Securitylab•October 1, 2026•🇷🇺Translated from Russian

Inside the Fortress: Why Perimeter Security Tools Fall Short and How Microsegmentation Protects Networks Internally

Companies annually invest enormous budgets in strengthening the IT perimeter by deploying firewalls and intrusion detection systems. Today, however, these measures no longer serve as a guarantee of security. Attack vectors have shifted: hackers increasingly cause damage from inside networks, moving between servers and seizing critical data. A viable solution lies in protecting the network at the virtualization layer.

The Danger Inside the Network

One problem with traditional networks is that virtual machines inside the same L2 domain are not isolated from each other. From a security perspective, this resembles an apartment building with poor sound insulation. Any resident can overhear neighbors, while unauthorized visitors can freely move between floors and test door locks. For virtual machines holding critical data, this creates risks of traffic interception. For an attacker who has gained a foothold, it provides ideal conditions for scanning neighboring nodes and performing lateral movement to spread malware.

Isolation of objects within a single logical network is also necessary because different applications often have varying security requirements. Some require their own “network safe” — complete isolation to prevent any unauthorized access. This applies not only to controlling access to confidential data but also to protecting the environment from “noisy neighbors,” such as virtual machines that aggressively consume traffic.

Implementing such isolation through classic VLANs and static ACLs on physical switches is practically impossible today. Manually managing thousands of rule lines is too time-consuming and expensive, so security is often sacrificed for faster service deployment.

Why the Traditional Approach Is Insufficient

The traditional network protection model follows the “fortress” principle: network engineers configure powerful perimeter firewalls to block external threats. Yet a firewall cannot control traffic inside the network; it is designed to filter traffic between networks, not within them. Modern web applications consist of hundreds of third-party libraries downloaded from the internet that do not always undergo full audits. If even one such library is compromised, malicious code ends up inside the perimeter.

Modern IT systems rarely operate in complete autonomy. They often involve connections to contractor infrastructure and remote workplace architectures, creating multiple additional entry points without breaching multilayer perimeter defenses. The human factor adds further risk. Without internal controls, security of each virtual machine rests on its administrator, who may disable the OS packet filter during debugging and forget to re-enable it.

Limitations of Firewalls

Placing firewalls in front of every network and every virtual machine is impractical. Two virtual machines in the same VLAN do not require a third network node to communicate, so routing traffic through an inspection point would likely cause significant performance degradation. Firewalls also face throughput limits and can become bottlenecks. Since east-west traffic typically accounts for 80 percent of system traffic, firewalls sized for 20 percent north-south traffic would need to handle four times the volume, leading to increased latency and application degradation.

How Microsegmentation Protects the Network from Within

To achieve the required protection level, the network must be flexible enough to be divided into atomic isolated segments with individual security policies. This approach is realized through microsegmentation — a technology that, at the SDN level, breaks VLANs into numerous microsegments down to individual virtual machine ports. For each virtual machine or group, administrators can define allowed or denied traffic by ports, IP addresses, and protocols.

SDN technologies virtualize the packet path, effectively emulating a virtual packet filter at the desired segment. Microsegmentation via SDN installs these filters directly at the network port exit of the virtual machine. The key security advantage is complete independence from actions inside the virtual machine itself. The mechanism cannot be disabled because it operates at the virtualization infrastructure level and executes immediately upon traffic leaving the virtual machine’s network interface, enforcing the Zero Trust principle.

Performance remains unaffected because microsegmentation code runs on the most powerful component — the virtualization server. Scaling is natural: as the number of virtual machines grows, so does the number of hypervisors, keeping filtering load within established limits. All permissions and prohibitions are managed centrally and can change flexibly. One oil-and-gas customer using zVirt creates up to 5,000 new microsegmentation rules per week thanks to a mature open REST API. In traditional networks, such dynamics would slow infrastructure; in an SDN environment, it becomes standard practice.

Why Microsegmentation Is Harder to Bypass

In a traditional network, one successful perimeter breach places an attacker in a “trust zone” where they can move freely. Microsegmentation multiplies the difficulty. Even if one segment is compromised, the attacker remains confined. Any attempt to scan ports or map the network is blocked, and every probe against a closed segment is logged, enabling early detection. Separation of the control plane from the data plane ensures that even root access on a virtual machine does not allow modification of access rules stored on the SDN controller and hypervisor.

This makes microsegmentation a resilient foundation for network security. Its presence does not eliminate the need for perimeter firewalls; external and internal protections must complement each other. The combination of traditional security tools and SDN delivers both high security under Zero Trust and automated network management, removing the need to choose between protection and speed.

Related articles

Securitylab•Other

OpenAI Releases GPT-6.1 Terra: Cost-Effective Model Focused on Computer Control, Image Analysis and Practical Automation

OpenAI has launched GPT-6.1 Terra, a new model positioned between its high-end GPT-6 Astra and more affordable options in the GPT-6 family. The model emphasizes real-world computer interaction, visual understanding of documents and interfaces, and reduced error rates rather than raw reasoning benchmarks. It supports a 1.05 million token context window, image inputs, and tool use through the Responses API while cutting input token prices by half compared with its predecessor. Published benchmarks show substantial gains on Terminal-Bench 4.0, OSWorld 2.1 and Agents' Last Exam, with error rates dropping sharply at both low and maximum reasoning levels. OpenAI stresses that the model still requires human oversight for high-stakes actions and does not directly process audio or video. The release targets developers and enterprises seeking reliable automation of repetitive multi-step tasks at lower cost than flagship models.

Habr•Other

Yandex Black October: How Architectural Flaws and Capacity Mismanagement Turned Data Center Losses Into a 11-Day Fintech Coma

In October 2026 three Yandex data centers in Central Russia were physically destroyed within days, triggering the collapse of Yandex Cloud and multiple core services. Between the first and third site failures engineers had 74 hours to restore quorum and rebalance workloads yet failed to do so because Control Plane components remained tied to the lost zone and spare capacity had been eliminated to cut costs. Core banking and BNPL systems entered Read-Only mode after losing quorum, forcing a complete stop-the-world reconciliation that lasted until 22 October. Backups stored inside the same regional S3 buckets proved unusable, resulting in a seven-day RPO and primary-key collisions after restoration. The incident exposed that Metro-Cluster designs had been incorrectly treated as full Disaster Recovery and that aggressive AP microservice patterns created logical split-brain conditions outside the database layer.

AntiMalware•Other

Astra Group Releases Low-Code Astra AI Platform for Building Corporate AI Agents in Closed Environments

Astra Group has launched the Astra AI Platform, a low-code constructor designed for creating corporate AI applications that operate entirely within closed circuits without reliance on foreign clouds. The platform targets small and medium businesses as well as large corporations, enabling rapid deployment of AI secretaries, technical support assistants, and other agents in days rather than months. It forms part of the broader Astra AI ecosystem and includes two main constructors: one for machine learning model training and deployment, and another for LLM-based language assistants, document search, and process automation. Users can assemble scenarios from visual blocks, debug them in real time, or extend functionality with custom Python code when needed. The solution supports RAG-based knowledge bases, integration with external tools such as CRM and ERP systems, and the coordination of multiple agents for distributed task handling. One demonstrated use case involves processing ten pages of engineering drawings and specifications in five minutes to prepare data for downstream systems. The product is already listed in the Russian domestic software registry and available to customers.

Habr•Other

Multi-Tenancy Isolation in VictoriaMetrics, VictoriaLogs and Grafana: Building Client Separation in a Shared Stack

Voltir runs a single shared monitoring stack consisting of VictoriaMetrics, VictoriaLogs, Grafana and vmalert for all customers instead of deploying separate instances per tenant. Isolation relies on numeric account IDs enforced at every layer: nginx validates tokens against the platform API before proxying writes, Grafana data sources hard-code tenant accounts, and vmalert rules receive automatic account filters. The team discovered that a single malformed rule or template could break alerting for every tenant because vmalert loads the entire rules directory at once. They also found that Grafana automatically created personal organizations granting Org Admin rights, accumulating hundreds of orphaned organizations and users that consumed disk space and resources. A dedicated cleanup module plus hourly reconciliation now removes rules, organizations, series and users when a tenant is deleted, while isolation tests verify both data and API objects across tenants.