At FDC Summit 2026, Gigamon tackles the blind spots hiding inside AI-driven hybrid clouds
As enterprises accelerate their adoption of artificial intelligence, the biggest cybersecurity problem may not be the number of security tools they have.
It may be what those tools still cannot see.
That is the challenge Gigamon is bringing into focus at FDC Summit 2026, where the company will participate as a Technology Partner with a strategy built around what it calls Deep Observability — giving security and IT teams a clearer view of traffic moving across increasingly complex hybrid and multi-cloud environments.
For Gigamon, the timing is important.
Its latest 2026 research found that 83% of reported security breaches involved AI, while 65% of surveyed organizations suffered a breach during the previous year despite continued investment in security tools and governance. The company argues that the problem is increasingly linked to fragmented visibility as AI generates new traffic patterns, machine-to-machine activity and encrypted communications that traditional monitoring tools can struggle to inspect.
That makes Gigamon’s story at FDC less about another layer of cybersecurity software and more about a more fundamental question: can an organization defend infrastructure it cannot fully see?
The hybrid cloud is becoming harder to watch
Modern enterprise infrastructure rarely sits in one place.
Applications can run inside private data centers, public clouds, SaaS platforms and edge environments at the same time, while users, APIs, automated processes and AI systems constantly exchange data between them.
That architecture offers flexibility, but it also creates blind spots.
Security tools often rely on logs, endpoint agents and cloud-native telemetry, but some activity can remain difficult to identify, particularly traffic moving laterally between systems or encrypted communications that do not pass through traditional inspection points.
Gigamon has built its business around that gap.
Its Deep Observability Pipeline takes network-derived telemetry from traffic moving across physical, virtual and cloud environments and delivers that intelligence to security, cloud and observability platforms already used by enterprises.
The idea is not to replace existing tools.
It is to make them see more.
AI is creating traffic humans did not design
The rise of generative and agentic AI is making that visibility problem more complicated.
Traditional enterprise applications generally generate predictable patterns of communication.
AI agents can behave differently.
They may communicate with models, databases, APIs, business applications and other agents, sometimes taking actions automatically and generating large volumes of machine-driven traffic.
Gigamon says this shift is contributing to a surge in East-West traffic, encrypted data and machine-generated activity across hybrid infrastructure, creating areas where traditional tools may have limited visibility.
That becomes a security issue because an attacker does not necessarily need to break through a conventional perimeter if malicious activity can hide inside trusted internal traffic.
Understanding what AI systems are communicating with — and where data is moving — could therefore become as important as protecting the models themselves.
Gigamon is adding AI to observability
Gigamon is also using artificial intelligence inside its own platform.
During 2026, the company expanded its push into AI-powered Deep Observability, including Gigamon AI Traffic Intelligence, designed to help identify and understand traffic across hybrid environments, alongside Gigamon Insights, which applies additional intelligence to network-derived data.
The objective is to move beyond collecting packets and flows toward identifying meaningful patterns inside them.
As infrastructure becomes more complex, security teams need to distinguish normal application behavior from unusual activity without manually examining enormous volumes of network data.
AI can help automate that process.
But this creates an interesting contrast.
The same technology increasing the complexity of the network is also being used to understand it.
83% of breaches now involve AI
Gigamon’s latest global Hybrid Cloud Security Survey gives the company a strong argument for why that matters.
According to the study, AI was involved in 83% of reported breaches, while breach rates continued to increase despite higher cybersecurity spending.
The findings suggest organizations may be more confident about their ability to secure AI than their actual visibility supports.
That gap becomes even more striking in financial services.
Gigamon found that 77% of financial institutions surveyed had experienced a breach involving AI, while 98% of breached organizations reported a material business impact, including financial losses, data loss, higher cyber insurance costs or regulatory consequences.
At the same time, 66% of financial services organizations said AI was already initiating some security functions without human intervention.
That illustrates how quickly automation is moving into security operations.
It also raises another question: if an automated system makes a security decision, can the organization see enough of the underlying activity to know whether that decision was correct?
Gigamon and Google Security Operations
One of Gigamon’s notable moves this year has been its integration with Google Security Operations.
The integration sends network-derived telemetry from the Gigamon Deep Observability Pipeline into Google Security Operations, giving security teams additional visibility into hybrid cloud traffic that can then be analyzed alongside other security data.
This becomes particularly useful in environments where security teams already have large numbers of alerts but lack enough context to understand what happened before or after an event.
An alert from an endpoint may show that something suspicious occurred.
Network telemetry can help reveal where that device communicated, what other systems were involved and whether lateral movement followed.
That difference between an isolated alert and a broader sequence of activity can determine how quickly an attack is understood.
Zero Trust still needs visibility
Gigamon has also extended its Deep Observability strategy into Zero Trust environments.
In June, the company announced an integration with Zscaler Private Access, combining Zscaler’s identity-aware access controls with Gigamon Application Metadata Intelligence.
Zero Trust architectures are designed around the principle that users and devices should not automatically be trusted simply because they are inside a corporate network.
But controlling access does not eliminate the need to observe what happens after access is granted.
The Gigamon-Zscaler integration is designed to give security teams greater visibility into user-to-application activity, helping detect lateral movement, validate policies and investigate suspicious behavior.
That is particularly relevant as enterprises replace traditional VPNs with Zero Trust Network Access.
The architecture may change, but the need to understand traffic remains.
Splunk partnership targets AI-ready data
Gigamon’s 2026 strategy also includes a new partnership with Splunk aimed at providing unified access to distributed data for AI and security workloads.
The underlying problem is familiar to many large organizations.
Data exists everywhere, but moving all of it into one platform can be expensive and inefficient.
Gigamon’s approach is to extract high-value intelligence from network traffic and send the relevant telemetry to analytics and security platforms rather than treating every byte equally.
That could become increasingly important as AI systems consume more enterprise data.
The bigger AI becomes, the more organizations will need to understand not only what data exists but how that data moves.
A different cybersecurity story at FDC Summit
Gigamon therefore brings a notably different angle to FDC Summit 2026.
Google Cloud Security is pushing toward agentic defense, while Kaspersky is using threat intelligence to help organizations understand adversaries.
Gigamon sits underneath both problems.
Its focus is visibility.
Before a security platform can analyze a threat, before AI can investigate it and before an analyst can respond, someone has to see the activity in the first place.
For governments, banks, telecom operators and large enterprises running hybrid infrastructure, that may become increasingly difficult as cloud adoption and AI expand at the same time.
Gigamon’s latest strategy suggests that security teams will need to return to one of cybersecurity’s oldest principles — knowing what is happening on the network — but apply it to an environment dominated by encrypted traffic, cloud workloads and autonomous machines.
At FDC Summit 2026, that could make Deep Observability much more than an infrastructure term.
It could become one of the foundations of securing the AI era.