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Gigamon Integrates with Amazon Security Lake

Gigamon announced that its Deep Observability Pipeline now delivers network-derived application metadata intelligence (AMI) into Amazon Security Lake from Amazon Web Services (AWS).

Amazon Security Lake automatically centralizes an organization’s security data from across their AWS environments, leading SaaS providers, on-premises environments, and cloud sources into a purpose-built data lake, so customers can act on security data faster and simplify security data management across hybrid and multicloud environments. This integration provides organizations the ability to access and analyze data-in-motion across hybrid cloud infrastructure to more efficiently and effectively secure and manage workloads, applications, and data.

The integration of network-derived intelligence with Amazon Security Lake supports important use cases for organizations seeking both completeness and efficiency across their security tools stack. With Amazon Security Lake, Gigamon can provide:

- Security analytics based on actual data communications to completely and correctly identify any usage of vulnerable protocols, deprecated ciphers, and expired certificates

- Forensics that compare what applications actually did with what logs report

- A richer and deeper data set on which to base new AI-driven security analytics via tools like NDR or XDR

Gigamon leverages deep packet inspection (DPI) to extract more than 7,500 application-related metadata attributes derived from network packets. With Amazon Security Lake integration, users can centralize and gain deep observability into security data across their entire organization. The new integration helps organizations to:

- Efficiently deliver AWS traffic to multiple security tools without installing individual agents for each tool

- Contain excessive tool and transit costs by filtering unnecessary traffic and deduplicating redundant traffic

- Generate NetFlow for SIEMs and raw packets for NPMs and packet sniffer tools

Gigamon is also a launch partner for additional AWS services including AWS Gateway Load Balancer as an endpoint, expansion of VPC Traffic Mirroring to new Amazon Elastic Compute Cloud (Amazon EC2) instances, and others. In addition to integration with Amazon Security Lake, Gigamon GigaVUE® Cloud Suite™ for AWS is now fully integrated with AWS Network Load Balancer (NLB) and native AWS Virtual Private Cloud (VPC) Traffic Mirroring.

“The powerful combination of our GigaVUE Cloud Suite for AWS and Amazon Security Lake provides our mutual customers with the same level of deep observability and protection they’ve come to expect across their on-premises data center infrastructures, extending it to their entire AWS environment,” said Srinivas Chakravarty, VP, cloud ecosystem at Gigamon. “IT and security leaders are grappling with complex multi-tiered tool stacks today amid constrained budgets and resources, and with this new integration, organizations will now be armed with the necessary tools to maximize their visibility effectiveness and accuracy across their entire hybrid and multi-cloud infrastructure.”

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Gigamon Integrates with Amazon Security Lake

Gigamon announced that its Deep Observability Pipeline now delivers network-derived application metadata intelligence (AMI) into Amazon Security Lake from Amazon Web Services (AWS).

Amazon Security Lake automatically centralizes an organization’s security data from across their AWS environments, leading SaaS providers, on-premises environments, and cloud sources into a purpose-built data lake, so customers can act on security data faster and simplify security data management across hybrid and multicloud environments. This integration provides organizations the ability to access and analyze data-in-motion across hybrid cloud infrastructure to more efficiently and effectively secure and manage workloads, applications, and data.

The integration of network-derived intelligence with Amazon Security Lake supports important use cases for organizations seeking both completeness and efficiency across their security tools stack. With Amazon Security Lake, Gigamon can provide:

- Security analytics based on actual data communications to completely and correctly identify any usage of vulnerable protocols, deprecated ciphers, and expired certificates

- Forensics that compare what applications actually did with what logs report

- A richer and deeper data set on which to base new AI-driven security analytics via tools like NDR or XDR

Gigamon leverages deep packet inspection (DPI) to extract more than 7,500 application-related metadata attributes derived from network packets. With Amazon Security Lake integration, users can centralize and gain deep observability into security data across their entire organization. The new integration helps organizations to:

- Efficiently deliver AWS traffic to multiple security tools without installing individual agents for each tool

- Contain excessive tool and transit costs by filtering unnecessary traffic and deduplicating redundant traffic

- Generate NetFlow for SIEMs and raw packets for NPMs and packet sniffer tools

Gigamon is also a launch partner for additional AWS services including AWS Gateway Load Balancer as an endpoint, expansion of VPC Traffic Mirroring to new Amazon Elastic Compute Cloud (Amazon EC2) instances, and others. In addition to integration with Amazon Security Lake, Gigamon GigaVUE® Cloud Suite™ for AWS is now fully integrated with AWS Network Load Balancer (NLB) and native AWS Virtual Private Cloud (VPC) Traffic Mirroring.

“The powerful combination of our GigaVUE Cloud Suite for AWS and Amazon Security Lake provides our mutual customers with the same level of deep observability and protection they’ve come to expect across their on-premises data center infrastructures, extending it to their entire AWS environment,” said Srinivas Chakravarty, VP, cloud ecosystem at Gigamon. “IT and security leaders are grappling with complex multi-tiered tool stacks today amid constrained budgets and resources, and with this new integration, organizations will now be armed with the necessary tools to maximize their visibility effectiveness and accuracy across their entire hybrid and multi-cloud infrastructure.”

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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