AI SOC platforms

An AI SOC platform applies artificial intelligence to the work of a security operations center (SOC), which means detection, alert triage, investigation, and response. The label covers a wide range of maturity, from a chat assistant bolted onto an existing console through to systems that carry an investigation to a verdict without an analyst driving each step. If the category itself is new to you, start here.

Exaforce sits at that second end. It is an agentic SOC platform, built so that task-specific AI agents called Exabots do the work rather than suggest it. Exabot Detect surfaces signal, Exabot Triage clears noise, Exabot Investigate builds the case, Exabot Respond acts, and Exabot Hunt supports proactive threat hunting. Analysts oversee the agents and keep authority over the decisions that need it.

What separates a platform that holds up in production from one that demos well is where the reasoning happens. Point a frontier model at raw log text and you inherit its weaknesses, including inconsistent reasoning between runs, hallucination, and cost that climbs with data volume. Exaforce resolves events against structured context at ingest, so the AI reasons over an understood environment rather than parsing text and guessing.

That context comes from Multi-Model AI, three specialized models working together. The Semantic Data Model resolves entities and relationships into a living map of the environment. The Behavioral Model learns normal activity across identities, both human and machine, along with applications, data, resources, and locations. The Knowledge Model synthesizes the two and turns findings into actionable intelligence.

Underneath sits the Data Platform, handling real-time ingest, dedupe, normalization, and correlation at cloud scale with intelligent storage tiering. Above it, Advanced Data Explorer gives analysts a BI-style interface across logs, identity, configuration, code, and threat intel, queryable in natural language. The full architecture walks through each layer.

Exaforce covers identity, IaaS, SaaS, endpoint, email, and insider risk. It replaces the stack of traditional SIEM, SOAR playbooks, and MDR contracts that most teams currently pay for separately, and it is not itself a SIEM. Mid-market teams commonly run it alongside an existing SIEM first and consolidate later. Where the old stack leaned on brittle scripts, AI SOC automation takes a different approach.

Customers report 90% fewer false positives, a 95% reduction in mean time to investigate (MTTI), and more than $600K in average savings against a traditional SOC stack. Forcepoint auto-triages 95% of alerts as verified false positives and holds a 14-minute mean time to respond (MTTR) on P0 incidents. Forcepoint case study

Terms used on this page are defined in the AI SOC glossary, and the FAQ covers the questions that come up in evaluation.