The AI Security Paradox: Why Deeper Visibility Is Now Essential

SANTA CLARA, CA – December 09, 2025 – The corporate world's rapid embrace of generative AI and large language models (LLMs) is creating a profound paradox. While these technologies unlock unprecedented productivity and innovation, they are simultaneously opening a Pandora's box of complex, often invisible, security risks. As organizations rush to integrate AI into their hybrid cloud infrastructure, a critical new market is surging to address the fallout: deep observability. New research confirms this sector is not just growing, but becoming a foundational pillar of modern cybersecurity.

A recently published report from market intelligence firm 650 Group highlights this dramatic shift, revealing that the deep observability market expanded by 25 percent year-over-year in the first half of 2025. The report crowns Gigamon as the definitive market leader, commanding a 50 percent share of this rapidly evolving space. This dominance underscores a growing consensus among IT and security leaders: you cannot secure what you cannot see, and in the age of AI, there is more to see than ever before.

A New Frontier of Risk: The Shadow AI Problem

The security challenges posed by AI are not merely an evolution of existing threats; they represent a fundamental change in the attack surface. The most pervasive issue is the rise of “Shadow AI”—the unsanctioned use of public AI tools by employees. Research indicates a staggering 485 percent increase in corporate data being fed into generative AI tools over the past year, with inputs containing sensitive data tripling. This unauthorized data flow creates massive risks of intellectual property leakage, compliance violations, and inadvertent data breaches.

Beyond shadow AI, the very nature of AI workloads introduces new vulnerabilities. Malicious actors can use techniques like prompt injection to manipulate LLMs into revealing sensitive information, or data poisoning to corrupt training datasets and introduce hidden backdoors. Because these AI models often operate as opaque “black boxes” and communicate via encrypted traffic, traditional security tools that rely on logs, metrics, and event data are often blind to these activities. They can see that a connection was made, but have no insight into the content or context of the data in motion, particularly as it moves laterally—or East-West—across the cloud.

“As AI adoption accelerates, deep observability has become essential to maintaining security, performance, and compliance across hybrid cloud infrastructure,” notes Alan Weckel, co-founder and analyst at 650 Group. “LLMs introduce new levels of opacity and complexity, which makes trusted, network-derived telemetry critical for understanding system behavior and detecting threats that traditional tools might otherwise miss. This is why AI is now one of the strongest drivers of the deep observability market.”

A Market Forged in Complexity

The demand for solutions that can pierce this new veil of complexity is fueling explosive market growth. The 650 Group report forecasts the deep observability market will grow at a compound annual growth rate (CAGR) of 29 percent, projecting revenues to approach $1.7 billion by 2029. Tellingly, cloud-delivered SaaS offerings are expected to account for over 90 percent of that revenue, signaling a decisive shift away from on-premises hardware.

While Gigamon holds a commanding lead, the competitive landscape includes established networking and security vendors such as NETSCOUT, Arista Networks, and Keysight Technologies. Each is vying for a piece of this critical infrastructure layer. However, Gigamon's early focus and sustained innovation have solidified its position. The company’s strategy hinges on providing a foundational visibility fabric that enhances the entire security and observability ecosystem, rather than replacing it.

This market dynamic is validated by a 2025 Hybrid Cloud Security Survey, in which 89 percent of over 1,000 global IT and security leaders agreed that deep observability is now a foundational element of cloud security. It is no longer a niche tool but a strategic necessity for any organization serious about leveraging AI at scale.

Under the Hood: The Technology of Trust

At its core, deep observability is the ability to access and analyze raw network traffic—the ultimate source of truth for all digital interactions. The Gigamon Deep Observability Pipeline achieves this by intercepting all data in motion, whether it’s moving between servers in a data center, across multi-cloud environments, or within containerized applications.

Two key technological innovations are central to this capability. The first is agentless visibility into encrypted traffic. With over 90 percent of malware hiding in encrypted channels, this has long been a major blind spot for security teams. Gigamon's Precryption® technology provides plaintext visibility into traffic before it’s encrypted or after it’s decrypted at the workload level, without the performance drag and operational complexity of traditional decryption methods. The second is Application Metadata Intelligence (AMI), which extracts thousands of contextual attributes about the applications running on the network—including identifying traffic from over 30 specific GenAI and LLM engines like ChatGPT and Gemini—without inspecting the payload itself. This allows organizations to spot unauthorized AI usage and enforce governance policies.

The pipeline then intelligently filters, transforms, and enriches this network-derived telemetry before delivering it to an organization's existing portfolio of security and observability tools. This process not only reveals previously hidden threats but also dramatically improves the efficiency and reduces the data ingestion costs of SIEMs and other monitoring platforms by eliminating redundant and irrelevant traffic.

From Data Deluge to Strategic Advantage

For business leaders, the value of deep observability translates directly into reduced risk and improved operational efficiency. By providing a complete and trusted view of their hybrid cloud infrastructure, organizations can move from a reactive security posture to a proactive one.

Cybastion, a global cybersecurity firm, exemplifies this strategic shift. “Deep observability is becoming strategically critical to our security strategy as we expand our AI efforts,” stated Kevin Cardwell, the company's CIO and CTO. “Gigamon has been a trusted partner in elevating our cybersecurity posture, and we’re confident that the Gigamon Deep Observability Pipeline will continue to give us the visibility, control, and resilience we need to stay ahead of threats.”

This sentiment reflects a broader industry understanding that managing AI requires a new level of control. The visibility provided by deep observability enables organizations to build the robust governance frameworks needed to manage AI-related costs, ensure compliance, and mitigate the inherent risks. It serves as the foundational data source for Zero Trust security architectures, which operate on the principle of “never trust, always verify.”

“As AI-driven workloads scale across hybrid cloud infrastructure, the need for trusted, network-derived telemetry has never been greater,” said Shane Buckley, president and CEO of Gigamon. “Deep observability gives our customers the clarity required to detect threats, maintain performance, and uphold compliance in environments that are growing more dynamic every day. As the pioneer of deep observability, Gigamon remains focused on giving organizations the visibility and resilience they need to securely embrace AI at scale.”