ADHRITH AI offers autonomous, multi-agent AI systems that integrate with existing cybersecurity infrastructure to automate threat detection and response. These AI agents reduce SOC workload and operational costs by triaging alerts, minimizing false positives, and executing policy-guided actions.
Funding
Funding not disclosed
Founders
Product
Problem
Security Operations Center (SOC) teams face significant workload challenges due to alert fatigue and the need for constant human oversight in threat detection and response. This leads to increased operational costs and a reduced efficiency ratio between AI and human analysts.
Solution
ADHRITH AI provides autonomous, multi-agent AI systems designed to integrate seamlessly with existing cybersecurity infrastructures. These AI agents operate without human intervention, continuously learning and adapting to enhance threat detection and response capabilities. By automating alert triage, reducing false positives, and executing policy-guided actions, ADHRITH AI aims to significantly decrease SOC team workload and operational expenses. The platform offers an improved AI-to-human analyst efficiency ratio, enabling security teams to focus on strategic initiatives rather than manual analysis.
Target Audience
The primary target audience includes cybersecurity teams within enterprises and organizations seeking to enhance their Security Operations Center (SOC) efficiency and reduce operational overhead.
Features
- Autonomous AI agents capable of independent operation and continuous learning.
- Seamless integration with existing security stacks and workflows.
- Utilizes the MITRE ATT&CK framework for comprehensive threat detection and response.
- Achieves up to a 94% reduction in false positives through contextual analysis.
- Automates incident response actions based on predefined security policies.
- Offers an AI to human analyst efficiency ratio of 1:25.3.
- Demonstrates up to a 73% reduction in SOC team workload.
- Operates with up to 37.7% lower operational costs compared to traditional SOC models.