Application Metadata Is the Oil That Fuels Modern AI

Two decades ago, British mathematician Clive Humbly coined the term “data is the new oil.” His forward-looking statement has certainly come to fruition. With the rise of modern AI, including generative and agentic AI applications and workloads, data plays an invaluable role in the training and inference functions tied to large and smaller algorithmic models. More specifically, in the context of AI-powered cybersecurity and operations, it is network-derived application metadata that matters. If you are not leveraging it, you are potentially flying blind in a digital infrastructure storm. Network and data observability tools are unlocking newfound capabilities for organizations, but the augmentation of intelligence derived from telemetry flows is critical.

Last year, I highlighted Gigamon as a company that understands the value of intelligent, deep observability as it relates to AI operations. Flash forward, and the company continues to evolve its set of solutions that unlock new business value. In this piece, I will highlight why I believe that application metadata, derived directly from network traffic, is the real currency of digital resilience. For those who choose to embrace it, it can refine network assurance, bolster security posture, and drive higher cyber defensive efficacy.

Network Traffic as an Immutable Source of Truth

Network traffic can serve as a highly trusted source of truth. When one analyzes what moves across the wire, especially encrypted and East-West (lateral) traffic within a networked environment, insights into IT and OT systems and users can be captured. There are two primary ways to accomplish this objective. Full-packet capture solutions are powerful, but their resource-intensive demands and scalability challenges often make them impractical for real-time use.

Alternatively, metadata extraction that focuses on the Open Systems Interconnect (OSI) model within Layers 4 through 7 — transport, session, presentation, and application — can be a far more efficient approach. This process allows organizations to glean the most valuable information from traffic without needing to store or process entire packet streams. Gigamon is a pioneer in this regard, as evidenced by its Application Metadata Intelligence (AMI) capabilities. Consequently, the company has created and refined a telemetry engine that fuels its deep observability pipeline functionality and provides actionable insights.

The Value of Application Metadata

Application metadata elements that include HTTP hostnames, TLS session data, DNS queries, database calls, and cloud service identifiers allow network engineers, security analysts, and compliance teams to understand how traffic moves and the behavior of applications hosted across hybrid cloud infrastructure. These are important and critical insights. For cybersecurity, it enables smarter anomaly detection and threat hunting by feeding SIEMs and extended detection and response platforms with high-fidelity signals that catch lateral movement, uncover data exfiltration, and root out unsanctioned shadow IT usage. As an illustration, the ability for a security operations team to differentiate between a sanctioned Zoom connection and a suspicious proxy tunnel masquerading as one is powerful. This represents an insight that cannot be achieved through a simple NetFlow analysis. The capability also facilitates faster and more efficient operational control and troubleshooting, given the granular insights provided by the underlying metadata.

Furthermore, metadata analysis can strengthen corporate governance and compliance, serving as an audit trail that documents which applications were accessed, in what manner, and by whom. This is a critical consideration, especially in highly regulated industries such as healthcare that conform to HIPAA data privacy standards in the U.S., and financial service companies that must also meet exacting standards, including PCI DSS for secure credit card transactions. The European Union has set its data compliance and regulatory bar high with GDPR and most recently its Digital Operational Resilience Act (DORA), which deepens scrutiny on the region’s financial sector. On this latter point, Gigamon is a front-runner in its support of DORA.

A Perfect Match — AI and Metadata

Modern AI applications and workloads bring to light the importance of application metadata. Historically, metadata was often an afterthought that served as an additional point of reference. Today, it is a valuable element, something that AI small and large language models (LLMs) can digest in real time to produce more accurate alerts, make faster predictions and enable remediation, and automate workflows at scale. In growing numbers, private data lakes are being built on telemetry pipelines, and AMI can play a pivotal role in providing a structured, enriched dataset. AMI has the power to train agentic and generative AI (GenAI) applications to identify risks, optimize services, and reduce mean time to resolution in the event of a fault.

Gigamon is quickly positioning itself as a leader in an emerging telemetry-infused AI ecosystem. In addition to generating metadata for AI consumption, it’s also generating metadata about AI consumption. The company’s AMI capabilities enable organizations to gain real-time visibility into their own GenAI and LLM activity to provide deeper levels of intelligence and inform AI governance policy. These insights are a potential operational force multiplier for security analysts, network engineers, and compliance teams.

Final Thoughts

Regardless of an organization’s size or its industry, the storyline is the same — logs are no longer enough. Network-derived application metadata is the connective tissue that can convert fragmented tools into integrated, intelligent systems. However, it is still early days within the modern AI era. Broader education is needed across network, cloud, and security teams regarding the value that metadata can play as a foundational component of AI-augmented infrastructure. Tool integration must also continue to improve, especially in terms of interoperability with cloud-native platforms and open telemetry frameworks.

Data is the new oil, but application metadata can serve as the premium grade fuel that supercharges modern AI applications and workloads.

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