Best AI Guardrails for Cursor

Enkrypt AI is an AI security and compliance platform built to protect generative AI applications, autonomous agents, models, data, and AI infrastructure. It provides security agents and controls for detecting vulnerabilities, preventing threats, continuously monitoring AI systems, and automating compliance processes. Enkrypt AI's red teaming capabilities dynamically generate use-case-specific attacks that adapt during testing to uncover security, safety, and brand risks that static evaluations may miss. The platform addresses threats including prompt injection, jailbreaking, data leakage, model inversion, adversarial attacks, policy violations, hallucinations, model drift, bias, toxic content, deceptive behavior, and unmonitored agent actions. Real-time guardrails inspect AI interactions and can stop threats as they occur while continuously adapting based on prompts and observed activity. Monitoring capabilities provide ongoing visibility into AI risk and compliance so organizations can identify issues, track protection effectiveness, and maintain evidence for audits. Enkrypt AI can translate internal organizational policies and external regulatory requirements into automated guardrails to reduce manual compliance work. Its product portfolio includes Agent Red Teaming, Agent Guardrails, an Agent Policy Engine, AI Data Risk Audit, MCP Scanner, and MCP Gateway, with solutions for customer-facing agents, frontier models, third-party AI risk, AI compliance, MCP security, and secure AI development. The company also maintains an LLM Safety Leaderboard that continuously red teams and benchmarks more than 200 AI models to help organizations evaluate model security and safety.

When engineering standards live in wikis, tickets, and CI templates, they are difficult to apply consistently across a growing software organization. Earthly Lunar gives platform engineering teams a central way to enforce those standards across different repositories, pipelines, and developer workflows. It gathers evidence from source code and CI/CD execution, normalizes it into a service-level view, and runs deterministic guardrails against that data. A guardrail might require test coverage, an approved dependency, an SBOM, or a deployment check. Teams can introduce policies in a visibility-only mode, surface findings on pull requests, and move to blocking checks when ready. Developers get actionable feedback on proposed changes while platform leaders see adoption across the organization. Lunar includes a library of 200+ guardrails and supports custom policies for company-specific requirements, including lessons from incidents. Continuous results provide a record of what was checked and when, reducing the work of gathering compliance evidence.