With the growing complexity of today’s IT ecosystem, identifying red flags in enterprise security quickly and efficiently is essential to lessen the impact of security breaches. To make things worse, attackers now implement different hacking methods like malware, social media attacks, personalized phishing emails, and many more combined to penetrate through security frameworks. Owing to the increasing sophistication of cyber attacks, information security teams are in dire need of the appropriate tools to correlate and process the massive volumes of both historical and real-time security data. Advanced data analytics is a measure to detect and defend enterprise IT against these advanced attacks correctly.
To keep pace with the emerging complicated attacks, cybersecurity teams must monitor their network for a wide range of threats that may not necessarily adhere to the conventional threat patterns. However, the sheer volume of data that organizations possess can help them stay ahead of potential breaches and attacks. The cascades of data accumulated from various disparate sources such as intrusion detection systems, application and server logs, network infrastructure, and more add up quickly increasing to petabytes in size. In the instance of a malicious intrusion, these datasets assist threat response teams to verify the validity and extent of the breach. On running queries in real time against the traditional streaming datasets, cybersecurity teams can efficiently identify possible threats. Analyzing the vast datasets, it becomes easier for threat detection teams to determine traditional security patterns, recognize a potential threat to anomaly detection, and flag it for further investigation.