AI for Network Security and Monitoring: Enhancing Cyber Defense

Keeping networks safe from threats like ransomware and other hacking attacks is a full-time job, and one that is getting increasingly difficult as the tech landscape evolves.

Company networks are now more sprawling than ever, encompassing on-premises data centers, cloud computing resources, Internet of Things (IoT) sensors and a wide range of corporate and personally-owned devices. As a result, businesses have bigger network footprints than ever, with even small and medium-sized companies having many endpoints that need protecting.

Hackers are keen to take advantage of this to infiltrate businesses, move laterally within networks to find the most sensitive or valuable data, and then exfiltrate it for use in extortion. It’s therefore important for cybersecurity pros to be able to monitor this effectively – and this is where using technology like AI for network security and monitoring can prove useful.

Using the latest technology will be vital as networks continue to grow and hackers develop new ways of targeting businesses. Network perimeters have been expanding rapidly over the last few years, driven by improved access to high-speed fiber, 5G mobile connectivity and the growth of IoT. Indeed, there were projected to be almost 19 million IoT devices in use worldwide at the end of 2024, which can create new vulnerabilities for cybercriminals to exploit.

At the same time, the techniques used by criminals are also evolving. AI-driven attacks can travel quickly within networks, seeking out the most valuable data to exfiltrate while remaining undetected from threat hunting tools. Meanwhile, social engineering techniques such as phishing have also become more sophisticated, which makes it easier than ever for hackers to obtain genuine network credentials. These can then be used to access confidential files and databases to do damage or steal information.

These developments have made cyberattacks more dangerous and frequent, leading to unprecedented financial and operational costs. For example, one study from the World Economic Forum found that almost half of businesses (47 per cent) say adversarial advances powered by Generative AI (GenAI) are a key concern for 2025, while 72 percent reported seeing a rise in cyber risks.

AI offers a range of opportunities to improve network security, such as:

• Comprehensive network monitoring
• Real-time visibility into data
• Faster anomaly detection
• Instant alerts of any potential threats

Traditional security tools rely on predefined rules and signatures to identify threats within a network. This makes them less effective against evolving cyberthreats – particularly if hackers are using their own AI-powered malware to constantly adapt and evolve to avoid detection.

Network traffic analysis with AI, however, continuously examines patterns of activity, learning what constitutes normal behavior and flagging deviations that could indicate malicious activity. This may include unauthorized access attempts, unusual lateral movement within the network, or attempts to exfiltrate data.

Machine learning and predictive analytics enable businesses to shift from a reactive approach to security, where they can only respond to incidents after they occur, to proactive defense. Instead of waiting for a breach to trigger an alert, AI-powered systems engage in active threat hunting, looking for warning signs of a data breach and taking preventive measures. This could mean automatically isolating compromised devices, blocking suspicious IP addresses, or alerting security teams before an attack spreads.

Deploying AI-driven network monitoring to improve threat detection can offer businesses a range of advantages that ensure they stand the very best chance of defending against cybercriminals. Here are three that all firms should be able to take advantage of.

AI threat detection continuously scans network traffic, analyzing patterns and detecting anomalies in real-time. By leveraging predictive analytics, AI identifies unusual behaviors as they occur before they escalate into serious security incidents. This real-time approach enables businesses to respond immediately, reducing dwell time for attackers and preventing potential breaches before they cause significant damage. Faster detection translates into stronger defenses and minimized risk exposure.

Traditional security tools can generate a high volume of false positives. This causes a number of issues. For starters, it can disrupt genuine day-to-day business activities by blocking actions until they can be reviewed by the security team. However, it can also lead to ‘alert fatigue’, which means security pros start to take notifications less seriously, under the assumption it is likely to be a false positive. This can lead to legitimate threats being ignored until it is too late.

AI and machine learning in network security improves threat detection by learning from past incidents and distinguishing between legitimate network activity – including normal fluctuations – and actual suspicious behavior.

As organizations expand, so do their networks. This makes manual monitoring increasingly impractical and can lead to emerging security threats being missed as more endpoints are added – sometimes without the IT department’s knowledge. AI-driven security solutions are able to scale up easily as a business grows, adapting to ever-increasing data volumes and diverse IT infrastructures. 

AI is also able to process these very large datasets at speed, identifying risks without compromising system performance as it monitors cloud environments and keeps an eye on remote workforces without the need for human intervention. This scalability ensures that businesses of all sizes can maintain robust security, regardless of network complexity or traffic volume.

Adding AI capabilities to network security will become essential for any business that wishes to keep up with ever-evolving cyberthreats. However, doing this effectively requires more than just deploying AI-powered tools. Companies will need to take a careful, well-planned approach to ensure AI is seamlessly incorporated into operations without causing disruption. 

There also need to be clear plans for how it will be kept updated, as well as an understanding of the role human expertise will need to play in managing and directing the tools. The following best practices outline how businesses can successfully leverage AI for network security.

For AI to be effective, it must be able to work seamlessly with an organization’s existing security infrastructure. AI-driven solutions should be able to integrate and operate alongside a range of traditional security tools, including with SIEM (Security Information and Event Management) systems, firewalls, endpoint protection and network monitoring tools. 

It’s important for businesses to prioritize AI solutions that can analyze data from multiple sources, as threats often include multiple attack vectors across a network. Therefore, tools that have compatibility with all these systems will be a must. What’s more, organizations must ensure that AI deployments align with the business’ predefined security policies and compliance requirements to avoid falling foul of any data handling regulations, such as GDPR.

Cyberthreats evolve rapidly, with attackers constantly devising new tactics to evade detection. To counter this, AI-powered security tools must be designed for continuous evolution, using machine learning models that can adapt to emerging threats. This will mean keeping the AI up-to-date by feeding it with real-time data and information on new attack methods in order to refine its detection capabilities, and incorporate the latest threat intelligence updates. 

By regularly training their AI models with new attack patterns, organizations can ensure they remain effective against some of the most sophisticated cyberthreats, such as zero-day exploits and fileless attacks, which are a factor in 77 percent of successful attacks. By prioritizing continuous learning, businesses can maintain an AI-driven security system that stays ahead of adversaries.

It’s important to remember that while AI enhances system monitoring and can make threat detection and response faster, it is not a replacement for human expertise. Security teams will still need to play a crucial role in interpreting AI-driven insights, investigating flagged anomalies, and making strategic decisions. 

A major benefit of AI is its ability to automate repetitive tasks, such as filtering alerts and triaging threat assessments. Being able to leave these tasks to the AI, safe in the knowledge that intelligent machine learning is constantly keeping an eye on the network, frees up time for security professionals to focus on higher-level analysis. 

This collaborative approach, where AI is being used to enhance human judgment and not replace it, ensures a balanced cybersecurity strategy that combines a machine’s efficiency with the critical thinking skills that only humans can provide.

While the benefits of turning to AI for network security and monitoring are clear, there are a few important challenges that must be considered if the technology is to be implemented successfully. Failing to address these issues could result in firms falling foul of regulations, or the technology not meeting its full potential.

• Data privacy: AI security tools require access to vast amounts of data, including sensitive user information. This raises concerns about compliance with regulations such as GDPR and CCPA, as improper handling of data could lead to regulatory fines and reputational risks. Organizations must implement strict data governance policies to ensure that AI-driven security systems process information ethically.
• The need for quality data: AI is only as good as the data it is trained on. Inaccurate, incomplete, or biased datasets can lead to poor threat detection, false positives, or missed attacks. To mitigate this, businesses must conduct strong validation processes, regular updates and continuous monitoring for all data that will be analyzed by the tools.
• An evolving landscape: Cybercriminals are constantly refining their tactics, using AI to develop more sophisticated attacks. AI-driven security tools must therefore be regularly updated to avoid becoming obsolete in the face of new dangers. 

Ultimately, AI is set to have a transformative effect on the cybersecurity space for both hackers and businesses. It’s not a development any firm can afford to ignore, but by planning carefully and choosing the right tools, firms stand the best possible chance of enhancing their network security posture and successfully defending against the new generation of AI-driven attacks.