As cybersecurity evolves and bad actors become more sophisticated, security teams must take a more proactive approach to Network Traffic Analysis (NTA) in order to avoid the next generation of hacks and breaches. But most NTA solutions are severely lacking in one foundational component: an accurate, generative baseline that evolves over time. Without this, truly meaningful anomaly detection is impossible. In this Whitepaper, we'll explore topics such as...

• Why traditional network baselines (which use Supervised first or second wave AI) are ineffective because they're based on historical data
• A baseline that evolves on its own over time built with "Self-Supervised" AI is the only way to do accurate anomaly detection
• Traditional SIEM approaches to cybersecurity are ineffective and additive in terms of cost and labor
• The advent of "Self-Supervised" AI based solutions that can learn on their own over time solve the problems presented by traditional solutions