Abluva offers a multi-layered data protection platform that utilizes AI-driven risk detection and fine-grained access controls to secure both structured and unstructured data against insider breaches. The platform addresses the significant threat posed by insider attacks, which account for 62% of breaches and incur an average containment cost of $2.9 million.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional data protection solutions often fall short in preventing insider breaches, which account for a significant percentage of security incidents and result in substantial financial losses. These solutions typically focus on restricting data access, which can disrupt business operations without effectively stopping determined insiders or compromised accounts. The increasing sophistication of AI-driven attacks further exacerbates this problem.
Solution
Abluva offers a data protection platform that utilizes AI-driven risk detection and fine-grained access controls to secure structured, unstructured, and unique data formats against insider threats. The platform proactively identifies and mitigates potential risks by scanning both static data and dynamic user interactions. By implementing policy-based access restrictions and advanced masking techniques, Abluva enables secure data sharing without the need for redundant copies. The platform's breach detection capabilities provide unparalleled insight and control, catching even sophisticated, slow-acting intrusions that blend in statistically but stand out contextually.
Target Audience
The primary target audience includes data scientists, security teams, and organizations handling sensitive data who need to secure their data against insider threats and ensure compliance with data privacy regulations.
Features
- AI-powered risk detection that scans static data and dynamic interactions to identify potential insider threats.
- Fine-grained, policy-based data access controls tailored to traditional and modern data formats.
- Autonomous data discovery and classification using standard ontologies and advanced machine learning.
- Data anonymization capabilities that implement advanced masking controls to protect privacy without compromising productivity.
- Breach detection that identifies sophisticated, slow-acting intrusions by analyzing contextual anomalies.
- Secure LLM module to protect proprietary data from leaking during training, retrieval, and access of AI models.
- Secure Graph module for fine-grained, policy-based data access controls on graph databases.
- True-bDSP architecture that extends broad data security platforms to encompass structured, unstructured, and unique data formats.