Legacy SIEMs were built for log aggregation, not autonomous threat detection. They demand endless rule maintenance, produce thousands of false positives, and require dedicated engineering teams to keep running. Alaris is the AI-native alternative, self-learning, zero-maintenance, and built for the speed of modern attacks.
Legacy SIEMs were built when attacks moved slowly and teams had time to write detection logic. Today, adversaries dwell for hours, not weeks. Alaris was built for this reality, using AI that adapts continuously rather than rules that go stale the moment they're written.
Most teams running a legacy SIEM spend more engineering time maintaining it than investigating threats. Every new source, attack vector, or false-positive wave means another round of parsers, rules, and tuning. Alaris eliminates this entirely. Self-learning models adapt to your environment automatically, freeing your team to focus on security instead of tooling.
When 90% of what your SIEM surfaces is noise, the real threats hide in plain sight. Analyst fatigue sets in, alert thresholds get raised, and the 1% of genuine incidents go uninvestigated. Alaris inverts this model, AI handles the noise layer completely, delivering only verified, evidence-backed detections to analysts. Every alert your team touches is worth their time.
Eliminate alert fatigue, cut detection time from hours to minutes, and stop paying a team to maintain rules that don't work. See what AI-native security looks like.