Artificial Intelligence and Machine Learning have become crucial tools for cyber threat detection and response solutions in edge security environments. The exponential growth of connected devices, data, and applications, as well as the increasing sophistication of cyber threats, have made it difficult for traditional security solutions to keep pace. AI and ML provide the necessary capabilities to secure edge environments, by automating and streamlining the threat identification and response process.
Here are some key reasons why AI and ML are important for cyber threat detection and response in edge security:
Real-Time Monitoring: AI and ML-powered solutions can monitor edge environments in real-time, analyzing vast amounts of data 24/7.
Threat Detection: AI and ML algorithms can immediately detect known and unknown threats, and identify anomalies in network traffic, system logs, and device behaviors. This enables organizations to proactively identify and respond to threats, before they cause damage.
Threat Response: AI and ML can automate the threat response process, providing organizations with a faster and more effective way to neutralize threats. This reduces the time it takes to respond to a security incident, minimizing the damage caused by a breach.
Edge Nodes, IoT Devices and IoT Gateways are often deployed far outside a centralized data infrastructure or datacenter, making it significantly harder to monitor from both a digital and physical security standpoint. There is a range of edge computing security risks that pose the highest threat for the organizations due to vulnerable Edge security architecture, including a distributed ransomware attack.
AI EdgeLabs delivers advanced network visibility, threat intelligence for early threat detection, and real-time response and remediation for incidents. Our solution, at its core, is an extended detection and response (XDR) platform that is powered by AI, ML, and Deep Reinforcement Learning models that automates the detection of even the slightest of anomalies signaling the presence of risks or cyber threats.
Next, we detail the specific ways in which AI EdgeLabs power and enhance the early detection and response of threats.
Optimized performance of machine learning inference to accelerate the performance and accuracy of early threat detection and response. Added models to ML inference pipeline to detect Denial of Service, Botnets, Address Resolution Protocol (ARP) Spoofing, Brute-Force attacks, ICMP Redirects, DHCP Spoofing, LLMNR Spoofing, NBNS Spoofing, ARP Harvesting, and more.
Employment of YARA rules for malware/ransomware detection. YARA rules are fully customizable detection patterns that help pinpoint targeted attacks and threats.
A centralized, all-in-one dashboard with a dedicated space for Security Operations Teams to gain extensive knowledge about attack and security weaknesses. The dashboard provides visualization tools for advanced attack surface visibility and recommends mitigation actions. Also, it allows organizations to historically visualize attack velocity and system impact with robust settings.
Advanced reporting and forensics capabilities - threat history and research based on historical data along with SOC/NOC Monitoring (24/7).
Threat monitoring based on the latest behavior-based analytics and rich traffic inspection for anomalous patterns with Reinforcement Learning algorithms and threat modeling.
Threat level overview and severity-based prioritization to provide security teams with In-depth alerts with context of adversary tactics, techniques, and procedures (TTPs).
Scope mapping of MITRE ATT&CK.
Real-time visibility across an organization’s information security systems (SIEM).
Our threat detection system and intelligence platform enables a holistic view of an organization’s cybersecurity and allows us to focus on what is important, and to know what is noise. This approach allows end-users to perform and design proactive security strategies to detect, analyze, and mitigate threats.
Using the right AI-driven XDR tool can make a 180° difference in the way companies approach cybersecurity. AI EdgeLabs’ unique abilities, such as deep learning and ML threat modeling, help analyze and fix large sets of potentially dangerous data without any human involvement, strengthening digital safety and security posture for companies.
Some of the biggest AI EdgeLabs benefits for securing edge environments include:
The ability to find and describe unusual patterns and weak spots in large and growing edge networks. Monitoring and analyzing large networks manually takes a lot of time or is hard to do. AI EdgeLabs makes it easier and faster to analyze data from multiple endpoints. The AI EdgeLabs extended detection and response system easily and accurately detects any strange or malicious traffic that comes into a network or edge environment.
Accurate risk assessments and better information about threats thanks to the platform’s expanding and continuously learning intelligence used to precisely find, analyze, and evaluate risks, as well as to recommend strong security controls for risks that have been found.
Automation to speed up response times and make it easier for human security analysts to handle complex security tasks.
Reduced costs of cybersecurity by at least 15%.
Cuts to data breach costs by at least 18%.
Increased return on security investment (ROSI) by 40% or more.
AI and ML are critical components of a comprehensive edge security solution and cyber threat prevention, providing organizations with the necessary tools to detect and respond to threats in real-time. By automating and streamlining the threat detection and response process, AI and ML help organizations secure edge environments and reduce the risk of a data breach.
Overall, edge security is critical to the success of modern organizations. With the growth of connected devices, cloud services, real-time data, and new threats, it is essential to implement robust security measures at the edge. By doing so, organizations can protect their data, devices, and reputation, and remain competitive in today’s rapidly evolving digital landscape.