For Security and GRC leaders
Your AI is in production. Your SOC can’t see it.
Every AI agent in your enterprise reads your data, calls your APIs, and talks to your customers. Your existing security stack was built for code, infrastructure, and traffic. None of it reads what the model actually said, what it reasoned, or whether a single output just put your name in a regulator’s inbox. Realm gives you runtime visibility, real-time enforcement, and the audit-grade evidence your board and your regulators ask for.
Prism
Detection & response for every model call. Token-level interpretability.
OmniGuard
Inline detection and response. Block, redact, rewrite, audit.
AGENTRealm
Runtime layer for agentic workflows. Audit every action.
The Problem
AI risk lands on your desk. Your security stack was built for a different surface.
You’re on the hook for AI risk before you have a single line of AI in your security stack. The board asks for the AI risk report. The auditor asks for evidence the AI controls are working. The CEO asks why the regulator’s letter mentions your product. And the tooling you have, SIEM, CASB, DLP, WAF, AppSec, was built for a different surface.
Your engineering team shipped an LLM-powered assistant six months ago. Your product team is rolling out an agent next quarter. Every one of those features is reading customer data, making decisions, and producing output that, in your regulator’s eyes, is the company speaking. You have no telemetry on what was said, no policy enforcement inside the model, and no continuous evidence to hand to the auditor.
The hard part is not whether AI failures happen. They do, in every enterprise running models in production. The hard part is that today, you can’t see them, can’t stop them, and can’t prove control effectiveness when asked.
Your engineering team shipped an LLM-powered assistant six months ago. Your product team is rolling out an agent next quarter. Every one of those features is reading customer data, making decisions, and producing output that, in your regulator’s eyes, is the company speaking. You have no telemetry on what was said, no policy enforcement inside the model, and no continuous evidence to hand to the auditor.
The hard part is not whether AI failures happen. They do, in every enterprise running models in production. The hard part is that today, you can’t see them, can’t stop them, and can’t prove control effectiveness when asked.
What Realm Delivers
Visibility your stack can't give you. Enforcement inside the model. Evidence the auditor accepts.
See every failure your gateway and guardrails miss.
Prism observes every model call in production. It catches what input filters and output filters cannot: failures that emerge from the model’s reasoning, not the strings. Hallucination, deception, policy drift, refusal failure, jailbreak success, prompt injection that slipped past the WAF. Token-level interpretability, not log lines.
Outcome: You see the 90% your existing stack misses. The incident report shows what happened, why, and which controls fired.
Outcome: You see the 90% your existing stack misses. The incident report shows what happened, why, and which controls fired.
Intervene before the user sees the output.
OmniGuard enforces inline. Allow, deny, redact, rewrite, identity-bind, rate-limit. Decisions land before the response is rendered, before the agent commits the action. Policy-as-code. Auditable. Built on Deep Neural Inspection, which reads the model’s reasoning rather than the output string.
Outcome: The failure becomes a blocked event in your audit log, not a customer-facing incident, not a regulator’s exhibit.
Outcome: The failure becomes a blocked event in your audit log, not a customer-facing incident, not a regulator’s exhibit.
Hand security the trace they can actually act on.
Every flagged and blocked event carries a forensic record: the prompt, the model’s internal state at the moment of deviation, the policy that fired, the enforcement action, the timestamp, the identity. Mapped continuously to EU AI Act, NIST AI RMF, ISO 42001, and OWASP LLM Top 10. Air-gapped deployment supported.
Outcome: Audit evidence stops being a six-week reconstruction. It becomes a dashboard export.
Outcome: Audit evidence stops being a six-week reconstruction. It becomes a dashboard export.
Prism
Runtime observability and interpretability. The MRI for AI behavior. Token-level explainability, not log lines.
OmniGuard
Inline detection and response. Allow / deny / redact / rewrite at the model layer. Identity-bound policy enforcement, full audit trail, regulatory citations baked in.
AGENTRealm
Runtime layer for agentic workflows. Trace every tool call, every memory write, every external action. Intervene before the agent commits.