AI Network Security: 6 Real-life Use Cases in 2025

Network security statistics show that ~90% of organizations have reported at least one cyber incident. AI-driven network security software can support security teams by utilizing strategies such as network segmentation, AI-SPM (AI-security posture management), or data security posture management.

However, a common cybersecurity issue is that vendors typically engage in AI washing to sell existing solutions as AI-enabled solutions. Here are the top 6 AI network security use cases with real-life examples to help you assess their authenticity:

1. Network microsegmentation

Artificial intelligence (AI) can improve DMZ network security by enhancing microsegmentation or network segmentation accuracy, particularly in large and complex networks.

AI/ML in microsegmentation tools can help companies with:

• Automated identity-based recommendations: Make recommendations for multidimensional identity. For example, AI can suggest assigning a certain role(s) to a particular user in a department such as a bookkeeping assistant so that the user will only access files restricted by the bookkeeping assistant role.

• Higher level of user identification: Increase the amount of grouping and membership (administrators, end-users or readers, etc.). For example, AI can generate attribute-based access control to specify which roles should be permitted access to particular devices.

• Enhanced zero trust security: Track changes to the identification over time to ensure the completeness and accuracy of a zero trust segmentation policy. For example, security teams can receive notification if a user tries to gain access to resources on the network.

Read more: Microsegmentation tools, network segmentation examples, microsegmentation examples.

Real-life example: AI-powered network microsegmentation

Case study of a multibillion-dollar manufacturing and transportation business.

Problems:

• Separated IT infrastructure: After separating a manufacturing and transportation division from its parent company, IT needed to be segmented across the common infrastructure.

• Complex network: The manufacturer’s infrastructure was diverse, with hundreds of servers across several locations. Engineers had to configure, test, and debug in three distinct regions. This made the system more error-prone and insecure.

Solutions and outcome: The manufacturer used a microsegmentation technique to segment IT communications between the two organizations.

• Behavior-based artificial intelligence (AI): The company used a behavior-based approach—this improved segmentation accuracy and minimized labor for IT and security teams through automation. According to the company the new tool reduced costs by 30% more than their prior solution.

• Automated network security policy management: The organization leveraged automation over network security setups. For example, the automation capabilities enabled IT specialists to execute centralized security policies, allowing them to control who has access to the system and what they can do with it.

• Enhanced network visibility: The manufacturer gained network visibility into application communications. This helped engineers respond quickly to threats without constantly improving the rules.¹

2. Role-based access control (RBAC) 

A Forrester study reveals that AI models may enhance security by calculating the risk of each login attempt and validating users using behavioral data, decreasing fraud costs by up to 90%.² Organizations can leverage several network security practices with AI in their network security practices:

• AI role-based policy suggestions: AI in role-based access control tools can help companies adopt changing identity and role changes during network segmentation. AI in RBAC systems can recognize changes in their network and make suggestions to maintain policies in sync.
  
  For example, AI can suggest administrators deny access of sales representatives in Riverside to Orange County sales data, describing who has access to it and what actions may be taken with it.
  
  Read more: Network security policy management solutions, NCCM software.

• Network traffic analytics with AI bots: AI in RBAC systems gleans insights into what’s happening in your network at any given time based on user identity. Users can ask precise questions about particular parts of your network traffic in natural language.
  
  For example, assume an administrator creates scopes that correlate to REST APIs. Users may ask the AI bot a basic query like “What are the access privileges of “store_admin”? and promptly obtain a detailed response stating that. “store_admin” responsibilities have the authority to:
  
  • create:categories
  • delete:categories
  • list:categories

Read more: RBAC examples, RBAC use cases.

Real-life example: AI-powered RBAC

Case study of an American bank, ranked in the Fortune 1000, with over 800 full-service locations.

Problems: With the legacy access management system, the bank sought role-based access to address a poor security posture caused by unmanaged privilege controls and complex permissions.

• Complex network: The bank experienced non-compliance for segmenting 360+ apps and 10,000+ underlying IT assets.

• Low level of network controls: Admin-privileged access was not approved or matched with the organization’s established roles.

Solutions and outcome: The bank implemented an agile-based RBAC provisioning, Developing a framework to incorporate identity and access management (IAM) controls for integrations.: The bank created an automated framework to conduct static address translation (SAT) for RBAC implementation.

• Automated RBAC management: The organization stated that following the bulk role-based access control solution, they can reliably handle 360+ apps.

• Faster access controls: RBAC implementation resulted in a 40% decrease in the timeframe.

• Stronger compliance: The company ensured IAM compliance and control over 10,000+ Sarbanes-Oxley Act (SOX)  and non-SOX IT assets.³

3. Network detection and response 

Detecting fraudulent behavior is crucial for avoiding costs and since over half of polled firms have reported being victims of fraud, resulting in losses of over $42 billion.⁴

Network detection and response  (NDR) focuses on detecting unusual behaviors within the network that may indicate an ongoing or emerging threat.

Key use cases of AI in NDR:

• Malware and phishing detection: AI-based network security systems analyze email content and context to differentiate between spam, phishing attempts, and legitimate messages.

• Security log analysis: AI utilizes machine learning to analyze massive amounts of security log data, detecting patterns and anomalies without relying on known threat signatures.

• Automated incident response: AI can automate responses to certain network incidents, reducing the time to contain threats. For example, it can quarantine infected endpoints or block malicious communication.

Real-life example: AI-powered network detection and response

Case study of the City of Las Vegas.

Problems: Las Vegas faced external and internal risks to its private data and taxpayer information. The CIO of the City of Las Vegas stated that by using AI we can detect and respond to email-borne threats, cloud-based attacks, and novel strains of malware that other tools miss.

• Lack of AI: The City’s traditional security solutions relied on predefined criteria and signatures to identify threats, limiting their ability to detect new ones. The City needed a security solution that could adapt to constantly changing threats.

• Limited internal network visibility: Las Vegas had a multi-cloud architecture that includes AWS, Microsoft Azure, and Office 365, and securing these services lacks vital context. The city’s security personnel struggled to detect attacks due to outdated tools and limited internal visibility

Solutions and outcome:

• Mitigated a spear phishing attack: The city eliminated the attackers who accessed the city’s contact book and sent seemingly innocent emails with malicious payloads.

• Enhanced threat response with AI: The network AI features analyzed data flows from throughout the city’s digital infrastructure in real-time, allowing the software to respond to neutralize cyber-attacks.⁵

4. Network monitoring

According to IDC’s survey, ~35% of business and IT leaders believe that enhancing network monitoring will be the most significant network management enhancement driven by machine learning and artificial intelligence algorithms.⁶

Here are three major use cases where AI can be used to manage and monitor networks:

• Automated network traffic analysis: AI in network audit can gather data on current application traffic flows and then notify of any anomalies that might signal security issues such as illicit access or malware.

• Monitoring human-caused network outages: AI can be used to prevent human-caused network incidents. For example, AI can correct switch command syntax that IT users typically set using command-line syntax. 

• Automated policy orchestration: By monitoring the network traffic, user behavior, and application activity, AI can ensure that policies are consistently applied and up-to-date, reducing the risk of misconfiguration or non-compliance.
  

Real-life example: AI-powered network monitoring

Case study of a large university headquartered in the UK that employs over 3,500 people.

Problems: The University has a campus-wide WAN comprising 38 routers. The campus network served as the primary communication channel for its diversified population. If the institute’s WAN is down for an extended period, it may disrupt the university system. The main challenges included:

• Lack of network monitoring: The University needed to monitor network traffic patterns within the firewalled perimeter.

• Ineffective incident detection: The University could not identify network malfunctions quickly.

• Inaccurate predictive forecasts: The forecast accuracy of future network traffic was low.

Solutions and outcome: A network monitoring tool with network traffic flow analysis capabilities was installed and activated on all 38 routers. Network security analytics were imported from the older equipment to the new tool.

• Accurate bandwidth calculation: AI-powered insights provided network visibility into network traffic patterns and resource utilization, assisting the university team in allocating resources for better network performance. This helped the organization calculate bandwidth.

• Automated troubleshooting: Real-time  AI monitoring and analysis helped the university to identify anomalies from typical network activity. The solution effectively filtered unusual logs.⁷

5. Security information and event management (SIEM)

Security information and event management (SIEM) with AI capabilities can automate complicated data collection and normalization. For example, AI SIEM may forecast and detect possible problems before they occur by learning from previous security data and trends.

Real-life example: AI-powered SIEM

Case study of Sutherland Global Services, a content streaming service firm helping companies build a language-translation pilot. 

Problems: Sutherland Global Service aimed to create more accurate digital process optimization and required a tool to accelerate its processes for detecting and eliminating cybersecurity threats.

• Manual threat detection: The company used manual threat detection, and human skills to detect and respond to cybersecurity incidents. While manual threat detection allowed for some flexibility and human intuition, it was limited in scalability, speed, and consistency.

• High usage of sensitive data: The sensitive data spanned the company’s network infrastructure. The enormous diversity of this data hindered manual processing operations, resulting in severe downstream delays. 

Solutions and outcome: Sutherland Global Services enhanced its security procedures and technologies with advanced threat detection and response capabilities.

• Security information and event management (SIEM): The company employed SIEM to detect zero-day attacks that traditional rule-based approaches may skip over. This helped the security team to outline network traffic, security events, and AI-based anomaly detection.

• Data aggregation: The SIEM system leveraged AI to help normalize raw data irrespective of the source. AI and machine learning considerably automate these procedures, increasing the speed and intelligence with which security data is normalized, thus decreasing manual work and time.

• Incident response automation: The company generated 200+ alert automation.

• Faster threat detection: The company reduced the mean time to detect (MTTD) from days or weeks to hours.⁸

6. Predictive maintenance

Companies can leverage predictive maintenance in network security to evaluate all of the data points that suggest future outages, malfunctions, or bottlenecks before they occur. 

Real life predictive maintenance use cases in network security:

• Predicting network hardware failures: Identify signs of hardware degradation or potential failure in network equipment, such as routers, switches, and firewalls, before it happens.

• Predicting bandwidth bottlenecks: By analyzing traffic patterns and system load, AI can predict potential bandwidth bottlenecks in the network.

• Predicting security device malfunctions: Analyzing the performance of security devices, like firewalls, or endpoint security solutions, and predicting when a malfunction might compromise network security.

Real-life example: Predictive maintenance  

Case study of a Mobile Broadband Network Ltd (MBNL), is a mobile telecom operator that operates a communications network for two carriers with 20,000+ terminals.

Problems:

• Lack of machine learning-based approaches: MBNL had an abundance of structured data on equipment performance, considerably too much to compress using typical statistical approaches.

• Lack of structural incident response: Network incident records were frequently written in open-ended language, making systematic analysis extremely challenging.

Solutions and outcome: MBNL partnered with an AI company that has created a unique data science platform that simplifies data and algorithm management. The company leverages predictive maintenance to use:

• Improved prediction accuracy: NLP extracted failure reasons from ticket data, predicting fan or air conditioning failures with ~20% accuracy for the upcoming quarter.

• Enhanced forecast accuracy: 50,000+ machine learning iterations and smart meter data improved forecasting accuracy for equipment failures and maintenance needs.

• Proactive maintenance: Shifted to proactive maintenance, reducing unplanned outages, minimizing downtime, and improving resource efficiency.⁹

Key network security software to strengthen your cybersecurity posture

• Network security audit tools: Identify risks, vulnerabilities, and malicious behavior to assist businesses in mitigating cyber attacks.
• DSPM vendors: Deliver network visibility into sensitive data, access levels, and usage across the cloud.
• Network security policy management solutions (NSPM): Develop, implement, and maintain policies and procedures to safeguard an organization’s network.
• SDP software: Deliver a software-defined perimeter (SDP) across the cloud to control who has access to which resources.
• Open source RBAC tools: Assign users access permissions and roles based on their authorization level within a company.
• Open source microsegmentation tools: Enable and enforce network communication policies at the API layer.
• Network traffic analyzers (NTA): Collect and analyze network flow data to provide information on traffic volume, type, and network device performance. 
• Network performance monitoring tools: Monitor network performance to identify and resolve issues that affect the network’s overall performance.

Benefits of AI in network security

AI is already used in security, and its importance will grow. Some of the advantages of AI for security include the following:

• Improved threat detection and response: AI can process large volumes of data and perform in-depth analysis, which significantly enhances an organization’s ability to detect and respond to cyber threats more quickly.

• Automation of repetitive tasks: Cybersecurity requires a significant amount of data collection, analysis, system management, and other repetitive tasks that drain analysts’ time and resources. AI can automate these tasks, allowing security specialists to direct their efforts where they are most needed.

• Improved situational decision-making: Security leaders frequently experience data overload, meaning they have more information than they can effectively process and use. AI insights can help security leaders improve their situational awareness and data-driven decision-making.

Challenges of using AI in network security

• Lack of transparency: AI systems are typically trained as “black boxes” by providing them with data and allowing them to create their models. The lack of transparency, and extracting information about how the AI system renders decisions is difficult. Thus, security users may not easily learn from or correct the model.

• Integration with current systems: AI systems have the potential to improve security operations, but they work best when they are integrated into an organization’s security architecture. If AI-powered solutions do not integrate well with an organization’s other tools, which is a common issue, they will have limited value to the organization.

Further reading

• Role-based Access Control (RBAC)  
• Network Segmentation: 6 Benefits & 8 Best Practices
• 80+ Network Security Statistics

AIMultiple can assist your organization in finding the right vendor.

External Links

• 1. ”Top Manufacturer Finds Superior Microsegmentation Solution“. TRUEFORT. 2024. Retrieved June 24, 2024.
• 2. Banking firm automating RBAC .
• 3. Banking firm automating RBAC .
• 4. Global Economic Crime Survey 2024 | PwC .
• 5. ”City of Las Vegas“. DARKTRACE. 2022. Retrieved June 24, 2024.
• 6. IDC FutureScape: Worldwide Future of Connectedness 2024 Predictions.
• 7. University Campus Network Monitoring with NetFlow Analyzer: Case Study .
• 8. Sutherland Global Services | IBM.
• 9. Predictive Maintenance using AI - Case Study for MBNL.