What is signal analytics?
Signal analytics involves analyzing data patterns or signals to extract meaningful insights, trends, or predictions. These signals come from a variety of sources, including network traffic, log files, endpoint devices, threat intelligence feeds, security tools, and more. In practice, signal analytics typically include several stages — for example, data collection, data analysis, and data interpretation — with the process relying heavily on artificial intelligence (AI) and machine learning algorithms to distill down signals into actionable insights.
Organizations can use signal analytics to help identify irregular patterns or suspicious behavior in data streams, which helps with detecting and even preventing cybersecurity threats like malware infections, phishing attacks, and insider threats. Additionally, cybersecurity teams can leverage signal analytics to help with investigating security incidents and conducting digital forensics to determine the scope, impact, and root cause of security breaches. For example, with signal analytics, these teams can reconstruct attack timelines and identify indicators of compromise (IOCs) to enhance response and remediation efforts.
Moreover, when organizations have access to historical data, they can use signal analytics to review the data and help prioritize security vulnerability patching, predict future cybersecurity trends, and better manage the risk of exploitation.
Overall, signal analytics helps organizations make more informed, data-driven decisions, engage in more proactive risk management, and improve operational efficiency by pinpointing weaknesses and gaps, and streamlining processes.
In Netenrich
At Netenrich, we combine a data analytics approach with AI and large language models (LLMs) at Google petabyte scale and sub-second speed with one year of “hot data” across SecOps infrastructures. In the context of signal analytics, this level of hot data retention is essential as it ensures that relevant data is readily accessible for analysis and decision-making.
