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AI security & guardrails

Lakera Guard / Check Point AI Guardrails

Model-independent API screening with configurable policies across prompts, outputs and agent tool interactions.

Overview

Research summary

Lakera Guard is the runtime protection offering now documented as Check Point AI Guardrails. Applications send user input, model output and agent interactions to the Guard API, which evaluates them against configured policies and returns detection results. Coverage includes prompt attacks, data leakage, content violations, malicious links and selected off-policy agent actions.

Teams remain responsible for deciding how their application responds to flagged interactions. The product offers cloud and self-hosted deployment options under commercial terms. It is available as a standalone runtime layer and within the broader Check Point AI Agent Security product, which adds discovery and risk assessment.

Repository summary

Stars
Unavailable
Open issues
Unavailable
Last push
Unavailable
Commits, 90 days
Unavailable
Repository activity
Not scored
Version
Unavailable

Recorded catalogue figures. View repository data and provenance →

Classification

Pricing & services

Paid services unknown

Whether the provider offers paid products or services has not been established.

Licence scope

Proprietary

Implementation

Recorded implementation details and interfaces for Lakera Guard / Check Point AI Guardrails.

Implementation details

Commercial Guard API and policy dashboard; cloud or self-hosted runtime. Server implementation language is not publicly specified.

Languages
Not public
Repository type
none

Recorded interfaces and capabilities

Licence scope

Proprietary

Repository

Repository snapshots, release information and recorded maintenance signals.

Repository snapshot

No GitHub repository is recorded for this entry.

Stars
Unavailable
Open issues
Unavailable
Last push
Unavailable
Commits, 90 days
Unavailable
Repository activity
Not scored
Archived
Not recorded

Repository activity is a snapshot, not a quality or popularity ranking. It combines recent-push freshness (50%), 90-day commits (30%) and issue pressure (20%).

Maintenance and provenance

Catalogue snapshot
2026-10-06

Documentation

Recorded references and research provenance for this entry.

Recorded sources 3

Research metadata

Research date
2026-10-01
Catalogue snapshot
2026-10-06

Community & social 1 channels

Communities