Why AI-Powered Security Needs Network Telemetry Across the Hybrid Cloud

AI is quickly becoming embedded in how security and IT teams operate. From threat detection to incident investigation to compliance validation, AI is exciting us with complex reasoning and faster answers.

But there’s a simple constraint that gets overlooked:

AI can only be as effective as the data it can see and analyze.

In hybrid and multi-cloud environments, one of the big emerging datasets is network telemetry.

The Limits of AI Without Network Visibility and Telemetry

Early entrants into AI-driven analysis for security and performance have leaned heavily on logs and events. Those are useful, but they only show what systems choose to report, not the unfettered truth of what is actually happening on the wire. This is a critical distinction, especially for security, where attackers have long demonstrated an ability to control and manipulate what is reported by logs and endpoint detection.

As environments span data centers, public clouds, VMs, and containers, more activity is happening beyond the reach of logs alone:

• East-West traffic moving laterally between hosts and workloads
• Encrypted communications that mask risky behavior
• Conscription of standard apps and protocol to mask nefarious activity
• DNS and TLS activity that often signals early-stage threats

Without visibility into traffic in motion, AI is forced to draw conclusions from incomplete, sometimes misleading data. The result:

• Missed detections when attacks blend into log noise
• Noisy alerts when systems overreact to partial signals
• Slower investigations because teams must reconstruct what really happened

Network Telemetry as Ground Truth for AI

Network-derived telemetry is an alternative which provides the solid foundation AI needs to create proper analyses.

By observing traffic directly from the network across physical, virtual, cloud, and containerized environments, network telemetry captures what applications, users, and systems are actually doing—not just what they report. That includes:

• What is communicating with what
• Which applications and protocols are truly in use
• How traffic is spiking abnormally
• Where latency, errors, or anomalies appear
• Who is sending data externally

Raw packets alone are not practical for AI to process at scale. This is where a deep observability pipeline becomes essential.

How Network Telemetry Becomes AI-Ready Data 

Within a deep observability pipeline, network traffic is analyzed and transformed into structured, enriched telemetry that AI-powered systems can actually use.

Traffic is classified, contextualized, and converted into metadata that reflect real behavior instead of assumptions, including:

• Application identity, even when ports are being spoofed
• DNS behavior that indicates tunneling or command-and-control activity
• TLS posture, including weak ciphers and expired or misconfigured certificates
• Performance indicators across both network and application layers
• Connections to suspicious or unexpected destinations
• Encrypted traffic that violates security or compliance policy

Instead of stitching together disconnected logs, AI gains access to a consistent, trusted, enriched dataset describing all data in motion. This is where application metadata, derived from the network, becomes a force multiplier for existing tools. When delivered into AI-enabled SIEM platforms, teams gain:

• More accurate detections with fewer blind spots
• Tighter, more focused investigations
• Stronger evidence to support response and remediation

Using Network Telemetry for AI-Assisted Troubleshooting and Performance Insight

The value extends beyond security.

Network telemetry also enables AI systems to analyze performance signals across network, application, and infrastructure tiers. By comparing network round-trip time with application response time, teams can more quickly pinpoint where degradation starts.

This evidence-based approach helps answer questions that traditionally trigger long war-room sessions:

• Is latency introduced in transit or after the request arrives?
• Are retransmissions or packet drops contributing to slowdowns?
• Is the issue rooted in the network, the application, or the underlying infrastructure?

AI-assisted analysis, backed by network telemetry, shortens mean time to resolution and reduces cross-team friction because everyone is working from the same source of truth.

Network Telemetry and AI Governance

As organizations adopt GenAI and LLM-powered applications, governance becomes a new challenge. AI workloads increase traffic volumes, expand attack surfaces, and often bypass traditional controls.

Network telemetry provides the data foundation teams need to govern AI use:

• Identification of sanctioned and unsanctioned AI applications
• Insight into when and where AI traffic appears
• Evidence to support policy enforcement and risk management

This level of visibility is critical as AI becomes embedded across enterprise environments and as DNS monitoring, TLS visibility, and TLS certificate monitoring grow in importance.

Teaching AI What to Analyze

AI does not replace human judgment—but it can amplify it when grounded in accurate, trusted data.

Network telemetry gives AI access to the information it needs to:

• Detect threats earlier
• Investigate incidents faster
• Validate compliance continuously
• Support more efficient operations

Without network-derived telemetry, AI is left inferring from partial signals. With it, AI can analyze what is actually happening across the hybrid cloud in real time, across all data in motion. That difference is what turns AI from a reactive assistant into a reliable decision partner across security, operations, and cloud teams.

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