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Developer-first security platform unifying code, cloud, runtime and AI pentesting with noise reduction and autofix.
Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
Free trial available
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
- ✦SAST, SCA and secrets scanning
- ✦Cloud misconfiguration (CSPM) and container scanning
- ✦AI-powered autonomous pentesting
- ✦AutoFix pull requests and auto-triage
- ✦Runtime and bot protection (Zen)
- ✦SOC 2 and ISO compliance support
- ✦LLM API router
- ✦OpenAI API proxy
- ✦Model aggregation (OpenAI, Gemini, DeepSeek, Llama, Qwen, Claude, etc.)
- ✦Unified OpenAI API standard
- ✦Unlimited concurrency
- ✦Deep-learning data classification (99.5% claimed accuracy)
- ✦Cloud DLP across SaaS applications
- ✦One-click deployment across apps, devices and users
- ✦End-user self-remediation of violations
- ✦Broad SaaS integrations
- ✦Insider-threat and breach monitoring
- ✦Encrypted credential vault injected only at approved endpoints
- ✦Agents run inside Trusted Execution Environments (encrypted enclaves)
- ✦Sandboxed tools in Wasm containers with capability-based permissions
- ✦Real-time outbound leak detection to block credential exfiltration
- ✦Rust codebase for memory safety
- ✦One-click cloud deploy on NEAR AI Cloud or self-host from source
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- →Finding and fixing code vulnerabilities
- →Securing cloud and containers
- →Running continuous pentests
- →Automating compliance evidence
- →Integrating multiple AI models into applications using a single API
- →Accessing the latest AI models through a unified interface
- →Managing and scaling AI model usage with unlimited concurrency
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements