Preparing for Viral Attacks in the Digital and Physical Worlds Requires Centralized Intelligence

Posted on by Yinglian Xie

Viral outbreaks are not new—humans have battled them for centuries. We’ve successfully developed defenses against everything from smallpox to SARS to Ebola. Yet, even though our ability to identify and battle human viruses has improved, new strains continue to emerge, adapt and evolve faster than we can keep up.

Rapidly spreading viruses are also an ongoing problem in the digital world, and for a similar reason—our inability to identify and prevent unknown threats. Just as it’s impossible to use yesterday’s science to prevent a pandemic outbreak caused by a novel virus strain, it’s impossible to prevent digital threats with yesterday’s technology.

New World, New Approach

The world as we once knew it has forever changed as the result of the COVID-19 pandemic. People are making dramatic modifications to their behaviors in an effort to flatten the curve in their communities. In this new reality, going digital is no longer optional; it’s an imperative. Employees must work remotely, students must learn online and live events are now virtual. These are necessary and beneficial steps, and they introduce a new paradigm for how we operate as a society.

However, rapid digitalization plays right into the hands of modern fraudsters who have the latest technologies at their disposal, and are working overtime to try to exploit any new vulnerability for illicit gain. As such, technologists are stepping up efforts to implement artificial intelligence and machine learning techniques that can help spot unknown threats and prevent widespread damage.

The good news is, what we know about detecting and preventing viruses and threats in the digital world can inform our approach to detecting and preventing them in the physical world. Here are some of the most important lessons we’ve learned from high-profile digital fraud attacks:

  • Reactivity is no longer a viable approach. We cannot simply wait for something to happen and then try to contain the damage. Today’s threats are too large, too fast and too sophisticated. By the time an attack occurs, it is often already too late. We need to embrace more proactive strategies.

  • Everything today is interconnected. While there may have been a time when viral attacks were isolated events, that time is long gone. This is especially important to understand as we rapidly move our lives online. The result of this migration is that the surface area for a digital attack is vastly wider than before. An attack can come from anywhere and quickly spread throughout the digital ecosystem.

  • Knowing the unknown is critical for safety and security. Through the use of AI and machine learning—particularly unsupervised machine learning, which does not depend on legacy data, labels or rules to surface correlated patterns and connections from raw data—we can begin to spot new and emerging threats that were previously unknown and unseen. We can do this in real time, and at scale.

  • Centralized intelligence is the difference between success and failure. When data is acquired but not shared, silos result, and one entity can know more than another. Breaking down silos and centralizing intelligence makes critical knowledge available and readily actionable throughout the system, which helps make containment more successful.

The Intelligence to Prepare for the Unknown

While we can’t always anticipate new threats—whether from a virus like COVID-19 or a sophisticated fraudster infiltrating our digital networks—we can leverage new technologies to take a proactive stance. AI and machine learning techniques are enabling us to harness the power of big data to understand, analyze and predict emerging threats with high levels of accuracy. By centralizing access to this intelligence, we can work together to prepare for the unknown—and take swift, proactive action when a new threat emerges.

Yinglian Xie

CEO and Co-Founder, DataVisor

Hackers & Threats

hackers & threats artificial intelligence & machine learning artificial intelligence & machine learning

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