COMMENTARY: Artificial intelligence has broken out of sci-fi territory for good – and it’s here to stay. While many people equate AI with its mainstream generative side, this umbrella tech offers more applications that hold promise across various sectors.
One industry AI will help improve includes cybersecurity, where a global talent shortage and limited human “bandwidth” in analyzing large datasets have become very challenging. With its capabilities to process diverse information clusters and automate workflows, AI promises to fill the void.
From an organizational perspective, network security stands at the forefront of these transformations. AI’s integration into this area has already revolutionized how threats are detected, mitigated, and prevented.
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Here are six ways AI can stretch the potential of traditional network defenses today:
Real-time firewall data: Conventional firewalls often rely on pre-configured rulesets. That makes them reactive and limited in scope. AI-driven firewalls leverage machine learning to recognize abnormal patterns and behavior in real-time. By analyzing vast amounts of network traffic metadata, AI can dynamically adjust firewall rules, anticipate possible attacks, and even detect zero-day threats. These tools learn over time from both successful and failed network infiltration attempts. The capability to autonomously adapt makes AI-powered firewalls far more effective in managing emerging and previously unknown attack vectors.
Dynamic access control systems: Managing access control within large organizations presents a challenge AI can take up by improving the way permissions are assigned and monitored. AI-enhanced access control systems can analyze user behavior to identify potential insider threats or unauthorized access attempts. These tools constantly assess the risk profile of each user to automatically adjust privileges based on real-time activity patterns. Such systems are also invaluable for handling MFA, pinpointing anomalies, such as when a user logs in from an unusual location or device, and flagging these events for further investigation. With AI, access control tools can shift from static role-based models to more dynamic, behavior-driven approaches.
Next-gen SIEM tools: AI can give security information and event management (SIEM) systems a boost by automating the detection and prioritization of security incidents. Traditional SIEMs rely heavily on pre-configured rules and produce numerous alerts, many of which are false positives. To address this issue, AI can sift through large volumes of data, detect patterns that human analysts might miss, and classify events based on risk levels. This makes threat hunting more effective and lets security teams focus on genuine threats. Furthermore, such SIEM platforms learn from past incidents to improve their accuracy, which helps organizations respond faster to new network threats and reduce downtime caused by breaches.
Optimized network segmentation: Splitting the network into smaller fragments helps minimize the spread of an attack, but it’s difficult to implement in a frictionless way across complex infrastructures. AI enhances network segmentation strategies by evaluating traffic patterns and suggesting optimal configurations for subnets. This process creates more granular and dynamic segmentation based on current risk levels, thereby limiting the attacker’s progress if they gain a foothold in the network. AI tools can also monitor the behavior of devices and applications within each segment to detect anomalies or suspicious lateral movement across segments. This offers real-time threat detection while ensuring that security policies adapt to the continuously evolving network topology.
Better incident response: Already today, AI plays a critical role in improving the speed and accuracy of the organization’s response to cyberattacks. By automating parts of this process, it identifies and contains threats faster than human teams could do manually. Such systems automatically isolate impacted network segments, apply patches, or remove malware to narrow down the window of opportunity for malicious actors. Moreover, AI tools assist incident response teams through real-time insights and recommendations. This helps security pros prioritize their actions and avoid unnecessary escalations.
Greater network visibility: With the proven capability to analyze vast amounts of information in real-time, AI helps organizations maintain a clear picture of what’s happening across their networks. It’s a stepping stone to detecting subtle signs of intrusions early. Events like unexpected data transfers or unknown devices connecting to the network will trigger alerts for immediate action. AI’s effectiveness in processing data from various sources, including endpoints, cloud environments, and IoT devices, offers a holistic view of the network. This all-around visibility minimizes blind spots that traditional monitoring systems may overlook.
There’s very little question that AI has reshaped how companies approach network security. This technology does the heavy lifting in terms of automation, predictive analytics, and adaptive learning. Ultimately, it offers security teams the tools they need to detect, prevent, and respond to modern cyber threats more effectively.
David Balaban, owner-Privacy-PC
SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Each contribution has a goal of bringing a unique voice to important cybersecurity topics. Content strives to be of the highest quality, objective and non-commercial.
