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7 areas where AI can support disaster response
Responding to a disaster requires IT teams to move quickly, with no room for error. Find out how AI-based technology can help before, during and after a crisis.
AI tools can help improve business decision-making and efficiency, especially in the high-stakes field of disaster recovery. In addition to DR planning, some organizations use AI to guide them toward the best incident response options.
There are generally three disaster response and recovery phases where AI tools can help: before the incident strikes, during the incident and post-incident response. In the first phase, AI tools are mostly predictive and used for planning and testing. During a disruption, AI might assist with communications, resource allocation and real-time monitoring. After the disaster has passed, DR and IT teams can use the information that AI tools learned to mitigate future incidents.
Disaster response and management is a critical operation, and there is no room for error. When integrating AI into a disaster recovery plan, be careful to consider where it works best and make sure the products have been properly tested in nondisaster scenarios.
The technology is still developing, so a complete overhaul to AI disaster response tools is unlikely for most organizations. However, there might be some processes within a disaster recovery strategy where AI can fit in and improve operations. Below are seven different areas of disaster response that might be aided by AI tools.
1. DR scenario planning and testing
Tabletop exercises are one of the most accurate and reliable methods of testing out a DR plan from start to finish. AI disaster recovery tools can help businesses create tabletop exercises, plan test scenarios and provide full-interruption testing to see how a DR plan will play out. These test simulations are a critical component of any DR plan, and properly trained AI tools can help make sure they are relevant, accurate and complete.
2. Automated response and recovery
AI can provide consistency, predictability and calculated automated responses in a disaster. This enables DR teams to act rapidly when an unexpected disruption occurs. In response to a crisis, AI can initiate failover actions, manage data replication and begin recovery processes.
3. Log analysis and incident response
AI's ability to analyze log files and traces quickly and from multiple sources enables it to react swiftly to cybersecurity incidents, service interruptions due to natural disasters or misconfigurations that cascade across an organization's infrastructure.
4. Communications management
Communication is a critical component of any disaster response plan. Various stakeholders, law enforcement officials, first responders, employees and customers must be informed of service interruptions, safety concerns and other expectations.
AI helps facilitate communication in several situations, including the following:
- Automated notifications to relevant people and organizations.
- Message prioritization among stakeholders.
- Real-time status updates on websites and social media.
- Chatbots to provide information and instructions if human support is unavailable.
- Social media monitoring for community sentiment, misinformation and influential voices.
5. Real-time monitoring and analysis
AI is capable of aggregating and parsing massive amounts of data quickly, ensuring effective monitoring during disasters. Real-time monitoring can even provide early warnings of impending incidents, including cyberattacks, major cloud outages or upcoming weather events.
6. Prioritized restore processes
Highly complex multi-cloud and hybrid cloud environments rely on interconnected services and communication paths. Prioritization helps make sure the most critical functions are restored first, which can be a difficult task even without a crisis actively taking place. AI can help restore these resources in the appropriate order to ensure full communication and functionality. Automated responses are rolled into this larger set of prioritized recovery steps to benefit from their consistency and speed.
7. Continuous learning for future events
Until recently, analyzing historical data for disaster recovery was challenging. Sifting through natural disaster reports, looking for patterns in human mistakes and anticipating everything that could possibly go wrong is a complex task. AI can quickly analyze and draw conclusions from data to guide future DR plans.
An AI platform's ability to respond to disasters will only improve over time as it gains access to more logs and incident responses. It will also benefit from the results of DR test exercises and accumulated data from other organizations.
Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget and CompTIA Blogs.
