refining recovery processes. AI can automatically analyze the effectiveness of the recovery strategy that was implemented and suggest improvements.
Since AI is capable of continuous learning and adaptation, systems become better equipped to handle and recover from disasters over time. This can help strengthen DR efforts in the long term.
Stuart Burns is a virtualization expert at a Fortune 500 company. He specializes in VMware and system integration with additional expertise in disaster recovery and systems management. Burns received vExpert status in 2015.
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