Harnessing Packet Data to Stop Evolving AI Cyber Threats

Today, there is no escape from artificial intelligence (AI). According to Forrester’s 2024 security and risk predictions, this is the year of AI risks and regulatory scrutiny, the likes of which we have never seen before.

Unfortunately, attackers are learning to exploit technologies such as generative AI and machine learning to execute more frequent and problematic attacks. For example, large language models have been used by Iran’s Revolutionary Guard to automate the development of phishing emails and even used to test how invaders might evade network detection.

In this new cyber arms race, large enterprises that are frequently the target of the most sophisticated hacking groups and nation-state actors cannot remain static. If the large language models’ example is any indication, then enterprises must consider new solutions and processes that scale and multiply their own detection and response capabilities. Of course, a key consideration for security teams is how to effectively detect malicious activity once threat actors have breached front-line defenses.

Rising to this challenge, advanced Network Detection and Response (NDR) solutions have emerged to complement and enhance existing security capabilities by providing network context and AI-led automated responses to threats. Likewise, network context enables security and network operations teams to collaborate more effectively, leading to better detection and mitigation.

NDR Uses Network Context to Block AI Cyber Threats

Advanced NDR technology analyzes the content of packets and related metadata as it passes through the network to identify active threats. This involves monitoring network traffic patterns, application behaviors, and user activities to identify deviations that suggest malicious AI activity.

Compared with traditional packet filters, advanced NDR technology explores a much wider range of information, including not just the header but also the data or payload that the packet is carrying, and requires packet probes to monitor access of both internal (East-West traffic) and external (North-South traffic) communication paths. These paths include all the data routes within an enterprise's network (internal) and those that connect the enterprise to the outside world (external). While that might seem simple, it’s challenging to accomplish since today’s enterprise networks are a complex mix of legacy networks, branch offices, home offices, and private and public clouds.

Most importantly, advanced NDR technology provides packet-level network context about potentially compromised devices and users, which is crucial for gaining the big-picture perspective of active breaches and helping security teams quickly identify, understand, and respond to threats. In doing so, that network context can offer early warnings about malicious activity, provide contract tracing for infected hosts, and enable historical analysis to locate bad actors within the network. Likewise, it can feed other cybersecurity solutions for a more automated, AI-led initial response.

AI, Behavioral Analytics, & Machine Learning (ML): A Trifecta in Attack Response

When an NDR solution is deployed, it monitors an enterprise's network traffic to gain visibility into potential cyberthreats. It then relies on advanced capabilities, including AI, behavioral analytics, and ML, to uncover threats and suspicious activities on the attack surface, which may include IoT devices, SaaS applications, and other connected devices. In addition, ML models within NDR solutions can help identify trends indicative of AI-generated threats, better analyze traffic patterns, and compare profiles against known attackers. Simply put, this solution learns from new data and adapts to evolving attack techniques, improving the ability to detect and respond to AI-driven threats in real time.

In addition, NDR solutions can easily integrate with other cybersecurity tools, such as Security Information and Event Management (SIEM) or Endpoint Detection and Response (EDR). NDR can also cut the time spent conducting investigations by leveraging high-fidelity network metadata and packets and comparing it to a timeline of events to reveal attack behaviors. This network metadata can also be shared with SIEM solutions to create broader security assessments. In the end, prioritizing NDR solutions in the security stack, alongside other threat mitigation tools, effectively fills critical visibility and data gaps, creating a complete solution to make the security stack operate more effectively.

The only place that attackers can’t hide is on the network. That is because endpoint data can be manipulated, and once a packet is captured, it is challenging to change that data. As such, most large organizations need a more comprehensive solution combining network and endpoint data for a more robust, real-time view of the evolving threat landscape. Network data provided by advanced, packet-based NDRs can act as the glue that connects and contextualizes inputs from other security systems. That context is vital to understanding new AI-driven cyber threats that persist as bad actors continue engineering malicious exploits in the future.