Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?

Artificial Intelligence, also known as AI, is an umbrella term covering all methods and disciplines that result in any form of intelligence exhibited by machines. This includes the 1980s expert systems (which were basically datasets of hard-coded knowledge) all the way up to most advanced forms of AI now in use. Nearly all software that’s currently being used in just about all industries employs at least some form of AI, even if it’s limited to some basic manually coded procedures.

The term AI was coined by the pioneering computer scientist John McCarthy in the 1950s. Earlier incarnations of AI relied on routines that programmers specified manually, along with heuristics, which are essentially shortcuts for facilitating fast but accurate decision-making.

Deep Learning is the most advanced form of AI. While basic machine learning (ML)-based solutions either protect too much—slowing down the business and flooding your team with false positives—or lack the precision, speed, and scalability to predict and prevent unknown malware and zero-day threats before they have infiltrated your network, Deep Instinct used deep learning to detect and prevent threats with greater speed and accuracy than any competitor solution in the world.

Deep Instinct’s pioneering use of deep learning prevents >99% of unknown threats and is incredibly precise, ensuring false positives remain <0.1%. Deep learning is not dependent on human-led feature engineering, nor does it require frequent updating to maintain an extremely high level of prevention efficacy.

Deep Instinct’s vast neural network has been trained for more than five years on hundreds of millions of files to autonomously prevent threats, allowing your highly skilled SOC team to spend less time responding to and managing false positives and more time focusing on higher priorities like threat hunting, patching, and hardening security defenses. This represents a significant increase in productivity, as typical SOC teams average 25% of their time investigating false positives based on our independent research.

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