Artificial Intelligence is changing the game in cybersecurity. Increasingly, AI is enabling the bad actors to speed and scale their attacks, creating new levels of risk. I bet you were not expecting that! The truth is that AI/ML is a more effective tool for the adversary’s right now than the good guys who defend the networks.
Organizations can leverage edge technology to counter AI-driven threats more effectively by collecting data and executing AI analysis directly on edge devices, which can range from compact systems like a Raspberry Pi to tactical 1U servers, they can accelerate defensive responses where the data is generated. Edge processing also reduces reliance on constant connectivity, enabling real-time insights while still preserving data for deeper analysis later.
The New Threat Landscape
Artificial Intelligence is revolutionizing the cybersecurity risk landscape. By enabling attackers to automate and scale their operations, AI is rendering traditional defenses less effective. “Just as security organizations can use AI to help enhance their defensive capabilities, so too can cyber attackers leverage AI to augment their offensive strategies,” the Wall Street Journal reports.
Attackers can use AI to automate their efforts, increasing the speed and scale of their exploits. AI can be used to identify optimal attack vectors for precision strikes, for example, AI-generated code can help attackers to get around conventional security measures.
AI driven cyber attacks are only growing faster, more automated, and more complicated. Despite advancements in AI/ML algorithms, government and commercial organizations are struggling to find AI/ML solutions for cybersecurity analysis that keep up with modern high-speed networks. For example, account takeover (ATO) attacks—an increasingly common tactic where malicious actors use bots to rapidly attempt logins, aiming to find valid credentials and access target systems . Gartner forecasts that by 2027, AI agents will reduce the time required for these attacks to succeed by 50%.
Criminal organizations are increasingly exploiting this capability, as seen with the emergence of groups like FunkSec. As reported in BitDefender, “FunkSec has used AI to develop code for an encryptor, which is an essential capability for a ransomware group.” They’ve already gone after more than 120 organizations, including government and defense targets.
The Need for Edge-Based AI Detection
Centralized analysis strategies risk coming up short in the emerging era of accelerated, AI-driven cyberattacks. Traditional cloud-based security solutions struggle with latency and bandwidth issues, especially when dealing with encrypted traffic and large datasets.
For both Intelligence, Surveillance, and Reconnaissance (ISR) missions and Internet of Things (IoT) devices, it’s inefficient and ineffective to send data back to the cloud for analysis. “AI-driven systems generate vast amounts of data. Sending all this data to a centralized cloud server for processing creates bandwidth congestion and increases latency,” according to Viasat.
Edge computing, by comparison, “processes data closer to its source instead of relying on centralized cloud infrastructure,” Viasat reports. This approach in turn “reduces the need for constant data transmission between devices and remote servers, enabling faster response times and improved efficiency.”
Organizations need their own AI-driven defenses to counter the rising threat, and edge processing is key to the successful deployment of such defenses against AI-empowered cyberattacks. It allows for real-time processing of critical signals, enabling defenders to act faster. Processing at the edge likewise frees up mission-critical bandwidth, keeping data local while ensuring channels of communication stay open.
For the military and others with high-value missions, the ability to process at the edge can help to deliver real-time threat detection, reducing the time needed to respond to a cyberattack and thus minimizing potential damage.
Edge-Based Cyber Defense: Enhancing Detection with AI at the Edge
To keep pace with the increasing velocity and complexity of high-speed networks, Axellio believes there’s a strong potential in applying AI/ML techniques (such as anomaly detection) at the edge. With the ability to process packets at line rates exceeding 100 Gbps on compact, resource-efficient hardware, we see a future where AI-driven threat detection can occur directly at the network’s edge, in near real-time. High-throughput, low-latency packet analysis is increasingly achievable on lightweight, commercial off-the-shelf (COTS) systems. This paves the way for more detailed analytics, including AI and ML, to be deployed at the edge. This can be achieved even in tactical and forward-operating environments where size, weight, and power (SWaP) constraints demand compact and efficient solutions.
Conclusion: Staying Ahead of AI-powered Threats
In today’s high-stakes cyber environment, the ability to respond in real-time is a huge advantage. Traditional cybersecurity tools are not able to keep up with the scale and speed of modern AI-powered threats. In particular, organizations in defense and mission-critical sectors need solutions that can operate at the edge. Also, they need the ability to analyze raw traffic at high speeds to detect threats as they happen. To stay one step ahead, organizations need solutions that support effective edge processing and empower defenders to deploy AI-driven capabilities against tomorrow’s increasingly sophisticated cyber threats.
AI-driven threats are changing how we think about cybersecurity. Putting AI at the edge helps organizations act faster and stay ahead.
