Complete detection coverage 

Corelight exposes attackers attempting to spread internally by establishing behavioral baselines that instantly flag anomalous RDP and SSH connections, unauthorized admin file share access, and unexpected internal executable downloads. This gives your SOC immediate visibility into east-west threat progression before an adversary can establish a permanent foothold.

Corelight uncovers quiet phone-home infrastructure by analyzing network telemetry for hidden HTTP C2 frameworks, malicious SSL certificates, Tor connections, and subtle patterns like NXDomain beaconing or DGA activity. It strips away the attacker’s cover, exposing stealthy command channels even when they are intentionally disguised as normal web traffic.

Discover IOCs and TTPs used in sophisticated attacks with curated Zeek-powered detection packages. Analyze network traffic with exceptional precision using high-fidelity detections and enrichments to illuminate the digital breadcrumbs adversaries leave behind. By exposing signs of compromised hosts, unauthorized transfers, and infections in real time, Corelight gives your team the decisive intelligence needed to act quickly.

Corelight exposes east-west credential compromise by flagging Kerberos abuse, NTLM anomalies, and admin share manipulation that often slip past traditional endpoint (EDR) tools. By continuously monitoring native authentication protocols, Corelight alerts your team the moment an attacker tries to use stolen credentials to move across your environment.

Corelight thwarts Living-off-the-Land (LotL) tactics by using advanced baselining to catch adversaries turning legitimate administrative tools, subnets, and SSH connections against you. It transforms seemingly normal internal network activity into highly visible, actionable anomalies your team can trust and investigate.

Corelight strengthens ransomware defense by providing clear visibility into network activity missed by endpoint solutions. Advanced analytics allow security teams to spot early signals of ransomware, such as unusual lateral movement or suspicious external communications. By generating rich evidence across RDP, SSH, and SMB traffic, Corelight reveals threats before encryption occurs. This real-time detection empowers defenders to quickly respond to ransomware threats, minimizing overall impact and reducing recovery time.

AI threat detection, also known as AI-driven threat detection, uses artificial intelligence (AI) to identify potential cybersecurity threats. These threats may include malware, ransomware, phishing, insider threats, network intrusion, and other malicious activity. It often relies on machine learning (supervised and/or unsupervised) to analyze large volumes of data (network traffic logs, system logs, etc.) for patterns, unusual activity, anomalies, and other possible indicators of compromise (IOCs). Unlike traditional methods of threat detection (signature-based engines, regex patterns, IOC matching, and other rule-based systems), AI threat detection can be trained to learn from new data and behaviors as they evolve. It can also detect new and never-before-seen threats for which no signature or pattern yet exists, enabling proactive threat hunting.

Machine learning (ML) is a subcategory of artificial intelligence (AI) that involves training computer models, or algorithms, to recognize highly complex patterns. This process is similar to, but not the same as, rational decision making. The creation, training, and tuning of these models depends on experts who specialize in computer science, data science, or a combination of the two disciplines.

Artificial intelligence (AI) is affecting every industry and enterprise in both the near and long term. The cybersecurity industry has been an early adopter and testing ground for many AI use cases, helping define both the possibilities and challenges the technology delivers. Cyber attackers are using AI to launch more sophisticated attacks. Defenders, in turn, must use AI-based tools across their security portfolios to proactively hunt for threats, analyze complex data sets, and accelerate and automate decisions.

YARA (aka "Yet Another Recursive Acronym") is a tool designed for file analysis, identification and classification of malware based on textual and binary patterns. Its tongue-in-cheek name notwithstanding, YARA has been an important part of cybersecurity toolkits since it launched on GitHub in 2013. It is open source, which means that a broad community of security experts and organizations contribute to YARA rule sets while simultaneously using it in the field to test suspicious code and confirm malware types found in digital environments.

Anomaly-based detection, sometimes known as behavior-based detection, is a method that uses data analysis and rules to help identify evidence of potential malicious activity in digital systems. Unlike signature-based detection, which focuses on known indicators of compromise (IOC), anomaly-based detection does not search for known malware characteristics. Instead, it establishes a baseline for normal network behavior and, through continuous tuning and adaptation, identifies patterns that may be evidence of malicious activity.