There is an increased focus on how advances in artificial intelligence (AI) and machine learning (ML) can negatively impact network security. Security Magazine reported last year that 75% of security professionals saw an increase in attacks over the past 12 months, and 85% of those attributed the rise to cybercriminals using generative AI. But the truth is, while cybercriminals are most certainly using AI and ML, these technologies are also revolutionizing network engineering, leading to enhanced networks that can detect, prevent and respond to threats in real time.

Through deep learning models focused on large datasets, today’s AI systems can autonomously discern patterns that indicate cyberthreats within network traffic. ML algorithms employ techniques like anomaly detection and behavioral analysis and dynamically adapt to evolving attack vectors, improving detection accuracy and reducing response time. The effectiveness of AI-based security measures, however, hinges on companies addressing common challenges associated with incorporating advanced technologies into a security system.

How AI and ML are Impacting Network Security

AI and ML technologies offer superior performance in threat detection and mitigation compared to traditional network security measures. While traditional network security systems rely on predefined rules and signatures to spot known threats, AI and ML can detect unknown and evolving threats much faster and more thoroughly by analyzing network traffic patterns and anomalies, user behavior and log files. This ability to quickly process available information means faster response times to new attacks and greater flexibility and resistance against sophisticated attacks, such as zero-day attacks, advanced persistent threats (APTs) and polymorphic malware. In a cybersecurity article released earlier this year, Enterprise Apps Today reported that 60% of companies report better cybersecurity performance as a direct result of AI usage. In that same report, company executives said that AI reduced their security costs by an average of 12%.

AI and ML Detect a Wide Range of Cyberthreat Patterns

ML’s deep learning models are crucial in analyzing complex and high-dimensional datasets that improve the accuracy and efficiency of threat detection. These models identify subtle anomalies and sophisticated attack patterns that traditional security methods might miss. This ability has shifted companies’ security approach from reactive to proactive, allowing security systems to predict and mitigate threats before they cause harm. Among the patterns that AI and ML excel at detecting are:

• Anomalous user behavior, or deviations from regular user activity
• Unusual network traffic, such as unexplained spikes or irregular traffic patterns
• Malware signatures, or identifiable patterns in malicious code
• Phishing indicators, which identify potential phishing emails and dangerous websites
• Intrusion attempts, including suspicious access efforts and exploitation patterns.

The benefits of this improved cyberthreat pattern recognition include enhanced attack prevention, reduced human error and system automation that speeds up response time and frees staff to work on other essential tasks. To continue receiving these benefits long-term, companies need to guard against “model drift.” This is a general decay in model performance that occurs over time due to changes in data or in the relationship between input and output variables. The best ways to prevent model drift include consistent system monitoring, regular model retraining, implementation of adaptive learning techniques and feedback loops from security analysts.

Challenges to Unlocking AI and ML’s Many Benefits

The effectiveness of AI-based security measures hinges on companies recognizing and addressing five specific challenges that typically arise when AI and ML are leveraged in network security:

1. Data quality. Data quality is one of the biggest challenges to optimizing AI and ML usage. These technologies will only perform as well as the quality of the datasets on which they are based. For that reason, companies must take steps to ensure the highest quality data by carefully defining data quality and then establishing well-defined data governance. Additional steps include using data quality metrics, regularly conducting data quality checks and being consistent with data cleansing.
2. Integration difficulties. Integrating AI and ML tools with existing systems and workflows can often create problems limiting performance. One option is to use modular solutions that can be more easily arranged and adapted to meet different needs.
3. Lack of expertise. Unfamiliarity with AI and ML can lead to poor performance. Solutions include providing comprehensive training for existing staff, hiring experienced technology users, and arranging consultations with AI and ML experts.
4. High cost. Although implementing AI and ML solutions can be costly, they deliver financial savings that can justify their initial expense. These savings include task automation, data breach prevention, real-time threat response and expensive emergency IT service avoidance.
5. Ongoing research. In a world of rapidly evolving cyberthreats, staying updated through continuous research leads to the development of new algorithms and models that can more effectively detect emerging threats.

AI and ML are Ushering in a Paradigm Shift in Network Security

George Berg, associate professor and former chair of the information security department at the State University of New York at Albany, recently told Fortune magazine: “AI is a wonderful tool for defenders.” AI-powered indicators of attack (IOAs) are proving effective at identifying threats while ignoring a lot of the other “noise” that can come with network traffic.   This is a best-of-both-worlds scenario delivered at a time when the average cost of a data breach is $4.45 million globally and $9.48 million in the United States. According to IBM, companies that overcome challenges and take maximum advantage of AI and ML can save an average of $1.76 million and detect breaches 249 days faster than companies that do not utilize these tools.

To make the most of this new technology, companies need to commit to continuous monitoring and updating of their AI and ML models and provide staff with in-depth training. It’s also critical for companies to be aware of the additional concerns generated through AI use, such as privacy, bias and fairness, job displacement and accountability. Companies that address these issues early and continue to update and refine their responses to technology advances will set themselves up to successfully leverage AI and ML in network security, reaping big benefits like advanced threat detection, automated attack mitigation, enhanced user authentication and improved incident response.