Modern IT environments can generate billions of logs and events each day across endpoints, networks, cloud services, and identity systems. Machine learning models can correlate those signals in near real-time, and identify behavioral anomalies — such as unusual login patterns, suspicious lateral movement, or data exfiltration attempts — that might otherwise remain buried in the noise.
Many enterprise security teams expect such capabilities to significantly bolster their detection capabilities. In a 2025 survey that Anvilogic conducted in collaboration with the SANS Institute, 45% of respondents said their organizations have already integrated AI into their threat detection workflows; 88% believed AI would play a major role in detection engineering within the next three years.
Organizations are already using AI to automate many of the routine tasks traditionally handled by Tier 1 and Tier 2 analysts, says Martin Sordilla, senior technology and security architect at Accenture. Much of this work involves reviewing logs, triaging alerts, identifying indicators of compromise, correlating events, and reaching out to system owners during investigations. AI can significantly accelerate these processes — automating tasks such as alert triage, documentation, evidence collection, and chain-of-custody tracking, he adds.
