Stop unsafe AI outputs inreal time,before it reaches a user.
Block prompt injection, jailbreaks, sensitive-data exposure, and harmful content at the moment they occur. Every policy decision is automatically preserved as evidence.
See guardrails in action
AI risk happens at runtime. Your controls should too.
Every production AI system can encounter malicious prompts, sensitive data, unsafe tool requests, and harmful outputs. Static reviews and pre-launch testing cannot anticipate every interaction once the system reaches real users.
Organizations need to show which controls were active, how each interaction was evaluated, and what happened when a violation occurred. That evidence must come from the system enforcing the policy, not from documentation assembled afterward.
EU AI Act
ISO/IEC 42001
NIST AI RMF
OSFI E-23
50+
PII and PHI entity types detected and blocked inline
100%
of policy violations blocked before the output reaches a user
<10mins
to deploy a new runtime guardrail
Documenting a policydoesn't enforce it.
GRC tools document what an AI system is allowed to do, but they do not apply those rules to live inputs, outputs, and agent actions. Traditional security tools can monitor infrastructure, network activity, and sensitive data without evaluating the full context of an AI interaction.
Without runtime guardrails, prompt injection, data exposure, harmful responses, and unauthorized tool use may reach a model or user before security teams can intervene.
No control at the moment of interaction.
Detection often occurs after the request has reached the model or the response has reached the user. Nothing is positioned to inspect and stop the violation inline.
Traditional security controls were not designed for AI behavior.
Infrastructure and data-security tools can detect known threats without fully evaluating prompt injection, jailbreaks, agent actions, model responses, and conversational context.
Protection is inconsistent across AI systems.
A guardrail configured for one application does not protect every model, agent, or provider. Without shared controls, coverage depends on each team remembering to build and maintain its own safeguards.
Enforce policy, not just document it.
Guardrails use the same test definitions as evaluation and monitoring. What you test is what you enforce, and evidence is generated automatically.
Real-time protection for every AI interaction
01
Inline PII/PHI blocking
Detect and block sensitive data in prompts and responses before it reaches a model, external provider, application, or user.
02
Prompt injection defense
Identify and stop prompt-injection and jailbreak attempts as requests flow through.
03
Harmful and toxic output filtering
Block toxic, harmful, and malicious content inline based on configurable policy.
04
Runtime policy enforcement
Apply governance policies to live traffic so non-compliant behavior is stopped, not just recorded.
05
OWASP-aligned security coverage
Map guardrails and security tests to risks identified in the OWASP Top 10 for LLM Applications, with evidence connected to each applicable control.
06
Audit evidence by default
Record every policy evaluation, enforcement action, exception, and outcome with a timestamp, system version, owner, and supporting trace.
07
Automated remediation
Notify on-call teams, quarantine affected traces, disable access, or block downstream actions automatically when a violation occurs.
“For the first time, the same AI policies are being enforced consistently across every team and application.”
CISO, regulated enterprise
Trusted by regulated leaders: Sun Life and Gallagher (insurance); Rogers, KPN, and Comcast (telecom and media).
Backed by Y Combinator and Race Capital. SOC 2 Type II.
Founded by ex-Apple/Siri ML engineers.
Jericho Security: 6x deployment frequency and +53% throughput after standardizing on Openlayer.
Named in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms.
Endorsed by Guillermo Rauch (Vercel CEO) and Max Mullen (Instacart founder).
