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Signature-based solutions, which only defend against 20% of successful assaults previously reported, account for a large percentage of the hundreds of billions of dollars spent on cybersecurity each year. However, according to the Ponemon Institute, the remaining 80% of cyberattacks are unique threats that require strong AI capabilities to detect. The existing solutions are unable to cover this very gap.
This is where the California-based startup Mixmode comes in. MixMode is a cloud-native threat, and anomaly detection technology for high-volume data feeds that uses self-learning AI to discover sophisticated threats in any setting. It is being claimed that MixMode is capable of detecting a wide range of known threats and unknown and never-before-seen dangers such as zero-day assaults.
MixMode, led by CEO John Keister, provides a self-learning platform that allows businesses and government organizations to detect and protect their networks. This AI-driven, automated solution is designed to detect both known and unknown assaults, and it can be used in both public cloud and on-premise corporate systems of Fortune 500 size, according to the company.
Additionally, the advantage of the MixMode platform is that people aren’t necessary to constantly train the system on data, tune it, or write new rules, according to the company, which also feels that adding humans to this workspace could considerably reduce the speed. The company claims that it has devised a system the system watches, learns, sees what’s typical, sees what’s not, and adjusts accordingly instead of placing that on the customer to create new rules or do new settings in order for the system to work effectively.
MixMode recently announced a $45 million Series B fundraising round and plans to use this as an opportunity to use its self-learning, “third-wave” AI engine to protect clients against previously unknown threats.
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