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AI red-teaming and security platform for LLMs, agents and GenAI apps, offering continuous testing and remediation.
Enterprise AI platform for redacting, anonymizing, and governing sensitive data across documents and AI workflows.
Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
AML and fraud compliance platform pairing transaction monitoring, screening, and explainable AI agents for financial institutions.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
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No public pricing
No public pricing
- ✦AI threat modeling for your stack
- ✦Continuous autonomous red-teaming
- ✦Auto-generated patches and remediation reports
- ✦Coverage for agents, MCP, LLMs and GenAI apps
- ✦Managed service or self-serve platform
- ✦AI-powered document redaction
- ✦Real-time data anonymization
- ✦AI guardrails for generative-AI apps
- ✦Automated compliance enforcement
- ✦Industry-specific solutions for government, legal, and healthcare
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- ✦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
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Red-team custom AI agents and copilots
- →Detect prompt injection and agent hijacking
- →Harden GenAI apps before shipping
- →Continuously assess AI security posture
- →Automating FOIA and public-records redaction
- →Protecting privileged data in eDiscovery
- →Preventing data leakage to AI systems
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →AML compliance and monitoring
- →Reducing false-positive alerts
- →Streamlining fincrime investigations and SAR filing
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects