AI Business recently caught up with Nicole Eagan, CEO of Darktrace. Nicole has led the company since 2014 and rapidly grown it into a global leader in cyber defense.
Under Nicole’s leadership, Darktrace’s machine learning and mathematics-based approach to cyber defense has won over 20 awards, including World Economic Forum Technology Pioneer. Darktrace was also named ‘Best Security Company of the Year’ 2016.
Darktrace’s Enterprise Immune System technology is capable of identifying new, emerging threats within computer networks in real-time.
Nicole will be speaking at The AI Summit in San Francisco, so we were very keen to hear the latest at Darktrace ahead of the event on 28-29 September.
Nicole Eagan of Darktrace
Nicole points out that “defending against cyberattacks is a number one priority for businesses today”:
“The information security strategies of the past, which rely on knowledge of past attacks, are no match for the constantly-evolving threats of the future. Indeed, AI-powered attacks are a major concern—threats that disguise themselves, mimicking user behaviour and blending into the everyday noise of the network.”
So how is Darktrace tackling this? Nicole explains:
“To fight against AI attacks, businesses need to equip themselves with self-learning technologies that learn ‘normal’ behaviour on the network automatically. Darktrace takes for granted that perimeter defenses will inevitably be penetrated and instead uses unsupervised machine learning to establish what is normal for a network and find anomalous activity, notifying users of potential threats in real-time.”
“Darktrace’s Enterprise Immune System can keep pace with the AI-driven attacks of the future, using unsupervised machine learning to detect and protect against even the best-disguised threats before they spread throughout a network.”
Darktrace is not focusing on any particular industry vertical for their solution – instead, Nicole says they are working across the enterprise landscape:
“Organisations across all industry verticals rely on Darktrace for cyber security. The increasing global demand for a machine learning-driven approach to cyber defense indicates a growing awareness of the importance of defending against today’s fast-moving, intelligent threats.”
In the future, Darktrace will be looking to the consumer market as well:
“Moving forward, Darktrace is looking toward new applications of our self-learning technology that would allow consumers to defend things like home routers, IoT devices, and even smart homes against attack.”
But with cyberattacks a constantly evolving threat, Nicole admits that cyber security is a “very difficult problem to solve”:
“Attacks have become faster, subtler, and more deadly. Threats quietly cross perimeter defenses and hide on a network for months before striking. In a matter of milliseconds, these new sorts of threats can devastate an organisation.”
Nicole knows that “Human analysts cannot keep up with the speed and intelligence of these machine attackers,” and insists that “defending against the new wave of threats requires fighting machine with machine”:
“Real machine learning is hard to deliver. A lot of the new technologies we’re seeing are no more than glorified rules and signature systems. But by using the most advanced probabilistic mathematics, Darktrace is proving that genuine self-learning, self-defending systems are possible.”
Nicole sees self-defending systems as the future of cyber security:
“Darktrace is extending unsupervised machine learning into the realm of self-defense, allowing networks to defend themselves by automatically selecting and executing the most effective action to take at the first sign of threat. This ensures a response time faster than any human analyst could achieve, allowing organisations to confront increasingly automated attacks without disrupting business. Antigena, Darktrace’s self-defense technology, puts companies at an advantage in the cyber arms race.”
Nicole will be sharing her cyber security expertise in her keynote at The AI Summit in San Francisco on 28-29 September .
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