RSA NetWitness Detect AI provides advanced analytics for actionable threat detection

RSA announced the general availability of RSA NetWitness Detect AI, a cloud-native advanced analytics and machine learning solution that provides rapid detection and actionable insights on data captured by the RSA NetWitness Platform.

RSA NetWitness Detect AI applies cloud-scale processing for behavior analytics and uses unsupervised machine-learning to detect and respond to threats without manual oversight.

The all-new SaaS solution provides high-fidelity, actionable insights on data captured by the RSA NetWitness Platform that empowers security teams to find, prioritize, and resolve threats faster and more efficiently.

“Security teams are grappling with siloed IT organizations, an expanding attack surface, and increasingly sophisticated threats.

RSA NetWitness Detect AI provides immediate value to security analysts in a turn-key solution, driving faster investigation into advanced cyber-threats and ensuring quicker and more comprehensive incident response,” said Michael Adler, Chief Product Officer for RSA’s Security Business Unit.

The sheer volume of attacks and bad actors operating today – including commodity malware, classic insider threats, state-sponsored exploits, and hacktivists – have made it increasingly difficult for security teams to quickly address cyber-threats.

This challenge is often exacerbated by inconsistent data formats, inconclusive monitoring technologies, and noisy detection platforms that simply don’t provide security analysts with the visibility or insights they need to perform their jobs.

RSA NetWitness Detect AI addresses those issues and extends the benefits of a SaaS solution: it provides continuous, high-fidelity, and high-value threat detection and monitoring without rules, signatures, or manual analysis, giving analysts the tools they need to resolve incidents quickly.

The solution, which scales to large organizations, easily integrates into the RSA NetWitness Platform and can be set up quickly, with limited additional resources required.

It uses unsupervised machine learning from the moment it’s activated, becoming smarter and more accurate as it assess threats at every step of the attack lifecycle, ensuring that analysts can prioritize critical incidents quickly and effectively.