Modern IT environments generate billions of logs and events every day across endpoints, networks, cloud services, and identity systems. Machine learning models can correlate these signals in near real-time to identify behavioral anomalies that may be buried in the noise, such as unusual login patterns, suspicious lateral movements, or data exfiltration attempts.
Many corporate security teams expect such capabilities to significantly improve detection capabilities. In a 2025 study conducted by Anvilogic in collaboration with the SANS Institute, 45% of respondents said their organizations are already integrating AI into their threat detection workflows. 88% believe AI will play a major role in detection engineering within the next three years.
Organizations are already using AI to automate many of the mundane tasks traditionally handled by Tier 1 and Tier 2 analysts, said Martin Sordilla, senior technology and security architect. Accenture. Much of this work involves reviewing logs, prioritizing alerts, identifying indicators of compromise, correlating events, and contacting system owners during investigations. He added that AI can significantly accelerate these processes and automate tasks such as alert triage, documentation, evidence collection, and chain-of-custody tracking.
