How Do AI and Machine Learning Improve Network Security?

In today’s world, most facets of our lives revolve around networks, from cloud storage environments to Wi-Fi-enabled security systems. Our increasing reliance on networks makes network security even more important. Network security includes all the tools and technologies an organization has in place to protect its network from unauthorized access, misuse, or attacks.

As networks expand, so do the challenges surrounding network security. Organizations are tasked with protecting increasingly complex networks and increasingly complex threats. According to the Hybrid Cloud Security Report, breach rates are up 17 percent year-over-year.

To address these complexities and provide comprehensive network protection, many organizations are turning to network security with AI. These technologies are transforming network security with artificial intelligence and machine learning, enabling real-time threat detection, automated response, and rapid adaptation.

Interested in learning more about AI network security? Read on to learn all about its benefits, challenges, and our favorite tools for incorporating AI into your network security protocol.

• What Is Network Security?
• The Role of AI and ML in Network Security
• Key Benefits of AI and ML for Network Security
• AI and ML in Network Security Monitoring
• Challenges and Considerations
• Conclusion

Key Takeaways

• Network security protects organizations from unauthorized access, data breaches, and network attacks using a layered approach
• Network security with AI enables real-time threat detection, proactive risk mitigation, significant scalability, and other benefits
• Organizations should consider common challenges when switching to AI network security, like the importance of data quality, difficulties around integrating with legacy software, and the needs for training and personnel

What Is Network Security?

Network security refers to the practices, procedures, and systems an organization uses to secure and protect its network, including its hardware, software, and data. With the help of network security, organizations can safeguard their network from:

• Unauthorized access: Malware, phishing attempts, or security breaches from within an organization
• Data breaches: Annexing of sensitive company or customer data
• Network attacks: DDoS and other attacks that overwhelm a network with the goal of making it unavailable to those who need it

Layered approaches are the most effective approaches to network security. These layers include:

• Physical network security: The first layer of network security is controlling who has physical access to the network. This includes security measures like placing locks on rooms that store computers and physical hard drives and implementing biometric verification like fingerprint scanners to allow access to the system. While many organizations have transitioned to cloud environments, physical endpoints like laptops and mobile devices still need to be secured.
• Administrative network security: Administrative network security protects an organization from internal network misuse, whether intentional or unintentional. This may include role-based access control, Zero Trust architecture, and training policies that dictate proper and improper use of the network.
• Technical network security: This layer of network security protects the network from unauthorized access and malicious activity. It may include data encryption and AI network security practices that monitor and detect threatening behaviors.

The Role of AI and ML in Network Security

Traditional network security relied on fixed rules to identify and prevent threats, like firewalls and antivirus software. These security measures tend to be quite rigid and result in a high number of false alerts.

Today’s network security is enhanced by the use of AI and machine learning technologies such as the Gigamon Deep Observability Pipeline, which enables AI-ready visibility across hybrid infrastructure.

AI for network security and monitoring uses algorithms to analyze network traffic and identify anomalies far more quickly than humans. These models ingest huge amounts of data and information in seconds to make real-time decisions.

Thanks to machine learning, AI network security models are much more flexible than traditional software. AI algorithms can make judgment calls and learn from past experiences to more accurately detect threats over time.

Key Benefits of AI and ML for Network Security

The benefits of network security solutions powered by AI are vast. Read on to learn how AI and machine learning can improve your network security systems.

Real-Time Threat Detection and Response

AI works much more quickly than humans or traditional network security software. AI algorithms can analyze data and detect anomalies nearly instantaneously, preventing malware and phishing attacks before they have time to do damage. AI is also capable of detecting subtle nuances that are undetectable by the human eye.

Then, AI network security can automatically respond to the incident by isolating infected devices or blocking malware, phishing attacks, or zero-day threats, cyberattacks that exploit a vulnerability that was previously unknown and for which no procedure exists. In the event of a multi-faceted attack, AI can prioritize threats and respond accordingly.

Anomaly Detection and Behavioral Analytics

AI network security monitoring practices operate based on pattern recognition. As algorithms ingest data, they create an in-depth understanding of “normal” network behavior. When traffic comes across the network that falls beyond those patterns, AI will instantly flag the anomaly. This minimizes false positives that are common with traditional, signature-based systems.

Predictive Analytics and Threat Intelligence

AI network security solutions look to two places to forecast threats: historical data and global threat intelligence. Machine learning models are trained on significant amounts of data, including past cyberattacks. They retain a memory of all historical data and behavior from both their training model and active security monitoring.

They also integrate with global threat intelligence systems, which are databases of known malicious behavior, including malware and TTPs. These two sources of information enable proactive and highly accurate risk mitigation.

Scalability and Efficiency

AI network security solutions have lots to offer in terms of scalability. They can handle large-scale, high-volume data environments with ease. They can also reduce the manual workload on network security staff, freeing them up for more important tasks.

AI and ML in Network Security Monitoring

AI and machine learning are changing how organizations detect and respond to network security threats. They enhance network security monitoring tools by:

• Correlating data: Traditional network security software was limited in terms of its data correlation. AI network security software can correlate diverse data sets from a range of different sources, like endpoints including laptops and servers, user behavioral patterns and access logs, cloud environment traffic logs and workloads, and IoT sensor data and device logs. This enables the identification of data that may not be otherwise visible.
• Identifying advanced persistent threats (APTs): APTs present another shortcoming in traditional network security systems. AI network security monitoring software can detect subtle behaviors and lateral movement that help identify these types of attacks.
• Integration with SIEM and SOAR platforms: AI in SIEM can help to prioritize alerts and triage attacks more quickly, while AI in SOAR can make nuanced incident response decisions.

Challenges and Considerations

There are some challenges to consider when switching from traditional network security software to an AI-powered network security solution. These include:

• Data quality and bias in machine learning models: Training data can have a significant impact on the accuracy of AI and machine learning models. Models trained on poor-quality data can result in bias, false positives, and inaccuracies. It’s vital to use diverse and up-to-date data sets for accurate threat detection.
• Adversarial AI and evasion techniques by attackers: When it comes to network security, AI can present a double-edged sword. Just as it can be used to develop advanced measures to secure networks, it can be used to make advanced network attacks, too. Bad actors can use AI to mimic legitimate behavior, exploit network vulnerabilities, and create harder-to-detect malware.
• Integration challenges with legacy network security software: For organizations with fragmented or outdated legacy security practices, integrating with AI network security solutions can be a challenge.
• Need for skilled personnel to manage AI-driven systems: AI systems require a skilled workforce to manage them, including security analysts, engineers, and data scientists. There also needs to be ongoing training in place to stay up to date on new tools and technologies.

Conclusion

AI and machine learning are enabling organizations to better detect and respond to network security threats for faster responses and less downtime. 

In our rapidly evolving world, modern network solutions are essential for resilience. Learn more about the Gigamon Deep Observability Pipeline, a superior approach to enabling intelligent, AI-ready network visibility.

CONTINUE THE DISCUSSION

People are talking about this in the Gigamon Community’s AI Exchange group.

Share your thoughts today