Why Today’s SOCs Need Multi-Layered Adoption

The cycle is over. For years, cybersecurity followed a familiar pattern: defenses improved, attackers adapted, and the back and forth continued. Today, AI-armed attackers simply bypass defenses. Most intrusions now bypass endpoint and malware-based detection entirely.

The CrowdStrike Global Threat Report estimates that about 79% of attacks do not contain malware, as threat actors rely on phishing and DLL sideloading techniques to bypass host-level monitoring. Perimeter risk includes these exposures; firewalls and VPN gateway breaches increased by 19% according to the latest Verizon Data Breach Investigations Report.

Once the enemy has access, outbreaks often occur within seconds. The Claude Mythos and similar models have increased performance pressure. These can quickly detect and exploit previously unknown vulnerabilities, virtually closing the window from initial detection to full mitigation.

Security processes must adapt to prioritize rapid containment and post-compromise behavior analysis, and defense forces now require real-time detection that goes beyond host-level coverage. That’s where multi-layered network detection comes in, extending defenses beyond the endpoint—but its effectiveness depends heavily on the data behind it.

Network evidence reinforces discovery

Endpoint, identity, and cloud platforms each offer an important perspective on enterprise security. Host tools track processes in memory, proprietary solutions monitor credentials, and log configuration changes for cloud environments. Although each source provides visibility, these systems work in isolation, leaving gaps in visibility that attackers can easily exploit.

Each tool only sees its part of the attack chain. Threat actors can compromise the workplace, exploit blind spots between storage and identity systems to hide data theft, move it sideways to cloud infrastructure, and exfiltrate data before the SOC is aware. That’s why unified, correlated telemetry across these domains is critical to revealing the full picture.

Network Discovery and Response (NDR), validates, enriches, and connects these different signals using network data. Because it is collected out-of-band, data remains intact even when local agents go dark or when threat actors disable endpoint devices. And because it captures traffic across the enterprise, NDR provides a valuable context, recording all conversations, transactions, and data transfers, bringing defenders the undeniable evidence they need to answer.

For example, if an identity tool flags an unusual login, network data confirms that account has initiated unauthorized database queries. If an endpoint alert flags an authentication access, it helps verify whether an adversary has attempted a coordinated move.

Multi-layered discovery builds confidence in decisions

Most organizations already have some form of network visibility, such as legacy intrusion detection systems (IDS), electronic packet capture devices (PCAP), or basic NetFlow logs. However, these valuable tools work in isolation, and most fail to match the speed analysts need to respond to modern attacks. NDR replaces these patchy, legacy tools.

By combining signatures, packet analysis, and logflow into a single application, NDR delivers a comprehensive array of detections and capabilities that dramatically ease the analyst’s cognitive load. Rather than searching through a large volume of separate, unconnected alarms, defenders use multiple layers of integrated network detection to find specific evidence.

• Signature-based detection and threat intelligence: This provides rapid verification of documented exploits, capture of known threats and malicious files of history with high accuracy, and detection of communications with established adversary infrastructure. However, to identify post-exploitation activity, modern automated tools require advanced and unconventional behavioral layers.
• Behavioral discovery: Behavioral models identify adversary tactics, strategies, and processes (TTPs) regardless of specific files or exploit code. For example, they can identify suspected command and control tactics without relying on specific indicators.
• Mysterious findings: Anomalous detection flags structural differences from baseline network traffic, such as a workstation that behaves like an internal port scanner, identifies connections to a large number of previously unseen hosts, or displays communication patterns that indicate data collection.
• Supervised ML models: These machine learning models excel at identifying patterns that are difficult to capture using signatures or rule-based logic, thereby increasing coverage against threats that evade traditional detection methods. They can detect indicators of corruption in encrypted traffic, identify malicious domains, and help uncover tunnels within the network.
• AI: Rather than delivering standalone alerts that force analysts to guess at complexity, advanced artificial intelligence engines correlate alerts from various telemetry sources and layers and map attacker behavior. This integration reduces confusion, tracks the complete kill chain, and builds confidence in operational decisions. With proven, relational intelligence, analysts shift from reassuring warnings to quick checks and grips.

To achieve this level of operational clarity, security leaders must invest in full lifecycle security. This scenario is predicated on advanced network telemetry that can reveal enemy activity fast enough to match the operational tempo of Mythos-class threats.

AI only works as evidence behind it

As a protective layer, AI currently excels in threat testing, automated workflows, and incident summarization. However, the basic rule remains intact: garbage in, garbage out.

The effectiveness of AI-driven security is limited by the “information ceiling” determined by source data, not model choice. Even the most advanced models cannot overcome the limitations imposed by low-quality or non-existent data. Invest in data; everything else follows.