An innovative approach to data acquisition, advanced AI and Machine Learning is helping analysts meet the volume and sophistication of modern attacks, reducing organizational risk and improving SOC teams’ ability to stay ahead of modern threats.


DNS Anomaly

Multiple Machine-Learning-based DNS Anomaly detection patterns look for abnormal behaviors in the DNS protocol including exfiltration, beaconing and C2 activities.


Ransomware Delivery

Multiple analytics to facilitate the detection of various forms of ransomware on the network prior to exploitation occuring.


Drive By Web Exploit (malware delivery)

Anomaly patterns to identify when potentially malicious content is downloaded via the web without relying solely on signatures or threat intelligence.


Lateral Movement Detection

SMB visibility to enhance the detection of adversarial movement across the enterprise once an insider has gained a foothold to quickly identify the scope of the incident.


Risky IT Behaviors

Detection of highly publicized threats like Intel AMT that can impact the entire IT ecosystem. This may also include detection of poor security practices that expose networks to additional risk.


Covert Channels & Data Extraction Anomalies

DNS, SSH, HTTP Tunneling detection to look for covert communications used to bypass traditional detection mechanisms.


CASE STUDIES

Government Solution Provider

Solution providers that work with government entities know that reputation and business vitality are on the line when it comes to security. That understanding is what drives a top digital government solutions…


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Global Investment Firm Bolsters Small Security Team With Autonomous Security Operations

When it comes to cybersecurity, a global investment firm with five offices worldwide is up against many of the same issues as other organizations in the financial industry. The firm’s five-person security, team, led by…


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