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Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
Automated AI red-teaming platform that discovers, tests, and defends AI models and agents against security threats.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦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
- ✦Automated AI red teaming
- ✦AI discovery and attack-surface mapping
- ✦Agent-native reconnaissance
- ✦Exploitable-risk detection and reporting
- ✦Runtime AI protection and response
- ✦CI/CD, Burp Suite, and API integrations
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment
- →Testing AI systems against evolving attacks
- →Finding vulnerabilities in models and agents
- →Securing agentic workflows in production
- →AI governance and compliance reporting