Traditional SIEMs force everything into one monolithic box: collect, store, correlate, detect. That model breaks under modern data volumes and attacker speed. Abstract breaks the monolith apart, decoupling data collection, detection, retention, and AI-enabled security operations into independent building blocks that work together without vendor lock-in. What that means in practice: Security teams can ingest telemetry from cloud, SaaS, network, endpoint, and identity sources, enrich and normalize it in-stream, and route it where it needs to go before it becomes expensive or rigid downstream. Detections run in the data stream itself, compressing mean time to detect from hours to seconds. Storage strategy aligns with actual operational and compliance needs, not one-size-fits-all indexing that drives runaway costs. No pipeline engineers required. No rip-and-replace. Abstract works alongside existing tools, including Splunk, Sentinel, CrowdStrike, SentinelOne, Elastic, and Palo Alto Cortex XSIAM, so teams can modernize incrementally. Continuous detection content, threat intelligence, and CVE-specific rules are delivered by ASTRO, Abstract's internal threat research team, so customers aren't writing rules from scratch on day one. Abstract is built for security operations teams ready to move beyond legacy SIEM: faster detection, lower cost, and less operational overhead, without sacrificing coverage.