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
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
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
- https://docs.lakera.ai/guard Project page · Documentation · Research reference
- https://docs.lakera.ai/docs/api Research reference
- https://www.lakera.ai/careers Research reference
Research metadata
- Research date
- 2026-10-01
- Catalogue snapshot
- 2026-10-06
Community & social 1 channels
Communities
- SlackMomentum by LakeraOfficialDiscussion