Splunk, a provider of software that helps organizations gather and make use of machine data from a various sources, announced on Thursday that is has acquired Caspida, a maker of machine learning and behavioral analytics software.
San Francisco, Calif.-based Splunk paid roughly $127 million in cash and $63 million in stock, for total consideration of approximately $190 million, to acquire Caspida.
Caspida describes itself as a cyber-security and threat detection company that detects and prevents threats hiding inside corporate networks.
“With Caspida, Splunk accelerates its focus on solving advanced threats – both external and from insiders – by shining a light on those who are wrongfully using valid credentials to freely and unpredictably exploit systems they have accessed,” said Haiyan Song, senior vice president of security markets, Splunk.
Capabilities of the combination of the Splunk and Caspida solutions include the ability to:
Detect Advanced, Hidden and Insider Threats
• Continuous threat and anomaly detection that applies multi-domain analysis using machine learning.
• Uncovers hidden breaches and new attacks out-of-the-box without extensive customization. Improve Threat Detection with Targeted Incident Response
• Provides threat activities relative to the kill chain with supporting evidence to enable targeted remediation.
• Detects multi-domain (user, device and traffic applications) anomalies and streamlines threat review and incident resolution.
Increase SOC Efficiency
• Scores and highlights the most important threats and anomalies to minimize alert fatigue.
• Detects and provides insights on threats and suspicious activities to complement and extend threat intelligence.
“We founded Caspida with a vision of applying data science to help solve the most pressing cybersecurity challenges – advanced threats and insider threats,” said Muddu Sudhakar, CEO, Caspida. “By analyzing machine data and using data science to detect meaningful anomalous behavior of users, devices and entities, Caspida has solved a problem that previously required significant manpower and expensive, do-it-yourself toolsets.”