Check Point AI Guardrails vs Openlayer Guardrails

Check Point AI Guardrails analyzes large-language-model inputs and outputs for prompt injection, data leakage, policy bypass attempts, unsafe content, and related AI threats. It provides threat intelligence, analytics, and developer tooling for securing AI applications. Deployment can be on premises or in a private cloud, with processed personal data kept in the customer’s controlled environment except for agreed support or maintenance. Check Point Firewalls can apply the service to generative and agentic AI traffic, including content moderation and MCP tool responses. Listed supported services include OpenAI, Claude, Gemini, and Mistral AI through developer APIs. A Copilot Studio integration combines runtime guardrails with data-loss prevention and threat prevention. The cloud service may process prompts, messages, tool information, enabled uploads, model outputs, and security analytics. Pricing is on request; retention lasts through the subscription and three months afterward.

Openlayer Guardrails inspects live AI inputs and outputs and can block, redact, escalate, or route policy violations for review. It targets prompt injection, jailbreak attempts, exposure of personal or health information, and harmful or toxic content. Openlayer says it detects and blocks more than 50 PII and PHI entity types inline. Teams can configure sensitive-data categories to block and topics an assistant should avoid. Guardrails are commonly enforced through the Gateway or applied directly through the SDK; connections are also available through framework integrations, a CLI, and REST API. Each decision can be recorded with its timestamp, system version, owner, supporting trace, and reason for blocking. Openlayer says guardrail activity contributes to compliance evidence and names the EU AI Act, ISO/IEC 42001, NIST AI RMF, and OSFI E-23. Deployment options include customer VPC and air-gapped environments; on-premise deployment is available on Enterprise. Basic is free, with one member, five projects, one inference pipeline per project, 20,000 monthly logs, 20 tests per project, one workspace, three months of retention, and Discord support. Enterprise pricing is custom.