During the Maui wildfires in 2023, AI saved lives by predicting the fire’s path. Now, California fire departments regularly use AI for the same function. In the same vein, AI systems quietly work in the background to detect digital threats before they can breach key data.
They show that modern emergencies can no longer be managed by manual monitoring alone. With the speed and scale of AI, governments and companies can continue headstrong despite various challenges. Whether it’s an attack by nature or by digital threats, artificial intelligence is allowing us to prepare for, respond to, and recover from crises we otherwise would not have.
Disaster Detection in Real Time
Speed is everything when dealing with disasters. It can mean the difference between life and death when it comes to floods, fires, and earthquakes. With the help of AI, experts can now interpret seismic data, satellite imagery, and weather forecasts to predict events before they even happen.
Take, for example, Google’s Flood Hub. It uses AI to forecast river flooding in over 80 countries through weather patterns, historical data, and a terrain model. It can predict floods up to a week in advance, giving locals more than enough time to prepare or evacuate if need be.
Meanwhile, companies like Pano AI help fire-prone regions in California and Australia. Through high-resolution cameras and computer vision systems that identify smoke plumes or suspicious heat signatures, people can be alerted immediately.
In Japan, earthquake early-warning apps like Yurekuru Call are used by over 5 million people. The app applies AI to seismic sensor data, sending mobile alerts seconds before a quake begins—potentially giving just enough time for people to reach shelter.
AI in Pandemic and Public Health Response
Even before and during the COVID-19 pandemic, before generative AI’s boom in 2023, AI’s pattern recognition skills were already proving crucial.
Canadian firm BlueDot’s AI actually detected an unusual pneumonia cluster in Wuhan in late 2019—days before official global alerts about the coronavirus were announced. By piecing together information from online news reports, flight data, and local health bulletins in multiple languages, it was able to detect the pandemic before people even realized it.
Later on, public health agencies such as those in South Korea and Singapore integrated AI to track and control the infection. Many countries also used it to manage the logistics of vaccine distribution. At the scale of a global pandemic, manual input and monitoring would be far from sufficient in handling the crisis.
The Trade-off of Privacy and Bias
This use case, however, has shed light and raised much concern about the tradeoff of AI’s power: people’s privacy. Where is the line between necessary surveillance and an intrusion of civil liberties?
These health departments looked into people’s phone activities, financial transactions, and geographic behavior. Beyond this, AI in general needs human data to learn, evolve, and function. AI’s power might be great, but it is undeniable that people are deeply uncomfortable with giving away such personal data to tech companies. As such, many use a VPN to encrypt their connection and hide their online activities.
Another concern is that of bias. If AI models are trained on incomplete or skewed data—say, data sets that underrepresent certain communities—they may fail to be effective for such communities. For instance, wildfire detection systems trained only in one region might underperform in different terrains or climates. This can only exacerbate existing social inequalities.
Digital Crises: AI vs. Cyber Threats
Natural disasters, at least, are visible and tangible. What about cyberincidents? More insidious and arguably quicker, cyberattacks can completely paralyze both private and public sectors. Especially because such organizations face hundreds of ransomware and phishing attacks a day.
For example, 32 government agencies in the Philippines had staff passwords leaked onto the dark web. In 2013, Yahoo! suffered a data breach that compromised all of its 3 billion users’ data.
Without AI in these situations, organizations had an incredibly difficult time recovering from such attacks, much less predicting them in the first place. Take a look at the 2020 SolarWinds breach, for instance, which compromised government and corporate networks. Human analysts took months to uncover its scope.
Now, with AI, studies show that AI use can reduce the lifecycle of a breach by up to 74 days.
Examples of firms using AI-driven tools for cybersecurity include Darktrace and CrowdStrike whose systems model network behavior and detect deviations in real time.
Should a server suddenly experience unusual data transfers, AI can step in immediately and autonomously quarantine the node. As a concrete example, it can help a bank catch an attempted ransomware attack in progress before it can do any damage.
Perhaps the best part is that these tools do not rely on known signatures—they adapt and learn, which is crucial to staying relevant against evolving threats.
AI for Simulation, Preparation, and Everyday Use
AI’s power can be used not only for detection and prediction. Many organizations are now using it to prepare for disasters, making them even more prepared should they actually strike.
For example, businesses are now adopting digital twin models. These are AI-powered simulations of physical assets like factories, supply chains, or utility grids. Teams use them to stress test their logistics against natural disasters, power outages, and even cyberattacks—all without real-world risk.
However, it’s not only large organizations that can benefit from AI’s disaster management potential. Consumer-grade navigation apps like Waze, for example, now incorporate real-time hazard updates informed by AI. Smart home systems can give out weather alerts or detect smoke and other unusual activity when residents are away. Even wearable health trackers can now detect falls and have emergency SOS features powered by AI.
Conclusion
AI won’t replace crisis response teams—but it will empower them, even redefining how they operate. Algorithm-run dashboards alongside traditional radio feeds and mapping systems allow responders a more holistic view of crises.
As such, whether we should use AI to manage crises is no longer a question. Despite concerns, AI’s predictive ability is too powerful to deny. Doing so will only cost lives and billions of dollars in damage.
After all, disasters are not a matter of if, but a matter of when. And when it comes, AI may be our best shot at seeing it early, responding faster, and recovering smarter.
The question then becomes: how do we build AI systems we can trust—especially when the stakes are highest?
