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Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
AML and fraud compliance platform pairing transaction monitoring, screening, and explainable AI agents for financial institutions.
Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
AI-driven network detection and response platform that identifies and stops identity-based and lateral-movement cyberattacks in real time.
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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
- ✦Real-time transaction monitoring and rule engine
- ✦Explainable AI forensics agents
- ✦Dynamic risk scoring
- ✦Watchlist/sanctions/PEP screening
- ✦AI-native case management
- ✦Automated SAR filing to FinCEN and 70+ GoAML countries
- ✦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
- ✦Real-time AI-driven threat detection beyond traditional EDR
- ✦Detection of identity-based attacks and lateral movement
- ✦360 Response for enforced containment across identity, devices, and network
- ✦Exposure management and security posture improvement tools
- ✦Managed detection and response (MXDR/MDR) services
- ✦Integrations across existing security tool ecosystems
- ✦Attack Labs research sharing threat intelligence and techniques
- →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
- →AML compliance and monitoring
- →Reducing false-positive alerts
- →Streamlining fincrime investigations and SAR filing
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment
- →Security operations teams needing detection beyond EDR/SIEM gaps
- →Enterprises defending against identity-based and hybrid cloud attacks
- →Organizations needing managed threat detection and response services
- →Finance, healthcare, and public sector teams meeting compliance-driven security needs