Today, most of the alerts are managed manually, creating an extra burden on security teams in different organizations. Security teams deal on average with 11,000 alerts daily. Of them, 18% are reviewed manually, 32% are false positive, 28% are ignored, and only 17% are automated. Due to manual processes, a lot of security team members burn out and quit jobs taking their knowledge and experience out of the company. Onboarding new specialists and bringing them up to speed takes time and effort, leaving loopholes in IT security systems unfilled for a while.
The Client was looking to build a solution that would allow cybersecurity teams to manage the flow of alerts in an automated way and triage them to identify the most critical ones that require immediate attention and investigation.
Our rinf.tech team built a Cognitive Automation Platform that uses deep learning and user feedback to automate security alerts triage and management.
A unique mix of capabilities enables the platform to
The solution allows cybersecurity teams to focus more on hunting, investigating, and responding to real threats without the burden of false positives and irrelevant alerts.
Our deep learning model captures the expert knowledge through feedback and encapsulates it, allowing the knowledge of all previous and current cybersecurity specialists to be used in decision-making.