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Multi-Model AI

Deterministic, explainable AI for precision SecOps

As both log volume and attacks increase with AI, Exaforce’s multi‑model architecture (Semantic, Behavior, and Knowledge Models) makes LLM‑driven decisions predictable, auditable, and aligned to your business, cutting noise while raising confidence across detection, triage, investigation, and response.

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How multi‑model AI makes LLMs ready for production

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The Semantic Model builds and maintains relationships between identities, resources, and actions, creating a living map of your environment for fast, precise reasoning. It stores context in a structured form, so the system interprets intent, understands dependencies, and reacts with clarity instead of guesswork. You gain faster decisions, higher fidelity outputs, and a foundation that strengthens as complexity grows.

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The Behavioral Model continuously establishes what “normal” looks like across every entity, including users, identities, resources, and devices, and quantifies deviations using explainable anomaly scores. Rather than relying on single-dimensional baselines that generate false positives, it evaluates multi-dimensional co-occurrence across factors such as location, time of day, entity attributes, and associated risk to produce comprehensive and accurate anomaly scoring.

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The Knowledge Model blends deep technical expertise, curated external intelligence, and intimate awareness of your environment. It draws from LLM reasoning, historic decisions, and hard-coded business rules to understand how your applications, infrastructure, and policies actually operate day to day. High-level intents are broken down into precise checks across the Semantic and Behavior layers, then reported back as clear summaries with supporting evidence, so analysts and auditors see not only the answer but the why behind it.

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Multi-model AI replaces ad-hoc prompting with governed reasoning for consistent, auditable results. The Semantic, Behavior, and Knowledge Models work within your guardrails, with explicit data scope, curated context, and full logging. You get focused, human-like interaction tailored to your business, not a general-purpose chatbot.

With Exaforce's Exabots, we've accelerated detection, threat hunting, response, and investigations while expanding coverage without adding headcount. Our team uses Exabot's natural language search to get actionable answers about security events and security posture in a single tool instead of juggling disparate interfaces and query languages. Exaforce has become a high-trust partner, and the platform has transformed how we run security operations.

Steve Mancini

CISO, Guardant Health

Frequently asked questions

How does multi‑model AI make LLMs more deterministic?
By separating deterministic tasks from probabilistic reasoning and eliminating common LLM failure modes. The Semantic Model handles factual entity resolution using algorithmic graph traversal. The Behavioral Model calculates anomaly scores using statistical ML. This eliminates three critical failure modes: (1) No hallucination of facts. LLM receives validated entities and calculated scores as input, never guesses, (2) No inconsistent scoring. Anomaly scores are deterministic algorithms, not LLM estimation that varies run-to-run, (3) No context overload. LLM reasons over structured summaries instead of gigabytes, where details get lost.
How is the Knowledge Model different from “just an LLM”?
The Knowledge Model isn’t a standalone language model; it’s part of a model network. It’s the reasoning layer that synthesizes context from across Exabot’s system. It continuously ingests signals from the Semantic Model (what entities, identities, and assets mean in your environment) and the Behavioral Model (how they normally act and interact). Together with Business Context (your org structure, policies, and roles) and Historical Context (past detections, user confirmations, and outcomes), it feeds the Knowledge Model precise, relevant inputs.
How is the behavioral model different from UEBA?
Traditional UEBA was built for human users and fails to address three core limitations that Exaforce solves. First, single-dimensional baselines generate false positives by flagging benign behavior changes. Exaforce evaluates multiple signals together and alerts only when rare, threat-relevant behavior patterns occur. Second, legacy UEBA has cloud and SaaS identity blind spots. Exaforce is cloud-native and establishes accurate baselines for identities, including shared IAM roles, service accounts, and federated users. Third, traditional UEBA requires months of manual tuning. Exaforce adapts automatically within hours as legitimate behavior changes.
What guardrails keep the system safe?
Data scope, policy constraints, and reason‑step logging. The Knowledge Model cannot reason outside your approved sources, and every action is recorded for oversight.
Will this work with my existing SIEM/EDR/IDP stack?
Yes. The models reason over signals from cloud, SaaS, identity, code, and endpoints, integrating alongside your current tools to improve accuracy and reduce noise.

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