Aurva provides real-time data visibility and access controls across various data stores, utilizing AI-powered threat detection and automated asset inventory to identify and remediate vulnerabilities. The platform addresses the risk of data breaches and misconfigurations by offering over 300 prebuilt security policies and continuous monitoring of cloud traffic.
Funding
$2.1M 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
Organizations struggle to maintain visibility and control over sensitive data spread across diverse data stores, leading to misconfigurations, compliance violations, and increased risk of data breaches from both internal and external threats. Identifying and remediating these vulnerabilities is a time-consuming and complex process, often requiring manual effort and specialized expertise.
Solution
Aurva provides an AI-powered data security platform that delivers real-time data visibility and access controls across various data sources. The platform automates asset inventory, discovers and classifies sensitive data using context-aware NLP models, and identifies misconfigurations and compliance violations. By continuously monitoring cloud traffic and applying over 300 prebuilt security policies, Aurva enables organizations to detect and streamline remediation of internal and external threats, reducing the mean time to detection (MTTD) and improving overall data security posture.
Target Audience
Aurva's primary customers are security leaders and engineering teams in organizations that need to protect sensitive data, comply with regulatory requirements, and reduce the risk of data breaches.
Features
- Automated data discovery and classification using context-aware NLP to identify PII, PCI, and PHI.
- Continuous monitoring of cloud traffic to detect malicious activity and policy violations.
- Prebuilt security policies based on industry best practices and compliance frameworks.
- Automated asset inventory across structured and unstructured data sources.
- Data lineage tracking to understand data flow and dependencies.
- External party monitoring to identify and mitigate third-party risks.
- Integration with infrastructure, communication channels, and identity providers.