Comprehensive Analysis of Network Infrastructure Security: Best Practices and Emerging Technologies

Abstract

Network infrastructure security stands as an indispensable pillar in the strategic defense of organizational assets, meticulously ensuring the integrity, confidentiality, and availability of critical data across an increasingly complex digital landscape. This extensive research report undertakes a profound examination of both well-established foundational best practices and cutting-edge, emergent technologies crucial for contemporary network infrastructure security. It delves into the architectural paradigm of Zero Trust, elucidates advanced network security solutions such as Network Detection and Response (NDR), explores specialized strategies for safeguarding the rapidly expanding Internet of Medical Things (IoMT), outlines robust cloud network security best practices, and details intricate methods for fortifying operational technology (OT) within highly sensitive environments, with a particular focus on healthcare settings. The overarching aim of this paper is to furnish a comprehensive and granular understanding of the multifaceted, adaptive approaches inherently required to elevate and sustain network security resilience in the face of an ever-evolving and sophisticated threat landscape.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

In the pervasive digital age, organizations across all sectors have become inextricably linked to and fundamentally reliant upon intricate and sprawling network infrastructures to underpin virtually every facet of their operations, from day-to-day administrative tasks to mission-critical service delivery. This profound dependency has, in turn, critically magnified the imperative for exceptionally robust and agile network security measures, designed to provide an unyielding bulwark against a rapidly proliferating array of cyber threats. Network infrastructure security, in its holistic sense, encompasses an elaborate tapestry of policies, meticulously defined practices, and sophisticated technological tools specifically engineered to safeguard the integrity, uphold the confidentiality, and guarantee the continuous availability of data as it traverses the numerous arteries and veins of organizational networks. The inherent dynamism and persistent evolution of cyber threats, ranging from highly sophisticated state-sponsored attacks to opportunistic ransomware campaigns, necessitates not merely a reactive posture but a continuous, proactive evolution of security strategies. This ongoing adaptation is crucial to effectively anticipate, address, and mitigate emerging vulnerabilities, novel attack vectors, and the increasingly cunning methodologies employed by malicious actors. The traditional perimeter-based security model, once the bedrock of network defense, is now demonstrably insufficient given the widespread adoption of cloud computing, remote workforces, and the proliferation of IoT devices, all of which dissolve conventional network boundaries. Consequently, the discourse has shifted towards more adaptive, identity-centric, and data-aware security paradigms that can provide consistent protection regardless of location or device.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. Best Practices in Network Infrastructure Security

Establishing a resilient network security posture requires a multi-layered approach, built upon a foundation of fundamental best practices that are continuously refined and adapted.

2.1 Vulnerability Assessments and Penetration Testing

Regular and systematic vulnerability assessments (VAs) alongside meticulously planned penetration tests (PTs) represent foundational and non-negotiable practices in the proactive identification and comprehensive mitigation of potential security weaknesses embedded within network infrastructures. While often conflated, these two practices serve distinct yet complementary roles in a robust security program.

Vulnerability Assessments (VAs) involve the systematic scanning and analysis of network components, applications, and systems to detect known vulnerabilities. These assessments typically leverage automated tools that compare system configurations and software versions against extensive databases of known security flaws (CVEs – Common Vulnerabilities and Exposures). The primary output of a VA is a prioritized list of vulnerabilities, often categorized by severity, along with recommendations for remediation. Types of vulnerability assessments include:

• Network-based Vulnerability Scans: These scan the network perimeter and internal segments to identify vulnerable hosts, open ports, and misconfigured services.
• Host-based Vulnerability Scans: Performed directly on servers, workstations, and other endpoints to detect misconfigurations, missing patches, and insecure settings.
• Web Application Scans: Target web applications to uncover vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure direct object references.
• Database Scans: Focus on database systems to identify weak configurations, missing patches, and overly permissive access controls.
• Configuration Reviews: Manual or automated checks against established security baselines (e.g., CIS Benchmarks) to ensure secure system hardening.

Penetration Testing (PTs), in contrast, moves beyond mere identification to actively exploit identified vulnerabilities in a controlled and ethical manner. A penetration test simulates real-world attacks by skilled security professionals, often referred to as ‘ethical hackers’, to evaluate the actual effectiveness of existing security controls, incident response capabilities, and the overall resilience of the organization’s defenses. PTs provide a deeper understanding of how an attacker might combine multiple low-severity vulnerabilities to achieve a significant breach. Methodologies vary:

• Black Box Testing: Simulates an external attacker with no prior knowledge of the internal network architecture or systems. This approach tests perimeter defenses and public-facing assets.
• White Box Testing: The testers are given full knowledge of the target system, including network diagrams, source code, and credentials. This allows for a more comprehensive and deep-seated assessment of internal vulnerabilities.
• Grey Box Testing: A hybrid approach where testers have partial knowledge of the internal systems, mimicking an insider threat or an attacker who has gained some initial access.

The frequency of these practices is crucial; VAs should be conducted regularly (e.g., weekly or monthly for critical systems), while PTs are typically performed annually or following significant architectural changes. Defining the scope is paramount, clearly outlining the assets to be tested, the allowable techniques, and the desired outcomes. Post-assessment, the emphasis shifts to remediation and verification, ensuring that identified flaws are patched, reconfigured, or otherwise mitigated, and then re-tested to confirm the effectiveness of the remediation efforts. Adherence to ethical hacking principles, including explicit authorization and non-disruptive testing, is critical. Furthermore, compliance frameworks like PCI DSS, HIPAA, and ISO 27001 often mandate regular vulnerability management and penetration testing, underscoring their importance (NIST, n.d.). Beyond these, the concepts of Red Teaming (a full-scope adversarial simulation designed to test an organization’s overall detection and response capabilities) and Blue Teaming (the internal defensive security team responsible for defending against real and simulated attacks) are increasingly employed to foster continuous improvement in security operations.

2.2 Firewalls and Intrusion Detection/Prevention Systems (IDS/IPS)

Firewalls and Intrusion Detection/Prevention Systems (IDS/IPS) constitute the cornerstone of network perimeter defense and internal traffic control, forming an indispensable tandem in safeguarding network integrity.

Firewalls serve as the primary gatekeepers, enforcing security policies by monitoring and controlling incoming and outgoing network traffic based on a predefined set of security rules. Their evolution reflects the increasing sophistication of threats:

• Stateless Firewalls (Packet Filters): The most basic type, these examine individual packets in isolation, allowing or blocking them based on source/destination IP addresses, ports, and protocols, without considering the context of the connection.
• Stateful Inspection Firewalls: These are more advanced, maintaining a ‘state table’ of active connections. They can determine if a packet is part of an established, legitimate session, providing a significant security enhancement over stateless filters.
• Proxy Firewalls (Application-Level Gateways): These operate at the application layer, acting as an intermediary for network connections. They break the client-server connection into two, inspecting and filtering traffic at a deeper level (e.g., filtering HTTP content).
• Next-Generation Firewalls (NGFWs): Representing a significant leap, NGFWs integrate traditional firewall functionalities with advanced features such as deep packet inspection (DPI), application awareness and control, integrated intrusion prevention, identity awareness, and threat intelligence feeds. They can identify and block threats based on application signatures, user identities, and contextual information, rather than just IP addresses or ports.
• Web Application Firewalls (WAFs): Specifically designed to protect web applications from common web-based attacks (e.g., SQL injection, cross-site scripting, DDoS) by inspecting HTTP/S traffic.
• Cloud Firewalls: Implemented as a service within cloud environments (e.g., AWS Security Groups, Azure Network Security Groups, Google Cloud Firewall), these provide virtual network segmentation and traffic filtering for cloud-hosted resources.

Effective firewall deployment requires careful rule configuration, continuous auditing, and integration with other security tools.

Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) complement firewalls by providing real-time threat analysis and response capabilities. While firewalls block known bad traffic based on rules, IDS/IPS aim to detect and prevent more subtle or novel attacks:

• Intrusion Detection Systems (IDS): These passively monitor network traffic or host activities for signs of suspicious behavior, policy violations, or known attack signatures. When a threat is detected, an IDS generates an alert but does not actively block the traffic. Types include:

  • Network-based IDS (NIDS): Monitors traffic across a network segment, analyzing protocols and patterns for anomalies or known attack signatures.
  • Host-based IDS (HIDS): Monitors activity on a specific host, including file system changes, system calls, and event logs.
  • Signature-based Detection: Identifies threats by comparing network traffic or host activity against a database of known attack signatures (patterns of malicious activity).
  • Anomaly-based Detection: Builds a baseline of normal network or host behavior and flags any deviations from this baseline as suspicious. This can detect novel, zero-day threats but may produce more false positives.

• Intrusion Prevention Systems (IPS): These are active security components that not only detect threats but also proactively intervene to prevent identified malicious activities from reaching their target. When an IPS detects a threat, it can automatically block the malicious traffic, drop suspicious packets, reset connections, or quarantine compromised hosts. IPS can be deployed as Network-based IPS (NIPS) or Host-based IPS (HIPS), functioning similarly to their IDS counterparts but with enforcement capabilities.

The integration of these systems is vital. Firewalls provide the first line of defense, blocking clear threats, while IDS/IPS add a critical layer of deep inspection and real-time response, identifying and mitigating more sophisticated or evasive attacks that might bypass initial firewall rules. Challenges include managing false positives, keeping signature databases updated, and ensuring proper placement to maximize coverage without creating bottlenecks. Both IDS and IPS generate extensive logs, which are typically fed into a Security Information and Event Management (SIEM) system for centralized analysis and correlation, enhancing overall threat visibility (Calyptix Security, n.d.).

2.3 Network Segmentation

Network segmentation is a foundational security strategy that involves logically and/or physically dividing a larger, monolithic network into smaller, isolated sub-networks or segments. This practice is increasingly recognized as indispensable for limiting the lateral movement of attackers within a compromised network, ensuring that a breach in one segment does not catastrophically compromise the entire infrastructure. The principle behind segmentation is to create granular security zones, allowing for differentiated security policies to be applied to different types of assets or data.

The benefits of effective network segmentation are manifold:

• Containment: In the event of a breach, segmentation acts as a ‘firewall’ within the network, containing the compromise to a limited area and preventing attackers from easily moving to other critical systems or sensitive data stores.
• Reduced Attack Surface: By isolating systems that don’t need to communicate, segmentation reduces the pathways an attacker can exploit.
• Improved Performance: Segmenting broadcast domains can reduce network congestion and improve overall performance.
• Enhanced Compliance: Many regulatory frameworks (e.g., PCI DSS, HIPAA, GDPR) require the isolation of sensitive data (e.g., cardholder data, Protected Health Information) to specific segments, making compliance easier to achieve and demonstrate.
• Granular Policy Enforcement: Different segments can have distinct security policies, access controls, and monitoring mechanisms tailored to the specific risks and requirements of the assets they contain.

Various techniques facilitate network segmentation:

• Virtual Local Area Networks (VLANs): VLANs allow devices connected to the same physical switch to be logically segmented into different broadcast domains. Traffic between VLANs typically requires routing through a Layer 3 device (e.g., a router or Layer 3 switch), where security policies can be enforced.
• Subnetting: Dividing a large IP address range into smaller subnets, each representing a distinct network segment. This is a basic form of logical segmentation.
• Micro-segmentation: This is an advanced form of segmentation that extends the principle of isolation down to the individual workload level (e.g., virtual machines, containers, applications). Unlike traditional segmentation, which might use VLANs to isolate entire departments, micro-segmentation uses software-defined networking (SDN) principles and host-based firewalls to create granular, policy-driven security zones around each workload. This drastically reduces the attack surface and lateral movement capabilities within data centers and cloud environments.
• Firewall-based Segmentation: Deploying internal firewalls (physical or virtual) between segments to enforce traffic policies. Next-Generation Firewalls (NGFWs) are particularly effective here, offering deep packet inspection and application-level control.
• Software-Defined Networking (SDN)-based Segmentation: SDN controllers can programmatically define network segments and enforce policies across the entire network infrastructure, offering greater flexibility and automation than traditional methods.

Practical implementation considerations for network segmentation include:

• Traffic Flow Analysis: Before segmenting, it’s crucial to understand current network traffic patterns to avoid disrupting legitimate communications. Tools for network flow analysis (e.g., NetFlow, IPFIX) are invaluable.
• Policy Enforcement: Defining clear, comprehensive security policies for inter-segment communication, typically following the principle of least privilege.
• Management Overhead: While beneficial, excessive segmentation without proper management tools can increase operational complexity. Automation and centralized policy management are key.

Network segmentation is a core component of a Zero Trust Architecture, where it enables the principle of ‘least privilege’ by limiting access to specific network resources based on context and verified identity, rather than network location. This proactive approach significantly enhances overall network security posture (NSA, 2022).

2.4 Identity and Access Management (IAM)

Identity and Access Management (IAM) is a critical best practice that forms the backbone of modern network security, controlling who has access to what, when, and how. IAM encompasses the policies, processes, and technologies used to manage digital identities and control their access to resources. Its importance has skyrocketed with the decentralization of workforces and the proliferation of cloud services, moving beyond simple authentication to continuous authorization and contextual access.

Key components and principles of IAM include:

• Multifactor Authentication (MFA): Requires users to provide two or more verification factors to gain access to a resource (e.g., something you know like a password, something you have like a phone or token, something you are like a fingerprint). MFA dramatically reduces the risk of credential compromise.
• Single Sign-On (SSO): Allows users to authenticate once to access multiple independent software systems or applications, improving user experience while centralizing identity management and reducing password fatigue.
• Role-Based Access Control (RBAC): Assigns permissions to roles, and then users are assigned to roles. This simplifies access management, ensures consistency, and helps enforce the principle of least privilege, as users only have the access necessary for their job functions.
• Least Privilege Principle: Users and systems should only be granted the minimum necessary permissions to perform their authorized tasks. This limits the potential damage an attacker can inflict if an account or system is compromised.
• Privileged Access Management (PAM): Specifically focuses on securing, managing, and monitoring privileged accounts (e.g., administrator accounts, service accounts) that have elevated permissions. PAM solutions often include features like just-in-time access, session recording, and credential vaulting to minimize the risk associated with these high-value accounts.
• Identity Governance and Administration (IGA): Encompasses the processes and tools for identity lifecycle management (provisioning, deprovisioning), access request workflows, and audit/compliance reporting to ensure access rights are consistently appropriate.
• Context-Aware Access: Modern IAM systems can leverage contextual information (e.g., user location, device posture, time of day, unusual behavior) to make dynamic access decisions, adding an extra layer of security.

IAM is crucial for both human users and non-human entities, such as APIs, microservices, and IoT devices, each requiring robust identity and access controls. Implementing a strong IAM framework not only enhances security by preventing unauthorized access but also improves operational efficiency and simplifies compliance with various regulatory mandates (Sattrix, 2025).

2.5 Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR)

Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) are advanced capabilities that aggregate, analyze, and respond to security data, transforming raw logs into actionable intelligence and automated defensive actions.

Security Information and Event Management (SIEM) solutions serve as centralized platforms for collecting, correlating, and analyzing security-related data from a multitude of sources across the entire IT infrastructure. These sources include firewalls, IDS/IPS, servers, endpoints, applications, cloud services, and network devices. The primary functions of SIEM are:

• Log Aggregation and Retention: Consolidating logs from diverse sources into a central repository, often for extended periods to meet compliance requirements and support forensic investigations.
• Data Normalization: Converting disparate log formats into a common schema for consistent analysis.
• Event Correlation: Identifying relationships between seemingly unrelated events to detect complex attack patterns that might otherwise go unnoticed. For instance, correlating multiple failed login attempts on a server with an unusual outbound data transfer from the same machine.
• Real-time Alerting and Dashboards: Notifying security analysts of detected threats or policy violations, and providing intuitive dashboards for visualizing security posture.
• Compliance Reporting: Generating reports for various regulatory frameworks by demonstrating adherence to security controls through log data.

While SIEM excels at detecting and alerting, it often requires significant human intervention for investigation and response. This is where SOAR platforms come into play.

Security Orchestration, Automation, and Response (SOAR) platforms are designed to enhance and streamline security operations by automating repetitive tasks, orchestrating complex security workflows, and providing incident response playbooks. SOAR empowers security teams to react more rapidly and consistently to alerts generated by SIEM and other security tools.

Key capabilities of SOAR include:

• Orchestration: Connecting and integrating various disparate security tools (e.g., firewalls, EDR, threat intelligence platforms, vulnerability scanners) to work together seamlessly within an automated workflow.
• Automation: Automating routine, repetitive security tasks, such as enriching alerts with threat intelligence, blocking malicious IPs, isolating compromised endpoints, or performing vulnerability scans. This frees up analysts to focus on more complex threats.
• Incident Response (IR) Playbooks: Defining pre-built, standardized workflows (playbooks) for common incident types. These playbooks guide analysts through the steps of an investigation and response, ensuring consistency and efficiency.
• Case Management: Providing a centralized interface for tracking and managing security incidents, facilitating collaboration among security team members.

The synergy between SIEM and SOAR is powerful: SIEM identifies threats and generates alerts, while SOAR takes these alerts, enriches them with context, automates initial response actions, and orchestrates the subsequent steps of the incident response process. This integration significantly reduces mean time to detect (MTTD) and mean time to respond (MTTR), thereby enhancing the overall effectiveness and efficiency of an organization’s security operations center (SOC).

2.6 Data Encryption and Data Loss Prevention (DLP)

Protecting sensitive data is paramount, and two essential best practices for achieving this are data encryption and Data Loss Prevention (DLP).

Data Encryption is the process of transforming data into an unreadable format (ciphertext) using an algorithm and a key, making it incomprehensible to unauthorized parties. It’s a fundamental control for maintaining data confidentiality.

Key aspects of data encryption include:

• Data at Rest Encryption: Protects data stored on devices (hard drives, solid-state drives, databases, cloud storage). If a storage device is stolen or accessed without authorization, the data remains unreadable without the decryption key. Examples include full disk encryption (FDE), database encryption (TDE), and cloud object storage encryption.
• Data in Transit Encryption: Secures data as it moves across networks, preventing eavesdropping and tampering. This is achieved using protocols like Transport Layer Security (TLS) for web traffic (HTTPS), Virtual Private Networks (VPNs) for secure network connections, and Secure Shell (SSH) for remote administration.
• Data in Use Encryption: This is the most complex form, aiming to protect data even when it is actively being processed by an application. Emerging technologies like homomorphic encryption and secure enclaves are attempting to address this challenge.
• Symmetric vs. Asymmetric Encryption: Symmetric encryption uses a single key for both encryption and decryption, offering high speed. Asymmetric encryption (public-key cryptography) uses a pair of keys (public and private), enabling secure key exchange and digital signatures.
• Key Management: The security of encrypted data critically depends on the secure management of encryption keys. This involves secure generation, storage, distribution, rotation, and revocation of keys, often managed by Hardware Security Modules (HSMs) or Key Management Services (KMS).

Implementing robust encryption across the entire data lifecycle is crucial for protecting sensitive information from breaches and meeting compliance requirements (e.g., HIPAA, GDPR, PCI DSS).

Data Loss Prevention (DLP) refers to a set of tools and processes designed to ensure that sensitive data is not lost, misused, or accessed by unauthorized users. DLP solutions identify, monitor, and protect sensitive data wherever it resides – on endpoints, across networks, and in cloud environments.

Core functions of DLP include:

• Content Discovery and Classification: DLP solutions scan data repositories (file shares, databases, cloud storage) to identify and classify sensitive information based on predefined rules (e.g., credit card numbers, social security numbers, patient records) or custom policies.
• Monitoring and Analysis: DLP monitors data movement in real-time across network channels (email, web, instant messaging), endpoint activities (USB drives, printing, copy/paste), and cloud applications. It analyzes content against established policies.
• Policy Enforcement: When sensitive data is detected attempting to violate a policy, DLP can take various actions:
  • Blocking: Prevent the transmission or movement of the data.
  • Quarantining: Isolate the data for review.
  • Alerting: Notify security teams and/or users.
  • Encryption: Automatically encrypt data before it leaves the defined secure zone.
  • Auditing: Log all data movements and policy violations for compliance and forensic purposes.

Implementing DLP requires a clear understanding of what data is sensitive, where it resides, and how it flows within the organization. Effective DLP policies minimize the risk of accidental data leaks, malicious exfiltration, and non-compliance with data protection regulations. The combination of strong encryption and comprehensive DLP provides a formidable defense against data breaches (Mushroom Networks, n.d.).

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. Advanced Network Security Technologies

Beyond foundational best practices, advanced technologies are essential for addressing the evolving sophistication of cyber threats and the dynamic nature of modern IT environments.

3.1 Zero Trust Architecture

Zero Trust Architecture (ZTA) represents a paradigm shift in network security, moving away from the traditional perimeter-centric model that implicitly trusts anything inside the network. Instead, ZTA operates on the fundamental principle of ‘never trust, always verify.’ This means that no user, device, or application is inherently trusted, regardless of its location (inside or outside the traditional network perimeter). Every access request must be authenticated, authorized, and continuously validated.

Originating from concepts articulated by John Kindervag at Forrester Research, ZTA has gained significant traction as organizations grapple with cloud adoption, mobile workforces, and sophisticated insider threats. The core tenets of ZTA, as articulated by the National Institute of Standards and Technology (NIST SP 800-207), include:

• All data sources and computing services are considered resources.
• All communication is secured regardless of network location.
• Access to individual enterprise resources is granted on a per-session basis.
• Access to resources is determined by dynamic policy — including the observable state of client identity, application/service, and the requesting asset — and may include other behavioral and environmental attributes.
• The enterprise monitors and measures the integrity and security posture of all owned and associated assets.
• All resource authentication and authorization are dynamic and strictly enforced before access is granted.
• The enterprise collects as much information as possible about the current state of assets, network infrastructure, and application/service and uses it to improve its security posture.

Implementing ZTA involves several key pillars:

1. Identity Verification: Strong, multi-factor authentication (MFA) is mandatory for every user and every access attempt. This extends beyond human users to machine identities (e.g., APIs, microservices).
2. Device Posture Assessment: Before granting access, the security posture of the accessing device (e.g., patched, free of malware, compliant with security policies) is continuously evaluated.
3. Application and Workload Security: Access is granted to specific applications or workloads, not the entire network. This involves robust application security measures and API security.
4. Data Protection: Data classification, encryption, and data loss prevention (DLP) are integrated to protect sensitive information at rest and in transit.
5. Micro-segmentation: Network segmentation is applied at a granular level, isolating individual workloads and applying fine-grained policies to limit lateral movement within the network. This ensures that even if one component is compromised, the blast radius is minimal.
6. Continuous Monitoring and Threat Intelligence: All network traffic, user activities, and device states are continuously monitored and analyzed for anomalies, feeding into real-time threat detection and response systems.
7. Least Privilege Access: Users and devices are granted only the minimum access necessary for their tasks, and this access is dynamically adjusted based on context.

Benefits of adopting ZTA include a significantly reduced attack surface, enhanced data protection, improved compliance, and a more robust incident response capability. However, implementation challenges can include the complexity of existing legacy systems, the need for deep visibility into network traffic, and managing the dynamic nature of policies (CyberProof, n.d.). Organizations often adopt ZTA incrementally, starting with critical assets and gradually expanding the scope.

3.2 Network Detection and Response (NDR)

Network Detection and Response (NDR) solutions represent a sophisticated evolution in threat detection, offering real-time monitoring and advanced analysis of network traffic to identify and respond to both known and unknown threats that may evade traditional signature-based security controls. NDR platforms provide deep visibility into network activity, making them invaluable for detecting advanced persistent threats (APTs), insider threats, and sophisticated malware that might have bypassed perimeter defenses.

NDR solutions typically work by capturing and analyzing raw network traffic (packets) or enriched flow data (e.g., NetFlow, IPFIX) across various points in the network, including the perimeter, internal segments, and cloud environments. Key capabilities include:

• Packet Capture and Analysis: Detailed inspection of network packets to understand the content, protocols, and metadata of communications.
• Flow Data Analysis: Leveraging flow records to gain insights into connection patterns, volume, and communication endpoints without storing full packet payloads.
• Behavioral Analytics and Machine Learning (ML): Building baselines of normal network behavior (e.g., typical data volumes, communication partners, protocol usage, user activity patterns). NDR then uses ML algorithms to detect deviations from these baselines, identifying anomalies that could indicate malicious activity (e.g., unusual data exfiltration, command-and-control communication, lateral movement).
• Signature-less Detection: Unlike traditional IDS/IPS that rely heavily on known signatures, NDR can detect novel threats and zero-day exploits by identifying anomalous behaviors rather than specific attack patterns.
• Threat Intelligence Integration: Correlating network activity with external threat intelligence feeds (e.g., known malicious IPs, domains, malware hashes) to identify indicators of compromise (IoCs).
• Deep Visibility: Providing comprehensive visibility into East-West (internal network) traffic, which is often a blind spot for perimeter-focused security tools, allowing the detection of lateral movement and insider threats.
• Automated Response Integration: While primarily focused on detection, many NDR solutions can integrate with Security Orchestration, Automation, and Response (SOAR) platforms, firewalls, or endpoint detection and response (EDR) tools to automate initial response actions, such as blocking malicious IPs, quarantining devices, or isolating compromised segments.

NDR complements existing security tools like firewalls, IDS/IPS, and SIEM by offering a layer of behavioral analysis that is crucial for detecting sophisticated, stealthy attacks. It provides security teams with rich forensic data and contextualized alerts, enabling faster and more effective incident response. The ability of NDR to ‘see’ and interpret network behavior is critical in an era where attackers constantly innovate to bypass traditional defenses (CyberProof, n.d.).

3.3 Security Service Edge (SSE) and Secure Access Service Edge (SASE)

The shift to cloud applications, mobile workforces, and edge computing has rendered traditional perimeter-centric security models increasingly obsolete. In response, Secure Access Service Edge (SASE) and its security component, Security Service Edge (SSE), have emerged as transformative network and security architectures.

SASE (Secure Access Service Edge), a term coined by Gartner, represents a convergence of wide area networking (WAN) and network security services into a single, cloud-delivered platform. It re-architects how security and network services are delivered, bringing them closer to the user or device, regardless of their location. The core idea is to provide secure, optimized access to applications and data for all users, everywhere, removing the reliance on backhauling traffic to a central data center for security inspection.

Key components typically integrated into a SASE framework include:

• Software-Defined Wide Area Network (SD-WAN): Optimizes network routing and traffic management for improved performance and reliability, especially for cloud applications.
• Firewall-as-a-Service (FWaaS): Delivers firewall capabilities as a cloud service, providing consistent security policies across all edges.
• Secure Web Gateway (SWG): Protects users from web-based threats by filtering malicious content, enforcing acceptable use policies, and preventing data loss.
• Cloud Access Security Broker (CASB): Provides visibility, compliance, data security, and threat protection for cloud applications, both sanctioned and unsanctioned.
• Zero Trust Network Access (ZTNA): Replaces traditional VPNs, providing granular, identity-centric access to specific applications and resources based on the principle of ‘never trust, always verify.’

SSE (Security Service Edge) is the security component of SASE. While SASE encompasses both network (SD-WAN) and security services, SSE specifically focuses on unifying the security functions (FWaaS, SWG, CASB, ZTNA, DLP, sandboxing, remote browser isolation). Organizations might first adopt SSE to consolidate their security stack in the cloud, and then later integrate SD-WAN capabilities to form a complete SASE solution.

Benefits of SSE/SASE for modern enterprises are substantial:

• Enhanced Security: Consistent security policies are applied to all users and devices, regardless of location, reducing the attack surface. Zero Trust principles are deeply embedded.
• Improved Performance: By moving security inspection to the cloud edge, traffic is not backhauled, leading to lower latency and better user experience, especially for cloud-based applications.
• Simplified Management: Consolidating multiple security and networking functions into a single platform reduces complexity and management overhead.
• Scalability and Agility: Cloud-native architecture allows for easy scaling to meet demand and rapid deployment of new services.
• Cost Reduction: Consolidating point solutions and reducing hardware footprints can lead to significant cost savings.

SSE and SASE are particularly relevant for organizations with distributed workforces, significant cloud adoption, and a need for consistent security enforcement across all access points. They represent a strategic move towards a more flexible, secure, and performant network security model fit for the digital future.

3.4 AI and Machine Learning in Network Security

The integration of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized network security, moving beyond static, signature-based detection to dynamic, adaptive, and predictive threat intelligence. AI/ML algorithms are uniquely positioned to process vast quantities of security data, identify subtle patterns, and automate responses at speeds far exceeding human capabilities.

Key applications of AI and ML in network security include:

• Advanced Threat Detection and Anomaly Identification: ML algorithms can establish baselines of normal network behavior (e.g., typical user logins, data access patterns, network traffic volumes, application usage). Any significant deviation from this baseline can be flagged as anomalous, indicating potential threats such as zero-day attacks, insider threats, advanced persistent threats (APTs), or unknown malware. This is particularly valuable in NDR solutions.
• Malware Detection and Classification: AI-powered engines can analyze file characteristics, behavioral patterns (sandboxing), and code structures to identify known and previously unseen malware more effectively than traditional antivirus. ML models can classify malware variants and identify polymorphic code.
• User and Entity Behavior Analytics (UEBA): UEBA tools leverage ML to analyze user activities (e.g., login times, access locations, data downloads, application usage) to detect unusual or risky behaviors that could indicate compromised accounts or insider threats. This is critical for detecting lateral movement post-breach.
• Network Traffic Analysis (NTA): ML algorithms can analyze network flow data to identify suspicious communication patterns, command-and-control (C2) traffic, data exfiltration attempts, and network scanning activities.
• Automated Vulnerability Management: AI can assist in prioritizing vulnerabilities by predicting which ones are most likely to be exploited based on contextual factors, attacker methodologies, and asset criticality. It can also help identify misconfigurations that create security gaps.
• Security Orchestration, Automation, and Response (SOAR) Enhancement: AI can augment SOAR platforms by intelligently analyzing alerts, suggesting response actions, and automating complex decision-making processes, thereby speeding up incident response.
• Fraud Detection: In financial networks, AI/ML models are highly effective at detecting fraudulent transactions by analyzing patterns of legitimate behavior and identifying deviations.
• Threat Intelligence Processing: AI can rapidly process and correlate vast amounts of global threat intelligence data, identifying emerging threats and informing proactive defense strategies.

Despite their immense potential, deploying AI/ML in network security presents challenges:

• Data Quality and Quantity: ML models require large datasets of high-quality, labeled data for effective training. Bias in training data can lead to skewed results.
• False Positives/Negatives: Overly aggressive models can generate too many false positives, overwhelming security analysts, while conservative models might miss actual threats.
• Interpretability (Explainable AI – XAI): Understanding why an AI made a particular decision can be challenging, which is crucial for forensic analysis and audit purposes.
• Adversarial AI: Malicious actors can attempt to fool or poison AI models to evade detection or generate false alerts.

Despite these challenges, AI and ML are rapidly becoming indispensable components of a comprehensive network security strategy, enabling organizations to move towards more predictive, proactive, and automated defenses against an increasingly intelligent adversary (Maric et al., 2025).

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Securing the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) refers to the intricate ecosystem of interconnected medical devices, healthcare IT systems, software applications, and services that collect, analyze, and transmit health-related data. This rapidly expanding domain includes everything from wearable fitness trackers and remote patient monitoring devices to hospital-based infusion pumps, MRI machines, and surgical robots. Securing the IoMT is not merely a matter of data privacy; it is paramount due to the profoundly sensitive nature of Protected Health Information (PHI) and the direct, life-threatening potential impact of device vulnerabilities on patient safety and clinical operations.

Unique challenges in securing IoMT include:

• Legacy Devices: Many medical devices have long operational lifecycles (10-15+ years) and were not designed with modern cybersecurity in mind. They often run outdated operating systems, lack robust security features, and cannot be easily patched or updated.
• Resource Constraints: Some IoMT devices have limited processing power, memory, or battery life, precluding the installation of heavy security software or strong encryption protocols.
• Proprietary Protocols and Closed Ecosystems: Devices often use proprietary communication protocols and are part of closed ecosystems, making integration with standard security tools difficult.
• Direct Patient Impact: A compromised infusion pump could alter dosages, a hacked pacemaker could be maliciously controlled, or a non-functional imaging device could delay critical diagnoses. The consequences extend beyond data breaches to physical harm and loss of life.
• Interoperability Requirements: Healthcare environments demand seamless communication between diverse devices and systems, creating complex attack surfaces.
• Compliance Complexity: Healthcare organizations must navigate a labyrinth of regulations, including HIPAA (US), GDPR (EU), and specific medical device regulations (e.g., FDA guidance).
• Limited Patching Windows: Medical devices often require specific certification and validation processes, making rapid patching difficult. Downtime for updates can impact patient care.

Best practices for effectively securing IoMT must be tailored to these unique constraints:

• Comprehensive Device Inventory and Lifecycle Management: Maintain an accurate, up-to-date inventory of all IoMT devices, including make, model, operating system, network configuration, and security posture. Implement secure design principles from procurement to decommissioning, including secure configuration baselines.
• Threat Modeling for IoMT: Conduct specific threat modeling exercises to identify potential attack vectors and vulnerabilities unique to each type of medical device and its operational context. Understand the clinical workflow implications of a cyber-attack.
• Robust Device Authentication and Authorization: Beyond basic credentials, implement strong authentication mechanisms for IoMT devices connecting to the network. This includes certificate-based authentication, unique device identities, and mutual authentication. Employ strict authorization policies to ensure devices only communicate with approved endpoints and access necessary resources.
• Advanced Data Encryption: Mandate strong, end-to-end encryption for all PHI, both in transit (using TLS/SSL for communications) and at rest on device storage or connected systems. Implement secure key management strategies to protect encryption keys.
• Continuous Vulnerability Management and Patching Strategy: Develop a structured program for identifying and addressing vulnerabilities. For devices that cannot be immediately patched, implement compensating controls such as virtual patching (using IPS to block known exploit attempts) or network isolation. Coordinate patching schedules with clinical teams to minimize disruption.
• Enhanced Network Segmentation and Micro-segmentation: Isolate IoMT devices on dedicated network segments, distinct from general IT networks and other clinical systems. Leverage micro-segmentation to create granular security zones around individual devices or groups of devices with similar security profiles, limiting lateral movement and containing potential breaches. Traffic between segments should pass through firewalls with strict access controls.
• Behavioral Analytics for IoMT: Implement solutions that monitor IoMT device behavior for anomalies. For example, an infusion pump suddenly attempting to access the internet or communicating with an unauthorized server would be flagged as suspicious. This helps detect zero-day exploits or compromised devices.
• Strict Access Controls for Maintenance and Remote Access: Implement multi-factor authentication for all remote access to IoMT devices. Use jump servers and privileged access management (PAM) for vendor access, logging all activities.
• Proactive Incident Response Planning specific to IoMT: Develop incident response plans that explicitly account for the clinical impact of IoMT device compromise. This requires close collaboration between IT security, clinical engineering, medical staff, and organizational leadership. Plans should include protocols for isolating affected devices, maintaining patient safety, and restoring services.
• Regulatory Compliance and Vendor Management: Ensure all IoMT deployments comply with relevant healthcare regulations. Collaborate closely with medical device manufacturers (MDMs) to understand their security roadmaps, vulnerabilities, and patching processes. Demand transparency and commitment to security in procurement contracts.

Securing the IoMT demands a multidisciplinary approach, blending traditional IT security expertise with deep understanding of clinical workflows, medical device functionality, and patient safety imperatives. It requires continuous vigilance and adaptation to the evolving threat landscape (NIST, n.d.).

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. Cloud Network Security Best Practices

As organizations increasingly migrate critical workloads, data, and applications to various cloud environments (IaaS, PaaS, SaaS), the responsibility for network security evolves from solely on-premises concerns to a shared responsibility model. Cloud network security necessitates distinct strategies and best practices tailored to the distributed, ephemeral, and programmable nature of cloud infrastructures.

The Shared Responsibility Model

This fundamental concept defines the security obligations between the cloud service provider (CSP) and the customer. Generally, the CSP is responsible for the ‘security of the cloud’ (e.g., physical infrastructure, hypervisor, underlying network), while the customer is responsible for the ‘security in the cloud’ (e.g., customer data, applications, operating systems, network configurations, access management). The exact demarcation varies depending on the service model (IaaS, PaaS, SaaS), but the customer always retains responsibility for their data.

Key cloud network security best practices include:

• Robust Identity and Access Management (IAM): This is arguably the most critical cloud security control. Implement strong cloud-native IAM policies, enforcing the principle of least privilege. This involves:

  • Multi-Factor Authentication (MFA): Mandatory for all users, especially those with administrative privileges.
  • Role-Based Access Control (RBAC): Define granular roles and assign permissions based on job function.
  • Just-in-Time Access: Granting elevated permissions only when needed and for a limited duration.
  • Conditional Access Policies: Dynamically granting or denying access based on context (user location, device posture, time of day).
  • Federated Identity: Integrating cloud IAM with enterprise directories for centralized management.
  • Service Accounts and API Keys: Securely managing and rotating credentials for non-human entities.

• Comprehensive Data Encryption: Utilize encryption to protect data at every stage within the cloud:

  • Data at Rest: Encrypt data stored in cloud storage (object storage, databases, virtual disks) using provider-managed keys or customer-managed keys (CMK) via Key Management Services (KMS).
  • Data in Transit: Ensure all data moving between cloud resources, on-premises environments, and users is encrypted using protocols like TLS/SSL for APIs and HTTPS for web traffic, or VPNs for inter-network communication.
  • Encryption Key Management: Securely manage encryption keys, considering options for key rotation, revocation, and robust access controls for KMS.

• Continuous Monitoring and Cloud Security Posture Management (CSPM): Implement solutions for real-time visibility into cloud environments:

  • Log Management: Centralize and analyze logs from all cloud services (CloudTrail, Azure Monitor, GCP Cloud Logging) using SIEM/SOAR platforms.
  • Cloud Security Posture Management (CSPM): Automatically detect misconfigurations, compliance violations, and security risks in cloud infrastructure. CSPM tools scan cloud environments against security best practices and regulatory frameworks.
  • Cloud Workload Protection Platforms (CWPP): Protect virtual machines, containers, serverless functions, and other cloud workloads from vulnerabilities and threats, often including runtime protection, vulnerability scanning, and host-based intrusion detection.

• Cloud-Native Network Segmentation (VPCs, Security Groups, Subnets): Leverage cloud provider capabilities for network segmentation:

  • Virtual Private Clouds (VPCs): Create logically isolated networks within the public cloud, allowing customers to define their IP address ranges, subnets, and routing tables.
  • Security Groups/Network Security Groups (NSGs): Act as virtual firewalls at the instance or network interface level, controlling inbound and outbound traffic based on rules.
  • Private Endpoints/Service Endpoints: Allow secure, private connectivity to cloud services without traversing the public internet.
  • Transit Gateways/VPC Peering: Securely connect multiple VPCs or on-premises networks.

• Cloud Access Security Brokers (CASB): CASBs act as a security policy enforcement point between cloud service consumers and cloud service providers. They provide:

  • Visibility: Discovering sanctioned and unsanctioned cloud applications.
  • Threat Protection: Identifying and blocking malware and risky behaviors.
  • Data Security: Enforcing DLP policies for sensitive data in cloud applications.
  • Compliance: Ensuring adherence to regulatory requirements for cloud data.

• Serverless and Container Security: Adopt specialized security practices for these modern cloud paradigms:

  • Container Security: Image scanning for vulnerabilities, runtime protection, least privilege for container processes, network segmentation for container orchestration platforms.
  • Serverless Security: Secure function configuration, minimal permissions for serverless functions, API gateway security, robust logging and monitoring.

• Infrastructure as Code (IaC) Security: Integrate security into the automated provisioning of cloud resources:

  • Secure Templates: Use vetted, hardened IaC templates (CloudFormation, Terraform, Azure Resource Manager).
  • Policy as Code: Embed security policies directly into IaC to ensure resources are provisioned securely by default.
  • Automated Scanning: Scan IaC for misconfigurations and vulnerabilities before deployment.

• Compliance Adherence and Auditability: Design cloud architectures and implement controls to meet relevant regulatory and industry standards (e.g., PCI DSS, HIPAA, ISO 27001, FedRAMP). Ensure comprehensive logging and audit trails are maintained for forensic analysis and compliance reporting.

By adopting these best practices, organizations can build secure, resilient, and compliant network infrastructures in the cloud, leveraging the agility and scalability that cloud environments offer while mitigating inherent security risks (Hays Communications, n.d.).

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Protecting Operational Technology (OT) in Healthcare Settings

Operational Technology (OT) encompasses hardware and software systems that detect or cause changes through the direct monitoring and control of physical devices, processes, and events. In healthcare settings, OT includes a vast array of critical systems such as building management systems (HVAC, lighting, physical access control), laboratory equipment, medical imaging devices (MRI, CT scanners), robotic surgical systems, medication dispensing units, and utility controls (power, water, oxygen supply). Protecting OT is profoundly critical, not only for maintaining the integrity of healthcare services but, more importantly, for ensuring patient safety and sustaining continuous clinical operations.

Traditionally, OT networks were air-gapped from IT networks and the internet, relying on physical isolation for security. However, the increasing convergence of IT and OT (IT/OT convergence) for efficiency, remote management, and data analytics has dissolved these air gaps, exposing vulnerable OT systems to new and significant cyber risks.

Unique characteristics and challenges of healthcare OT security include:

• Legacy Systems and Long Lifecycles: Many OT systems are designed for decades of operation and often run proprietary, outdated operating systems (e.g., Windows XP, embedded Linux variants) that are unsupported and unpatchable.
• Real-time Criticality: OT systems control physical processes where latency or disruption can have immediate, catastrophic consequences for patient care, facility operations, or even life support.
• Proprietary Protocols: OT often uses specialized, non-IP-based industrial control protocols (e.g., Modbus, DNP3, BACnet) that traditional IT security tools do not understand or monitor effectively.
• Limited Patching and Downtime Constraints: Patching OT systems is complex, often requiring extensive validation, vendor approval, and scheduled downtime that can impact patient services.
• Physical Safety as Priority: Any security measure must not interfere with the functionality or safety of medical equipment. A security update causing a ventilator to malfunction is unacceptable.
• Physical Security: OT devices are often physically accessible in clinical environments, requiring strong physical access controls.
• Vendor Dependence: Healthcare organizations are highly dependent on OT vendors for maintenance, support, and security updates, which can be slow or non-existent for older systems.

Strategies for effectively protecting OT in healthcare settings require a specialized approach, often differing significantly from traditional IT security:

• Deep Network Segmentation and Isolation: This is paramount. Physically and logically isolate OT networks from IT networks and the public internet. Use firewalls and data diodes (unidirectional gateways that allow data flow in only one direction) to strictly control any communication between IT and OT. Micro-segmentation within OT networks can further isolate critical assets.
• Strict Access Controls (Physical and Logical): Implement robust access controls for OT systems. This includes multi-factor authentication for all logical access, strict role-based access for technicians, and the use of jump servers for vendor remote access with session monitoring. Physical access to OT devices and control rooms must be tightly controlled and logged.
• Comprehensive OT Asset Inventory and Vulnerability Management: Develop a detailed, continuously updated inventory of all OT assets, including firmware versions, operating systems, and network connections. Conduct passive vulnerability assessments (non-intrusive) to identify flaws without disrupting operations. Implement virtual patching or network-based compensating controls where direct patching is impossible.
• Anomaly Detection and Behavioral Analytics for OT: Deploy specialized OT security solutions that understand industrial protocols and can establish baselines of normal OT device behavior. Monitor for unusual commands, unexpected network connections, unauthorized configuration changes, or abnormal operational parameters that could indicate a compromise.
• Whitelisting and Application Control: Instead of blacklisting known bad applications (which is challenging for legacy OT), implement whitelisting policies to only allow explicitly approved applications and processes to run on OT systems.
• Secure Remote Access: If remote access for maintenance is essential, use highly secured, audited channels (e.g., strong VPNs with MFA, jump servers, session recording) with time-bound access. Limit remote access to only necessary functions.
• Incident Response Planning for OT-Specific Incidents: Develop and regularly test incident response plans tailored to OT environments. These plans must involve not only IT security but also clinical engineering, medical staff, facilities management, and leadership. Emphasize patient safety, business continuity, and rapid recovery of clinical services.
• Supply Chain Security: Assess the security posture of OT vendors and ensure their products meet security requirements from procurement. This includes vulnerability disclosures, patch availability, and secure development practices.
• Employee Training and Awareness: Provide specialized cybersecurity training for clinical engineering teams, facilities staff, and medical personnel who interact with OT systems. Emphasize the unique risks associated with OT and the importance of secure practices.

Protecting healthcare OT requires a paradigm shift from traditional IT security, demanding deep domain knowledge of industrial control systems, medical device functionality, and a constant focus on safety and continuity of patient care. It’s a critical intersection of cybersecurity, physical security, and clinical operations (NSA, 2022).

Many thanks to our sponsor Esdebe who helped us prepare this research report.

7. Conclusion

The security of network infrastructures remains a dynamic, multifaceted challenge that demands a pervasive, comprehensive, and continuously adaptive approach. In an era characterized by an ever-expanding attack surface — driven by the widespread adoption of cloud computing, the proliferation of IoT and IoMT devices, the convergence of IT and OT systems, and the persistence of remote work models — organizations can no longer rely on static, perimeter-focused defenses. Instead, a holistic security posture, deeply embedded throughout the entire digital ecosystem, is an absolute imperative.

This paper has meticulously explored the critical components of such a posture, ranging from foundational best practices like rigorous vulnerability assessments, multi-layered firewall and IDS/IPS deployments, and strategic network segmentation, to the adoption of transformative advanced technologies. The implementation of Zero Trust Architecture stands out as a pivotal shift, advocating for continuous verification and least-privilege access regardless of location. Network Detection and Response (NDR) solutions provide vital behavioral analytics to uncover sophisticated, evasive threats, while Security Service Edge (SSE) and Secure Access Service Edge (SASE) redefine secure connectivity for the cloud-first, mobile enterprise. Furthermore, the strategic application of AI and Machine Learning is enhancing our capabilities for threat detection, anomaly identification, and automated response.

Crucially, specialized strategies are indispensable for securing highly sensitive domains: the Internet of Medical Things (IoMT), where patient safety is directly intertwined with cybersecurity, and Operational Technology (OT) within healthcare, where physical processes and critical services demand unique protection strategies that balance security with operational continuity and safety. Cloud network security best practices, anchored by the shared responsibility model, robust IAM, and continuous posture management, are essential for safeguarding digital assets in distributed environments.

In essence, network infrastructure security is not a destination but an ongoing journey. Organizations must cultivate a culture of continuous vigilance, proactively anticipating and adapting their security measures to address emerging vulnerabilities, evolving threat actor methodologies, and the increasing complexity of their own digital footprints. By diligently integrating established best practices with cutting-edge technologies and tailoring approaches to specific contexts like IoMT and OT, enterprises can build resilient, defensible network infrastructures that not only protect critical assets but also ensure the sustained integrity, confidentiality, and availability of data in the face of an ever-present and intelligent adversary. The future of network security lies in its intelligence, automation, and unwavering commitment to verification at every point of interaction.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

References

• Calyptix Security. (n.d.). Network Security 101: 8 Best Practices. Retrieved from https://www.calyptix.com/educational-resources/network-security-101-8-best-practices/
• CyberProof. (n.d.). Network Security Best Practices: Protect Your Organization. Retrieved from https://www.cyberproof.com/siem/network-security-best-practices-protecting-your-assets/
• Hays Communications. (n.d.). 10 Best Practices for Securing IT Infrastructure. Retrieved from https://www.hayscomm.com/10-best-practices-for-securing-your-it-infrastructure
• Maric, S., Baidar, R., Abbas, R., & Reisenfeld, S. (2025). System Security Framework for 5G Advanced /6G IoT Integrated Terrestrial Network-Non-Terrestrial Network (TN-NTN) with AI-Enabled Cloud Security. arXiv preprint arXiv:2508.05707. Retrieved from https://arxiv.org/abs/2508.05707
• Mushroom Networks. (n.d.). 10 Essential Network Security Best Practices for IT Leaders. Retrieved from https://www.mushroomnetworks.com/blog/network-security-best-practices/
• National Institute of Standards and Technology. (n.d.). NIST Cybersecurity Framework. Retrieved from https://en.wikipedia.org/wiki/NIST_Cybersecurity_Framework (Note: While referencing NIST, specific SP 800-207 for Zero Trust is referenced in the text for clarity, the general framework link is retained as per original request).
• National Security Agency. (2022). Network Infrastructure Security Guidance. Retrieved from https://www.nsa.gov/Press-Room/News-Highlights/Article/Article/2949885/nsa-details-network-infrastructure-best-practices/
• Sattrix. (2025). Top 6 Best Practices for Strong Network Security in 2025. Retrieved from https://www.sattrix.com/blog/strong-network-security-tips-2025/