Artificial Intelligence, Machine Learning & Cybersecurity

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Our Rational Perspective on AI and Machine Learning in Cybersecurity

The hype over artificial intelligence and machine learning might lead you to believe these disciplines represent a bold new frontier in cybersecurity capable of “autonomously” solving all of your organization’s threat detection challenges. In fact, the discipline of artificial intelligence dates back to 1956. Machine learning, AI’s spry younger sister, was born three years later.

What is Artificial Intelligence? How is Machine Learning Different?

What is Artificial Intelligence? How is Machine Learning Different?

AI is a branch of computer science that aims to endow computers with some degree of human intelligence, such as the capacity to learn, identify patterns or make predictions. Machine learning is a subset of AI that uses algorithms to give computers the ability to learn from vast sets of data without specifically programming them to do so. The algorithms that power machine learning applications find patterns and relationships in data sets, then make predictions or recommendations based on those identified patterns. These learning algorithms help the software get “smarter” over time.

AI at RSA

AI at RSA

Machine learning has a strong history at RSA. RSA co-founder Ronald Rivest made many seminal research contributions to the field, and RSA has been using machine learning in many of its products for more than a decade—far longer than the startups now claiming leadership in this space and touting the technology as a cybersecurity silver bullet.

RSA: Making Machine Learning Work

RSA: Making Machine Learning Work

Because we’ve been working with machine learning in production environments for such a long time, we’re intimately familiar with its strengths and limitations and what it takes to apply it successfully to cybersecurity use cases. We’re keenly aware of the fact that machine learning algorithms weren’t originally designed for cybersecurity, yet we’ve pressed on and found innovative ways to make it work for our customers. Done right and done responsibly, machine learning can deliver tremendous value for security and fraud teams.

Making Sense of Tomorrow’s Cybersecurity Trends: AI

RSA CTO Dr. Zulfikar Ramzan and Secureworks CTO Jon Ramsey discuss the benefits and risks of using artificial intelligence to automate certain cybersecurity functions, such as threat detection and response, at RSA Conference 2018.

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RSA NetWitness<sup><small>®</small></sup> UEBA

RSA NetWitness® UEBA

Harness the power of machine learning to detect insider threats and APTs before you lose data.

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RSA<sup><small>®</sup></small> Adaptive Authentication

RSA® Adaptive Authentication

Leverage machine learning and risk-based authentication to prevent fraud in digital channels without losing customers.

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RSA SecurID<sup><small>®</small></sup> Access

RSA SecurID® Access

Use machine learning to detect and prevent suspicious access while making it easier for legitimate users to log in.

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RSA Adaptive Authentication for eCommerce

RSA Adaptive Authentication for eCommerce

Provide a seamless, 3D secure payments experience with machine learning.

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Featured Resources

Closing the Skills Gap with Analytics and Machine Learning

White Paper

Closing the Skills Gap with Analytics and Machine Learning

This white paper from the SANS Institute explains how analytics and machine learning can automate many cumbersome threat detection and response activities.

Download the white paper

Demystifying the Black Box of Machine Learning

Demystifying the Black Box of Machine Learning

Get an overview of the advantages and disadvantages of two different machine learning algorithms being used to detect online fraud.

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6 Keys to Successful Identity Assurance – Machine Learning

6 Keys to Successful Identity Assurance – Machine Learning

Discover the critical role machine learning plays in identity assurance, confirming that users are who they say they are at the point of access.

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New Account Fraud: How to Apply Fraud Data to Reduce Risk

New Account Fraud: How to Apply Fraud Data to Reduce Risk

Findings from a study conducted by data scientists at RSA reveal surprising fraud patterns identified through machine learning.

See the findings

“With the fraud detection system [from RSA] and the policy rules we’ve written, we’re able to detect fraudulent activity, and in some cases, we’re able to deter attackers altogether.”
Edward Lam
Head of Regulatory Compliance, Security Management and Self-Service Development

Hong Leong Bank

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