Flying Cloud Technology offers a data surveillance platform, CrowsNest DSPM, that employs patented technology to monitor, analyze, and protect all organizational data in real-time. This solution addresses the challenges of data visibility, security, and compliance by ensuring that data integrity and usage policies are enforced across various processes and environments.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations struggle to maintain visibility and control over sensitive data across diverse environments, leading to security vulnerabilities, compliance violations, and potential data breaches. Existing security solutions often focus on network and device access, failing to address data-level risks such as unauthorized usage, exfiltration, and insider threats. The increasing reliance on AI further complicates data governance, requiring stringent controls over data provenance, quality, and usage policies.
Solution
Flying Cloud Technology's CrowsNest DSPM (Data Security Posture Management) platform provides real-time data surveillance, enabling organizations to monitor, analyze, and protect data wherever it resides or travels. Utilizing patented technology, CrowsNest fingerprints data at the binary level, establishing a baseline of normal data behavior and detecting anomalies indicative of security threats or policy violations. The platform integrates with existing security infrastructure, enhancing alerts and remediation workflows while enforcing data usage policies beyond traditional access controls. CrowsNest delivers a data chain of custody, supporting incident response and digital forensics with contextual analysis and reconstructed events.
Target Audience
The primary target audience includes enterprises seeking to improve their data security posture, comply with data privacy regulations, and mitigate risks associated with data breaches and insider threats, particularly those leveraging AI and cloud-based data storage.
Features
- Real-time data fingerprinting and cataloging without modifying the original data
- Anomaly detection based on machine learning and automated baseline analysis of data behavior
- Automated data classification and policy enforcement, including data fencing to restrict data movement
- Identification of cyber threat activity within data, such as ransomware, botnets, and malware
- Integration with SIEM/SOAR platforms for enhanced alerting and remediation
- Visibility into both structured and unstructured data, including images and audio streams
- Data exfiltration detection based on tunable parameters and policy definitions
- Comprehensive data chain of custody for incident response and forensic analysis