Exaforce

Exaforce takes the most aggressive architectural bet in the AI SOC lane: rather than overlaying agents on the tools a SOC already runs, it unifies the agent layer with its own integrated data layer, so investigations reason over complete telemetry rather than only what existing tools surface. The company closed a $125 million Series B in May 2026 behind its Exabots agents and a knowledge graph data layer, one of the largest rounds in the category.

The technical distinction is the multi model engine. Where most rivals are LLM only, Exaforce combines three layers, data ingestion models that normalize cloud and SaaS telemetry, behavioral machine learning that baselines normal activity, and large language models that reason and explain, so the platform can expand detection coverage and cut false positives rather than simply triaging the alerts other tools generate. It ingests real time telemetry into a knowledge graph, runs investigations with graph visualizations analysts can follow, adapts through feedback loops and natural language business context, and is explicitly positioned as a SIEM cost counterweight: by holding the data layer itself, it targets the storage and licensing spend that traditional SIEM stacks accumulate. Exabots agents handle triage, investigation, and detection across the unified layer, and the platform also underpins a managed MDR offering.

The tradeoff is the mirror image of the overlay analysts. Exaforce assumes ingestion, which delivers a unified detection layer and complete log visibility but carries migration work and operational drag during cutover, so time to value is measured in quarters rather than days. No pricing is published. For teams ready to consolidate the data layer in a new platform and willing to pay the migration cost for detection depth the overlay tools cannot reach, Exaforce is the architectural bet worth evaluating; for teams that need fast triage on the stack they already own, an overlay analyst lands sooner.