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Gigamon Application Metadata Intelligence Introduced

Gigamon introduced Gigamon Application Metadata Intelligence (AMI), which delivers unprecedented application visibility to an organization’s tools ecosystem.

Gigamon AMI provides over 5,000 applications-related attributes extracted from network packet data, allowing NetOps, SecOps and analytics tools to quickly identify and troubleshoot performance and security trouble spots.

Gigamon AMI includes pre-built connectors with leading analytics tools such as Splunk Enterprise and IBM QRadar, enabling IT to easily leverage the power of metadata. Included are pre-configured templates that simplify integration with common use cases such as video quality monitoring, user/session tracking and International Mobile Equipment Identity (IMEI) devices. Furthermore, out-of-the box integration is available with a growing ecosystem of third-party tools, including FireEye, Plixer, Viavi, Flowmon and WitFoo.

Gigamon AMI centralizes network visibility and extracts the context around applications and protocols. Use cases of Gigamon AMI include:

- Network Performance: Troubleshooting server and file access performance issues using error resource codes

- Application Performance: Monitoring roundtrip SQL query time to and from a Mongo DB instance

- Operational Technology (OT) Communications: Isolating traffic and extracting intelligence for OT-related communications to help tools better focus on machine-to-machine communications for specialized use cases, such as healthcare (HL7), finance (OpenRTB) and Industrial Control Systems (SCADA)

- Security and Threat Detection: More precise identification of Command and Control attacks, identification of weak or old cyphers used for encryption, out of date certificates

“An exceptional application user experience, paired with strong security, is critical for the success of any digital transformation initiative. With Application Metadata Intelligence, organizations now have the contextual data needed to quickly pinpoint potential threats and resolve network or application performance issues that can impact the user experience,” said Ananda Rajagopal, VP of Products, Gigamon. “The unparalleled depth of metadata elements, as well as the ease and convenience of integrations with leading analytics providers, enables our customers worldwide to run fast and stay secure in today’s complex, digital ecosystem.”

Solution providers can take advantage of the Gigamon Metadata Empowered Partner Program for rapid AMI integration, with support ranging from access to a development environment to development support and certification. Partners can then leverage the Gigamon Catalyst Program, which includes joint collateral, demo presence at labs and events, and direct access to Gigamon’s extensive field and channel organizations that cover over 3,300 large businesses and global government agencies.

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Gigamon Application Metadata Intelligence Introduced

Gigamon introduced Gigamon Application Metadata Intelligence (AMI), which delivers unprecedented application visibility to an organization’s tools ecosystem.

Gigamon AMI provides over 5,000 applications-related attributes extracted from network packet data, allowing NetOps, SecOps and analytics tools to quickly identify and troubleshoot performance and security trouble spots.

Gigamon AMI includes pre-built connectors with leading analytics tools such as Splunk Enterprise and IBM QRadar, enabling IT to easily leverage the power of metadata. Included are pre-configured templates that simplify integration with common use cases such as video quality monitoring, user/session tracking and International Mobile Equipment Identity (IMEI) devices. Furthermore, out-of-the box integration is available with a growing ecosystem of third-party tools, including FireEye, Plixer, Viavi, Flowmon and WitFoo.

Gigamon AMI centralizes network visibility and extracts the context around applications and protocols. Use cases of Gigamon AMI include:

- Network Performance: Troubleshooting server and file access performance issues using error resource codes

- Application Performance: Monitoring roundtrip SQL query time to and from a Mongo DB instance

- Operational Technology (OT) Communications: Isolating traffic and extracting intelligence for OT-related communications to help tools better focus on machine-to-machine communications for specialized use cases, such as healthcare (HL7), finance (OpenRTB) and Industrial Control Systems (SCADA)

- Security and Threat Detection: More precise identification of Command and Control attacks, identification of weak or old cyphers used for encryption, out of date certificates

“An exceptional application user experience, paired with strong security, is critical for the success of any digital transformation initiative. With Application Metadata Intelligence, organizations now have the contextual data needed to quickly pinpoint potential threats and resolve network or application performance issues that can impact the user experience,” said Ananda Rajagopal, VP of Products, Gigamon. “The unparalleled depth of metadata elements, as well as the ease and convenience of integrations with leading analytics providers, enables our customers worldwide to run fast and stay secure in today’s complex, digital ecosystem.”

Solution providers can take advantage of the Gigamon Metadata Empowered Partner Program for rapid AMI integration, with support ranging from access to a development environment to development support and certification. Partners can then leverage the Gigamon Catalyst Program, which includes joint collateral, demo presence at labs and events, and direct access to Gigamon’s extensive field and channel organizations that cover over 3,300 large businesses and global government agencies.

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...