Beyond Logs: Finding AI‑Era Blind Spots in East‑West, Encrypted, and Cloud-Native Traffic

AI is radically changing cyber risk, not just by creating more vulnerabilities, but also by shrinking the time between discovery and exploitation. Logs and SIEM alerts alone can’t fully show which attack paths are live, where lateral movement is happening, or how shadow AI is handling your data.

Deep observability adds network-derived telemetry from data in motion across East-West, encrypted, and cloud‑native traffic so security teams can validate exploitability, prioritize what matters, govern AI usage, and prove that controls worked.

How AI Is Changing Cybersecurity Risk

AI is transforming both sides of cybersecurity. Attackers now use automation and generative models to scan for weaknesses, craft realistic phishing, and test exploits at a speed that would have been unthinkable a few years ago. On the defender side, AI‑powered tools are surfacing more potential issues, earlier, and at larger scale.

That creates a new bottleneck. The hard part is no longer simply finding vulnerabilities, misconfigurations, or suspicious behavior. It’s validating:

Which findings are reachable on real traffic paths

Which attack paths exist right now, not just in theory

Which issues matter most to the business

In this AI‑driven threat environment, security teams need a stronger evidence base. They need to see how data moves across their infrastructure, not just how systems report on themselves.

That means going beyond logs.

Why Security Logs Alone Are No Longer Enough

Log-based security tools that rely on Metric, Event, Log, and Trace (MELT) data will always be foundational for security operations. They capture what systems, applications, and services say is happening, including status changes, policy decisions, authentication attempts, and more.

But MELT data also has built‑in limits:

It is generated and filtered by the systems you’re trying to protect

It can be delayed, incomplete, misconfigured, or even manipulated

It often hides crucial context about how entities interact on the network

If you rely only on logs during an incident or threat validation exercise, you are often reconstructing the story from partial clues. In an environment where AI accelerates attacker behavior and compresses timelines, those gaps create:

Noisy queues of alerts that are difficult to triage

Missed lateral movement hidden in East-West traffic

Slower investigations and lower confidence in response

To close these gaps, organizations need an independent source of evidence from data in motion that complements MELT data and provides visibility into how systems, applications, and users interact across hybrid cloud environments.

East‑West, Encrypted, and Cloud‑Native: Today’s Biggest Blind Spots

Most organizations have spent years hardening the “front door”: internet‑facing apps, perimeter controls, and North‑South monitoring. Yet the most consequential activity in modern attacks often plays out elsewhere where traditional logging and perimeter‑centric monitoring struggle.

East‑West Traffic

Once attackers get a foothold, they rarely march straight to the crown jewels. They move laterally, blend with normal internal traffic, and test new paths quietly. Without consistent visibility into East-West flows:

Lateral movement can look like routine service‑to‑service communication

Internal reconnaissance and staging may never appear in perimeter logs

Compromised identities can traverse segments in ways that evade current controls

Encrypted Traffic

Encryption is essential, but it also creates a hiding place:

Malicious activity can be wrapped in TLS and appear as “just another secure session”

Weak ciphers, certificate issues, or odd handshake patterns can go unnoticed

Command‑and‑control, tunneling, and exfiltration can masquerade as ordinary encrypted web or API calls

If you cannot see meaningful telemetry from encrypted flows, you are trusting that other layers will always catch misuse, an increasingly risky assumption as attackers adopt AI‑driven evasion.

Cloud‑Native Environments

Containers, microservices, and managed services have unlocked agility and scale, but they’ve also made data paths more dynamic:

Services spin up and down rapidly; IPs and endpoints constantly change

Identity and policy decisions are distributed across multiple control planes

Traffic may traverse multiple clouds, regions, and virtual networks in seconds

In this world, visibility gaps compound. It becomes hard to answer basic questions: Which services are talking to which? What data is moving between them? Which paths touch regulated or critical systems?

These are not new problems, but AI has raised the operational cost of living with them. When threats and alerts both accelerate, uncertainty itself becomes a material risk.

What Is Deep Observability in Cybersecurity?

Deep observability transforms network‑level traffic into high‑fidelity security evidence across hybrid and multi‑cloud environments.

At the core is the Gigamon Deep Observability Pipeline: a telemetry and intelligence layer that accesses, optimizes, enriches, and delivers network-derived telemetry to the tools security and operations teams already use. The deep observability pipeline:

Continuously acquires traffic across physical, virtual, container, and cloud environments

Optimizes that traffic via deduplication, slicing, decryption (where permitted), filtering, and masking

Enriches telemetry with thousands of metadata elements at the network and application layers

Delivers curated evidence to SIEM, XDR, NDR, APM, observability, and AI/analytics platforms

The outcome is straightforward: the same downstream tools you already rely on receive optimized traffic, enriched metadata, and higher quality network-derived telemetry.

This improves the effectiveness of existing SIEM, XDR, NDR, observability, and AI analytics platforms by providing richer context and higher-fidelity telemetry.

That translates directly into:

Stronger detection and threat hunting

Faster, more accurate investigations

Clearer compliance and audit evidence

More reliable AI‑assisted security operations

Instead of drowning your platforms in raw packets or noisy logs, you give them signal: context‑rich, right‑sized, and aligned to real attack paths.

Key Security Insights You Gain From Deep Observability

By combining packets, flows, and network and application metadata with MELT data, deep observability fills in critical gaps in the security story.

It helps you:

Identify what’s truly in use: See which applications and services are running on the network even when ports, labels, or legacy assumptions say something different. This reduces blind trust in CMDBs and static inventories.

Understand live dependencies and data flows: Observe how applications, APIs, and services interact, including multi‑cloud and hybrid paths. That insight reveals where sensitive data travels, which is essential for both risk assessment and compliance.

Spot subtle indicators in encrypted and DNS traffic: Surface DNS anomalies, weak ciphers, certificate issues, or suspicious destinations that may signal tunneling, command‑and‑control, staging, or exfiltration, without relying solely on endpoint or perimeter logs.

Separate theoretical from reachable exposure: Correlate known vulnerabilities and misconfigurations with real traffic paths. If an issue is never exploited in production traffic or, more importantly, if it is, you can change how you prioritize and respond.

In other words, deep observability doesn’t just add more data; it provides the network-derived telemetry and context needed to validate whether risk models match reality.

Managing Shadow AI and Governance With Network Visibility

AI services, copilots, and agents are being adopted faster than most governance programs can evolve. Business units experiment with new tools, developers integrate external models, and third‑party vendors introduce AI into their offerings—often with limited central oversight.

From a security and governance perspective, you need to know:

Where AI tools are being used across your environment

What data flows to and from those services, including regulated or sensitive information

How AI‑related traffic interacts with critical systems and data stores

Application‑level visibility into AI traffic is becoming essential. With deep observability, Application Metadata Intelligence (AMI) can:

Add application‑level context to network flows

Distinguish sanctioned AI services from unsanctioned, shadow AI usage

Help enforce policy based on actual observed behavior, not just allowed domains or ports

This is the foundation for AI governance you can defend to regulators, customers, and boards: real evidence of how AI is used in practice, not just policy documents and training records.

From Detection to Proof: Operational Benefits of Deep Observability

In an AI‑driven SOC, speed is necessary but speed without confidence is dangerous. Deep observability directly supports higher‑quality decisions across the incident lifecycle.

Validate Exploitability

When a scanner, AI engine, or threat intelligence feed flags an issue, deep observability helps you answer:

Is this vulnerability being probed or exploited on real traffic paths?

Which identities, services, or data flows are involved?

Does the observed behavior match a theoretical attack path, or is it noise?

That context supports sharper triage and more targeted incident handling.

Prioritize What Matters Most

Not all findings are equal. By tying vulnerabilities and alerts to observed applications, dependencies, and sensitive data flows, you can:

Rank issues based on business‑critical impact

Focus limited resources on attack paths that are demonstrably live

Reduce “patch everything now” fatigue and avoid chasing low‑risk noise

Prove That Fixes Worked

After a change, patch, or control update, you need to show that risk has come down. With deep observability, you can:

Compare before‑and‑after traffic patterns

Confirm that risky paths or behaviors disappeared

Produce evidence to satisfy internal stakeholders, auditors, and regulators

This closes the loop: detection, investigation, remediation, and validation all draw from the same telemetry foundation. And as more SOC workflows incorporate AI for triage and response, the quality of your input telemetry becomes decisive. High‑fidelity, context‑rich data reduces the risk of faster, but worse, decisions.

Inside a Deep Observability Pipeline Architecture

The Gigamon Deep Observability Pipeline operates through five core functions:

Access: Acquire traffic from taps, virtual taps, cloud‑native mirroring, and container environments so you can see data in motion across physical, virtual, and cloud infrastructure

Broker: Aggregate, normalize, and distribute traffic to the right tools, avoiding redundant feeds and ensuring each platform receives the data it can best use

Transform: Optimize traffic with capabilities such as deduplication, slicing, header removal, decryption (where appropriate and compliant), filtering, and masking to reduce volume while retaining security‑relevant content

Enrich: Add metadata at the network and application layers, including protocol details, application identities, TLS attributes, DNS context, and more, to elevate packets and flows into analytics‑ready telemetry

Manage: Provide centralized policy, orchestration, monitoring, and governance across the pipeline so that security and operations teams can adapt telemetry flows as environments and threats evolve

This architecture improves signal‑to‑noise ratios, reduces telemetry ingestion and processing costs, and increases the effectiveness of existing security and observability investments without requiring a rip‑and‑replace approach.

Getting Ready for AI‑Era Threats With Deep Observability

AI will continue to accelerate discovery, prioritization, investigation, and remediation workflows. The competitive advantage will not go to the organizations that generate the most findings. It will go to those that can determine fastest, and most confidently, which findings matter.

For CISOs and security leaders, the implications are clear:

Strengthen AI governance with visibility into real AI application traffic

Extend observability beyond MELT data to include network-derived telemetry from data in motion wherever it flows

Ground risk decisions in evidence from the network, not assumptions or partial telemetry

Closing AI‑era blind spots starts with deep observability across East-West, encrypted, and cloud‑native traffic. When you can see what matters first—and prove it—you’re better positioned to defend at AI speed.

FAQ: Deep Observability in the AI Era

Q1. What is the difference between traditional observability and deep observability?

Traditional observability focuses on MELT data—metrics, events, logs, and traces—reported by systems themselves. Deep observability adds an independent perspective from data in motion, turning packets, flows, and rich metadata into security evidence across hybrid and multi‑cloud environments.

Q2. How does deep observability support SOC teams using AI‑based tools?

AI‑based SOC tools are only as good as the telemetry they ingest. Deep observability improves the completeness, timeliness, and quality of that telemetry so AI engines can more accurately detect live attack paths, reduce false positives, and automate triage without sacrificing confidence.

Q3. Can deep observability work across hybrid and multi‑cloud environments?

Yes. A Deep Observability Pipeline is designed to acquire and optimize traffic from physical, virtual, container, and cloud environments, creating consistent visibility and enriched telemetry across on‑prem, cloud, and everything in between.

Q4. How does deep observability help uncover shadow AI usage?

By adding application‑level context with capabilities like Application Metadata Intelligence, deep observability reveals where AI services are in use, what data they handle, and how they interact with critical systems—making it possible to distinguish sanctioned from unsanctioned AI and enforce governance based on real traffic.