Vectra detects previously unseen cyber attacks in public clouds using the Gigamon Visibility Platform on AWS
Vectra Networks, the leader in automating the hunt for in-progress cyber attacks, today announced a breakthrough in data center security that identifies hidden cyber attack behaviors within Amazon Web Services that were previously impossible to detect.
“Cyber attackers like to establish a beachhead in cloud workloads to inflict lasting damage,” said Vectra CTO Oliver Tavakoli. “Hijacking an open server port is the most effective way to install a backdoor in compromised workloads. Our ability to detect the presence of previously unseen backdoors by monitoring traffic within the cloud provides unique protection against this important attacker technique.”
In the next three years, 56 percent of all enterprise workloads and 83 percent of all data center traffic will be in the public cloud. Consistent cybersecurity controls that scale with enterprise demands for public cloud migration are urgently needed to expose attacker behaviors in both areas.
In AWS, Vectra uses artificial intelligence to automatically detect the tell-tale signs of a cyber attack – including command and control, lateral movement, internal reconnaissance, and data exfiltration behaviors – and enables security teams to respond quickly and decisively to stop the progression of any threat.
As a result, enterprise organizations can migrate enterprise applications, data and other shared computing resources to Amazon Web Services (AWS), while dramatically reducing the risk of compromise, damage and theft.
“This approach fills a big void in the public cloud and removes security as a roadblock to AWS migration,” said Beau Canada, vice president of information security at Ticketmaster. “Without native SPAN port support in AWS, options were limited for network visibility. Vectra integration with Gigamon delivers attacker behavior visibility for an integral and growing component of the enterprise within the public cloud. With a solution like this, companies will be able to move everything, including back-office applications, into the cloud.”
To meet the demands of public clouds, the Vectra AWS virtual sensor (vSensor) performance scales to the current 20 Gbps limit of AWS instance types. The Gigamon Visibility Platform on AWS enables organizations to use a standardized approach to gain access to network traffic in an AWS VPC. The Gigamon solution enables users to send traffic from Amazon EC2s to security tools such as Vectra on demand, a capability that was not previously available.
Vectra will demonstrate its cybersecurity AI solution leveraging the Gigamon Visibility Platform for Amazon Web Services (AWS) at the RSA Conference in San Francisco. The integration with Gigamon enables Vectra AI software to secure AWS workloads, eliminating security blind spots in the public cloud.
Visit Gigamon at Booth S1523 at the RSA Conference in San Francisco, Feb. 13-16, or schedule an onsite meeting with Vectra executives at RSA.
For more information, about Vectra and its artificial intelligence cybersecurity software, visit https://www.vectranetworks.com/product.
About Vectra Networks
Vectra Networks is the leader in automating the hunt for in-progress cyber attacks. Using artificial intelligence, Vectra correlates threats against hosts that are under attack and provides unique context about what attackers are doing so organizations can quickly prevent or mitigate loss. Vectra prioritizes attacks that pose the greatest business risk, enabling organizations to make rapid decisions on where to focus time and resources. In 2016, Vectra was named “Most Innovative Emerging Company” in the Dark Reading Best of Black Hat Awards. InformationWeek also named Vectra one of the Top 125 companies to watch in 2016. Vectra investors include Khosla Ventures, Accel Partners, IA Ventures, AME Cloud Ventures and DAG Ventures. The company is headquartered in San Jose, Calif. and has European regional headquarters in Zurich, Switzerland. More information can be found at www.vectranetworks.com.
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