By tailoring threat intelligence to the specific concerns of the analysts consuming it, organizations can reduce alert fatigue and achieve higher levels of security maturity.
SOC analysts face blizzardlike conditions during tax season as false positives pile up and legitimate tax fraud alerts are liable to fly under the radar.
To protect sensitive information, security professionals must know exactly where this data resides, who is accessing it, when it is being accessed and whether this activity violates policy.
Security-as-a-service can help teams sift through the overwhelming volume of alerts more efficiently, enabling them to focus on the threats that are most likely to require intervention.
Machine learning can be a boon for businesses, but effective machine learning must help analysts cut through the noise with few false positives.
It can be hard to determine false alarm security events with SIEM solutions. Through IBM's new partnership with AlgoSec, your team has a new tool to help.
New enhancements to IBM's application security testing solutions enable clients to simplify SAST, remediate open source vulnerabilities and more.
Machine learning systems like A12 are designed to augment human analysis with cognitive intelligence, enabling IT professionals to reduce false positives.
Excessive false positive detection can impair users' ability to perform basic functions or administrative actions, producing results akin to an attack.
Legacy anti-malware solutions tend to lack context, resulting in false-positive alerts that are ignored and real threats that are overlooked.