Provides a data security automation platform, HexaKube, that uses AI and machine learning to discover, monitor, and protect AI/ML data across cloud, on-premises, and hybrid environments. It prevents data leakages and malicious attacks by continuously tracking access to large language model (LLM) services and proactively resolving security issues.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises face increasing challenges in securing AI/ML data across diverse environments, including cloud, on-premises, and hybrid infrastructures. Traditional security measures often fail to adequately discover, monitor, and protect sensitive data used in AI/ML models, leading to potential data leakage and malicious attacks. The complexity of tracking access to large language model (LLM) services further exacerbates these security risks.
Solution
MLCode's HexaKube is a data security automation platform that leverages AI and machine learning to address the unique security challenges associated with AI/ML data. HexaKube automatically discovers and classifies AI/ML data assets, continuously monitors data access patterns, and proactively identifies security vulnerabilities. The platform tracks interactions with LLM services to prevent unintended data exposure and malicious activities. By automating data security tasks, HexaKube enables organizations to maintain data governance, comply with regulatory requirements, and mitigate the risk of data breaches in their AI/ML environments. The platform's unified dashboard provides visibility into data security posture, enabling security teams to respond quickly to potential threats.
Target Audience
The primary target audience includes data scientists, security engineers, compliance officers, and IT professionals responsible for securing AI/ML data within enterprises.
Features
- Automated discovery and classification of AI/ML data across cloud, on-premises, and hybrid environments
- Continuous monitoring of data access and usage patterns
- Real-time threat detection and alerting based on AI/ML-driven anomaly detection
- Integration with existing security information and event management (SIEM) systems
- Role-based access control and data encryption to protect sensitive data
- Automated data lineage tracking for compliance and auditing purposes
- Comprehensive reporting and analytics on data security posture