See more cyber threats. Faster.

Advanced network detection and response

Cyber threats are now ever-present and increasingly
unpredictable. Traditional approaches, such as signature-based
detection, can’t keep up.

Find the truth within
the traffic

Sophisticated cyber threats need
advanced detection.

Next-generation network traffic analysis leverages machine
learning so you can see unknown threats around the corner,
accelerating triage and response. A win-win for your SOC
and cyber strategy.

Patrick Collard
IronNet Director of Data Science
What are unknown unknown threats?
  • Malware leveraging new zero-day vulnerabilities
  • New Advanced Persistent Threats (APT) groups
  • Attacks targeting legacy equipment with limited or no cyber defenses

 

How can AI enable detection of unknown unknown threats?
  • Training predictive models to identify and classify all anomalies in a network
  • Applying human intelligence and intuition at scale to network anomalies
  • Increasing the amount of data from industry, supply-chain, or geographic-level analysis to build better AI models and achieve greater visibility into incoming threats across similar companies and/or sectors
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Increasing visibility into the threat landscape

An energy company faced a DNS tunneling attack (attributed to a third-party vendor with access to the corporate domain) that went undetected by the customer’s other cybersecurity tools. Using the DNS tunneling analytic in IronDefense and IronNet’s hunt services, we detected and reported to the company SOC within three hours of the activity occurring.

The result?

The asset had at least three types of malware on its host — and the customer was able to reduce dwell time and mitigate potential compromise of the corporate network. IP and sensitive information were protected, and IronNet improved the SOC’s effectiveness.

95% detection rate of
unknown threats

vs. the 17% detection rate of the
company’s existing security
tools.