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Description
Traditional Threat Intelligence Platforms (TIPs) notify you of dangers only once they are already attempting to breach your network. In contrast, SecLytics Augur employs machine learning to analyze the patterns exhibited by threat actors, thereby constructing detailed profiles of adversaries. This innovative system forecasts the development of attack infrastructure and accurately predicts potential assaults with minimal false positives, often before they occur. The insights gained from these predictions can be seamlessly integrated into your Security Information and Event Management (SIEM) system or managed security service provider (MSSP) to facilitate automated threat blocking. Augur continually manages and assesses a database of over 10,000 adversary profiles, with fresh profiles being introduced on a daily basis. By anticipating threats before they officially manifest, Augur effectively neutralizes the element of surprise that attackers often rely upon. Unlike conventional TIPs, Augur is capable of uncovering and safeguarding against a broader array of potential threats. Furthermore, it adeptly detects the establishment and accumulation of cybercriminal infrastructure online prior to an attack, as the patterns exhibited during infrastructure setup are both systematic and distinctive. This proactive approach not only enhances security measures but also empowers organizations to stay ahead of emerging cyber threats.
Description
The field of cybersecurity is in a state of constant flux, with both the approaches used by threat actors and the defenses implemented by security professionals adapting over time. Keeping pace with these shifting Tactics, Techniques, and Procedures (TTPs) poses a significant challenge, even for the most sophisticated security operations. Simply acquiring high-quality intelligence is insufficient; security teams must also effectively contextualize, analyze, and apply the information they gather to safeguard their organizations.
As the amount of intelligence grows, it becomes essential for organizations to adopt a scalable cybersecurity threat management solution. By leveraging automation and improved workflows, the reliance on costly analysts and team members can be diminished while still maintaining the effectiveness of the cybersecurity framework.
Developed by Goldman Sachs, scoutTHREAT serves as a Threat Intelligence Platform (TIP) designed to help your cybersecurity program proactively detect threats prior to their occurrence. This innovative platform empowers teams to stay ahead of potential risks, ensuring a more secure operational environment.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
IBM QRadar SIEM
LogRhythm SIEM
Palo Alto Networks Next-Generation Firewalls
PowerDMARC
Revelstoke
Splunk Enterprise
Sumo Logic
Zscaler
Integrations
Amazon Web Services (AWS)
IBM QRadar SIEM
LogRhythm SIEM
Palo Alto Networks Next-Generation Firewalls
PowerDMARC
Revelstoke
Splunk Enterprise
Sumo Logic
Zscaler
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
SecLytics
Founded
2014
Country
United States
Website
www.seclytics.com/seclytics/augur
Vendor Details
Company Name
LookingGlass Cyber Solutions
Founded
2009
Country
United States
Website
lookingglasscyber.com/solutions/scoutthreat/
Product Features
Product Features
Cybersecurity
AI / Machine Learning
Behavioral Analytics
Endpoint Management
IOC Verification
Incident Management
Tokenization
Vulnerability Scanning
Whitelisting / Blacklisting