The startup offers a predictive threat intelligence platform that utilizes behavioral profiling and machine learning to analyze and forecast cyber threats. By identifying inefficiencies during the setup phase, the platform enhances security analytics and enables clients to implement secure network connectivity, ultimately reducing the risk of data breaches.
Funding
$920K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional cybersecurity measures often react to threats after they have already emerged, leaving organizations vulnerable to zero-day exploits and novel attacks. Existing threat intelligence feeds are frequently filled with irrelevant indicators of compromise (IOCs) and suffer from high false positive rates, overwhelming security teams and hindering effective threat response.
Solution
Augur provides a predictive threat intelligence platform that leverages AI-powered behavioral modeling and agentic automation to identify and preemptively block cyber threats. By analyzing global internet infrastructure, Augur detects the earliest signs of malicious intent, identifying attack infrastructure an average of 51 days before attacks go live. The platform integrates with existing security stacks, including SIEM, SOAR, firewalls, and EDR solutions, to transform threat intelligence into immediate, automated preventative action. Augur's AI autonomously tailors blocklists based on the likelihood of specific threats targeting the client's environment, ensuring high-confidence intelligence with near-zero false positives.
Target Audience
The primary target audience includes SOC teams, CISOs, and security leaders seeking to proactively prevent cyberattacks and improve their organization's overall security posture.
Features
- AI-powered behavioral modeling to detect malicious intent before attacks are launched
- Agentic automation for autonomous tailoring of blocklists based on client-specific threat likelihood
- Integration with existing security infrastructure (SIEM, SOAR, firewalls, EDR) via API
- Identification of attack infrastructure an average of 51 days ahead of traditional threat intelligence feeds
- Near-zero false positive rate (0.007%) for high-confidence intelligence
- Executive-level reporting on threat sources, actors, and blocked connections