FAQ

Edge security is enterprise-grade security for corporate resources outside centralized data centers. It protects users and apps at the edge of an organization’s network, where data is significantly more vulnerable and prone to cyberattacks.

Edge security refers to the implementation of security practices at network nodes outside of the network core. The edge must possess the same fundamental security elements as the network’s core:

• The administrators must have visibility over the entire network.
• Those administrators must employ automated monitoring systems.
• Data must be encrypted both at rest and in motion.
• The ability to manipulate data and network resources must be restricted.
• Near the end-user, edge computing devices can take the shape of local data centers, micro data centers, or nearly any small computing device.

The Internet of Things (IoT) will increasingly rely on edge networks to drive deployment. However, IoT devices present major security vulnerabilities, making edge security more crucial than ever.

Edge computing is packed with benefits for businesses, but it also creates a larger surface area for cybersecurity threats to penetrate corporate networks. A network typically includes hundreds of edge computing devices, which also creates hundreds of risk areas and entry points for DDoS attacks, ransomware, and any other form of security breaches.

As the attack surface grows larger, protecting data at the edge is a monumental security challenge for Chief Information Security Officers and Chief Technology Officers.

With robust edge security measures, you can build a security stack that protects your corporate network against zero-day threats, malware, and point-of-access attacks.

There are several essential components in edge security:

• Device security. Endpoints or edge computing devices can be sensors, security cameras, routers, card terminals, cash registers, and more. These endpoints are everywhere, especially after hybrid work became the norm, giving room for distributed remote offices as well as BYOD devices all over the world. Typically, these edge devices fail to focus on security and instead prioritize functionality and connectivity. In addition, devices are at the risk of being stolen, can have insecure VPN connections, or lack a UI for IT visibility. Effective device security gives IT teams a centralized way to manage and monitor devices with ongoing user authentication and access control policies.
• Cloud security. The volume of data generated by internet-connected devices is massive. By design, edge computing moves data processing and storage closer to its source so it better manages the load, making it inadvertently vulnerable to attacks. Protecting sensitive data should be met with strict compliance requirements, so it’s critical to prioritize cloud security for the edge with measures like encryption for local and in-transit data between devices and the network.
• Network edge security. Users need internet connectivity to access cloud and SaaS applications, and while it makes for a superior user experience, it also increases the attack area for malicious activity from the internet and into the corporate network. Network edge security includes web filtering, anti-malware software, firewalls, intrusion prevention systems, and more.

AI EdgeLabs is a powerful and autonomous cybersecurity AI platform that helps security teams respond immediately to ongoing attacks and protect Edge/IoT infrastructures against malware, ransomware, DDoS, botnets, and other threats. AI EdgeLabs delivers cybersecurity measures by bringing network visibility, early threat detection, and automated incident response in the Edge and on-prem environments.

Sensor - a small piece of software that can be installed directly into the Edge and work as autonomous threat detection and prevention software. The Sensor is the agent that holds AI models and extracts network-based data points and provides threat detection and prevention on the spot.

Backbone - API server that holds communication between Edges, Security Operation Teams, and EdgeLabs infrastructure, where it stores incidents/features/historical data for operations in future. Backbone provides core functionality for alert mitigation and deduplication. The Sensor sends compacted telemetry and registers alerts directly on the Backbone.

The Lab - internal ecosystem for the DataScience and Security R&D teams that provides capabilities to research new threat patterns, re-train AI models, and maintain a knowledgebase of the threats and attacks that are known on the market and can be applied for detection of new threats with AI.

There is a range of edge computing security risks that pose the highest threat for the organizations due to vulnerable Edge security architecture:

• Theft of the data on a device;
• Data exfiltration on a device;
• A distributed ransomware attack;
• Unauthorized access to centralized computing resources.

• Retail
• Automotive
• Railway
• Manufacturing
• Telecommunications
• Oil and Gas
• Transportation and Logistics
• Agriculture
• Healthcare
• Smart Cities
• Energy and Utilities

The EdgeLab Sensor is a proprietary network telemetry and monitoring agent that continuously scans and reports network and Edge/IoT Gateway performance and configuration. It holds an AI-powered model stack that continuously checks traffic behavior through pre-trained algorithms. With EdgeLab Sensor, infrastructure teams can locate application performance problems faster, reduce time-to-diagnosis, and accelerate time-to-repair without additional efforts for deployment and integration thanks to plug and play capabilities.

During the first deployment, the EdgeLabs Sensor runs a configurable set of security checks and topology research to collect the initial context of the environment. Data from these collections is analyzed by the EdgeLabs AI Platform™ (ELAP).

ELAP provides a set of AI-based models which are pre-trained in the existing knowledge base of threat patterns and attack signatures; this pre-training is always done centrally on ELAP. By running these collections from a fleet of well-distributed/placed EdgeLabs Sensors, the AI Security team can dramatically improve the precision with which they instrument, monitor, and maintain their application delivery infrastructure.

The EdgeLabs Sensor helps:

• Generate near-time visibility of infrastructure and application performance.
• Prevent and block threat sources in real-time.
• Integrate cloud-based and on-premises application performance monitoring.
• Monitor SaaS applications from your users’ vantage point.
• Monitor IaaS resources from your users’ vantage point.

• NETWORK VISIBILITY & ANOMALY DETECTION
  • Low noise-to-signal ratio for Lateral Movement attack detection at scale;
  • Selection of next steps for threats for easy acceptance, mitigation, and remediation of risk vulnerabilities. Individual or bulk support for lifecycle disposition;
  • Centralized dashboard that offers attack surface visibility and network topology signaling both abnormal behavior and alerts;
  • Robust security audit reports and posture based on assets with clear recommendations on how to solve specific security issues.
• THREAT ANALYTICS
  • Threat monitoring based on the latest behavior-based analytics;
  • Rich traffic inspection for anomalous patterns with Reinforcement Learning algorithms and threat modeling;
  • Threat level overview and severity-based prioritization;
  • In-depth alerts with context of adversary tactics, techniques, and procedures (TTPs);
  • Scope mapping of MITRE ATT&CK by the security team.
• INCIDENT RESPONSE & REMEDIATION
  • Real-time visibility across an organization’s information security systems (SIEM);
  • Threat history and research based on historical data;
  • Anomalous behavior reporting and forensics;
  • SOC/NOC Monitoring (24/7);
  • Alerting system with Jira, Slack, Discord, and other reporting system integrations.

Yes, AI EdgeLabs is packed for immediate use and we offer prospects a trial of the solution to try out the basic functionality. Fill out the contact form below, and we will get back to you with the answer to your questions.

A brute force attack is a type of hacking that uses trial and error to crack passwords, login credentials, and encryption keys. Think of it as the equivalent of trying all the keys on your keyring until you find the right one. In short, it is a simple and reliable way to get into people's accounts and organizations' systems and networks without their permission using excessive digital force or trying too hard. The hacker tries different usernames and passwords, often with the help of a computer, until they find the right login information.

Intrusion detection systems (IDS) watch and analyze events on business networks to find security problems and threats that are about to happen. These security solutions protect businesses by stopping cyberattacks before they happen. An intrusion detection system is a way to keep an eye on a network and send alerts to incident responders or security operations center (SOC) analysts when something looks suspicious. With these alerts, security staff can look into problems that have been found and take the right steps to fix them before they cause a lot of damage.

Intrusion prevention systems (IPS) find intrusions and then take the next step to stop any threats they find. Similar to an intrusion detection system, an intrusion prevention system is a piece of hardware or software that watches how a network works all the time for signs of threats. But IPS goes one step further than IDS and automatically takes the right steps to stop the threats it finds. These could be things like sending a report, blocking traffic from a certain source, dropping packets, or resetting the connection. Some IPS solutions can also be set up to use a "honeypot," which is a fake target with fake data, to trick attackers and keep them from getting to their real targets, which have real data.