The Role of Deception Technology in The Network Security Reference Architecture

An attacker who lands on a compromised endpoint does not announce themselves. They move sideways, using valid credentials and living-off-the-land techniques that look like ordinary administration to the tools watching production traffic. That is the moment the network security reference architecture is built to address, and it is the moment Gartner points to deception. The 2024 Gartner Reference Architecture Brief: Network Security places deception alongside prevention and pattern-based detection as a core detection layer, precisely because it keys on adversary intent rather than known signatures.

At a glance

• Gartner’s 2024 Network Security Reference Architecture positions deception as a core detection layer for monitoring network intrusions and, in particular, for detecting lateral movement.
• Traditional network controls such as intrusion prevention systems, network detection and response, and firewalls detect known threats through signatures and anomaly baselines. Deception adds behavioral-independent detection that keys on intent, not pattern.
• Defense in depth pairs prevention controls (micro-segmentation, network access control, firewalls) with detection layers, and deception is the detection layer built for post-breach lateral movement.
• Zero Trust’s assume-breach principle calls for controls that detect active threats already inside the environment. Deception is a foundational control for that principle.
• Deception has produced high-fidelity results under independent testing: 100% true positive alerts and 80% denial of attacker objectives against automated, credential-driven techniques in the U.S. Navy ANTX FY25 exercise.

The reference architecture treats deception as a detection layer, not a curiosity at the edge of the stack. As the Gartner brief states, “Deception is deployed to monitor and respond to a network intrusion,” and, in particular, to detect lateral movement as an attacker moves from a compromised endpoint toward a deceptive network artifact.

That placement matters because the enterprise network no longer has a single perimeter to defend. The traditional model, an on-premises network with all computing hosted behind a perimeter firewall, has been redefined by public and private cloud, SaaS applications, remote work, and branch offices. Each of those shifts widens the attack surface and hands adversaries more pathways to gain a foothold and move laterally. A detection layer that fires on interaction rather than signature travels well across all of that ground, which is why the architecture positions deception across both on-premises and cloud network segments.

Network security was built first for prevention through perimeter-based defense, and the expanded network boundary has changed what prevention alone can accomplish. Adversaries now have multiple routes into the organization and multiple ways to perform lateral movement once inside. Roughly 60% of intrusions involve lateral movement before detection, which is why network threat detection has evolved to combine prevention with detection and response in a layered model, mirroring how cyber deception itself has evolved from static honeypots to AI-driven defense.

Each component of the reference architecture carries a distinct role:

• Enterprise network security concentrates on on-premises and private cloud deployments, using intrusion prevention systems, network detection and response, network access control, and network packet brokers.
• Enterprise edge security governs ingress and egress through enterprise firewalls, virtual private networks, remote desktop gateways, and secure web gateways.
• Secure access service edge (SASE) combines network and security capabilities for branch offices, remote workers, and on-premises access.
• Security service edge (SSE) sits within SASE to streamline secure web and cloud access.
• DDoS mitigation and transmission security defend against external threats and protect data in transit.
• Cloud network security mirrors these controls inside IaaS and PaaS environments, alongside network security processes such as risk assessment and DDoS response planning.

Deception threads through this model as the layer that detects the lateral movement the other components are not designed to adjudicate in isolation.

Defense in depth pairs prevention-based controls with detection layers, and deception is the detection layer designed for the post-breach case. Controls such as micro-segmentation, network access control, and firewalls focus on prevention. Intrusion prevention systems and network detection and response focus on detecting known threats based on signatures or established patterns. Those layers do their job well, and the gap they leave is a matter of judgment rather than capability: authenticated, authorized activity can still be hostile, and lateral movement that uses valid credentials or living-off-the-land techniques is hard to judge from pattern alone. You can learn more about the MITRE ATT&CK techniques attackers use for lateral movement and why they evade signature-based detection.

Deception closes that judgment gap by adding targets no legitimate user has any reason to touch. At the network layer, three mechanisms work together:

• Network decoys that impersonate real servers, workstations, and IoT devices.
• HoneyPaths, the deceptive breadcrumbs that guide an attacker toward the decoys rather than the crown jewels.
• Deceptive network shares, fake file repositories that raise an alert the moment they are accessed.

Because these assets are not part of any production workflow, any interaction with one is a high-confidence signal of intent. The question the alert answers is a simple one: why did anything touch an asset no legitimate process should need? Deception runs alongside your IPS, NDR, and SIEM rather than replacing them, and this network and endpoint deception layer extends detection coverage to the activity that pattern-based tools are not built to adjudicate.

The reference architecture builds the network on Zero Trust, and deception is what operationalizes its hardest principle. Zero Trust rests on three guiding principles:

• Least-privilege access.
• Never trust, always verify.
• Assume breach.

SASE, SSE, and the prevention-based controls for micro-segmentation and network access control deliver the first two by granting least privilege and verifying continuously.

Assume breach is different. It requires the organization to act as though an adversary already has a foothold and to deploy controls that detect and respond to that adversary inside the environment. Deception is a foundational control for assume breach, because it produces early, high-confidence detection of network threats without waiting for a signature to exist. ShadowPlex operationalizes the assume-breach principle by instrumenting the network with deceptive assets at every layer, from network decoys that attract post-breach lateral movement to HoneyPaths that guide attackers away from critical assets and toward detection points. This is where a modern zero trust network architecture gains an active detection capability rather than a purely preventive one, and where Acalvio’s 360 Deception framework extends the model to disrupt machine-speed and agentic attacks.

ShadowPlex delivers the reference architecture’s deception layer as an autonomous, agentless capability rather than a set of hand-built traps. That distinction is the entry requirement for modern networks: hand-built, hand-fed decoys cannot keep pace with a changing environment or with attacks that operate at machine speed, so automation is not a luxury but the baseline. The ShadowPlex Preemptive Cybersecurity Platform delivers this across IT, OT, and cloud network segments.

The coverage includes:

• Multiple decoy types, including servers, workstations, IoT devices, and cloud instances.
• HoneyPaths that surface lateral movement early in the intrusion path.
• Automatic generation and refresh of decoys, with no endpoint agents required.
• Detections that feed existing SIEM and SOAR workflows, so response runs inside the tooling your team already uses.

That operational fit, combined with recognition as a GigaOm Leader and Outperformer in deception technology for four consecutive years (2026), is why the reference architecture’s deception layer is practical to deploy at enterprise scale rather than aspirational.