Using Artificial Intelligence in Cybersecurity

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Using Artificial Intelligence in Cybersecurity

The enterprise attack surface is massive, and continuing to grow and evolve rapidly. Depending on the size of your enterprise, there are up to several hundred billion time-varying signals that need to be analyzed to accurately calculate risk.

The result?

Analyzing and improving cybersecurity posture is not a human-scale problem anymore.

In response to this unprecedented challenge, Artificial Intelligence (AI) based tools for cybersecurity have emerged to help information security teams reduce breach risk and improve their security posture efficiently and effectively.

AI and machine learning (ML) have become critical technologies in information security, as they are able to quickly analyze millions of events and identify many different types of threats – from malware exploiting zero-day vulnerabilities to identifying risky behavior that might lead to a phishing attack or download of malicious code. These technologies learn over time, drawing from the past to identify new types of attacks now. Histories of behavior build profiles on users, assets, and networks, allowing AI to detect and respond to deviations from established norms.

Artificial Intelligence vs. Data Analytics

Unfortunately, AI is a very popular, often misused buzzword at the moment. Not unlike big data, the cloud, IoT, and every other “next big thing”, an increasing number of companies are looking for ways to jump on the AI bandwagon. But many of today’s AI offerings don’t actually meet the AI test. While they use technologies that analyze data and let results drive certain outcomes, that’s not AI; pure AI is about reproducing cognitive abilities to automate tasks.

Here’s the crucial difference:

  • AI systems are iterative and dynamic.They get smarter with the more data they analyze, they “learn” from experience, and they become increasingly capable and autonomous as they go.
  • Data analytics (DA), on the other hand, is a static process that examines large data sets in order to draw conclusions about the information they contain with the aid of specialized systems and software. DA is neither iterative nor self-learning.

Understanding AI Basics

AI refers to technologies that can understand, learn, and act based on acquired and derived information. Today, AI works in three ways:

  • Assisted intelligence, widely available today, improves what people and organizations are already doing.
  • Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do.
  • Autonomous intelligence, being developed for the future, features machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.

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