Protect AI vs Arthur AI
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Protect AI is an enterprise AI security platform that combines model scanning, scalable AI red teaming, and runtime threat detection to help organizations assess and mitigate risks across model formats and AI application types including RAG systems and agents.
Model and agent evaluation and monitoring platform with dashboards, alerts, guardrails and a transparent Premium plan for small teams plus enterprise options.
Feature Tags Comparison
Key Features
- Guardian scanning: Scan models for security issues across major model formats with checks targeting threats like backdoors and unsafe deserialization
- Recon red teaming: Run scalable AI red teaming and vulnerability assessments to surface risks before launching AI apps to production
- Layer runtime detection: Use runtime scanners to detect attack patterns and protect AI apps including RAG systems and agents in production
- Unified platform: Operate Guardian Recon and Layer within one platform to align findings and workflows across teams
- Integration emphasis: Product pages highlight integration with existing scanners and environments to fit into current security programs
- Pre production decisions: Use Recon insights for model selection and evaluating the effectiveness of existing defenses
- Dashboards for model and agent KPIs with version comparison
- Custom metrics and slices to track drift and fairness
- Real time alerts via webhooks email and chat
- Agent traces showing tool calls outcomes and errors
- Guardrails and policy checks for safer responses
- Free, Premium, and Enterprise deployment options
Use Cases
- Model intake review: Scan third party models before deployment to catch unsafe formats and known threat patterns early
- Pre launch testing: Red team an AI app to identify prompt injection and misuse risks then prioritize mitigations before go live
- Runtime monitoring: Detect hostile prompts or suspicious behavior patterns in production AI systems including RAG and agent flows
- CI security gates: Add model scanning into build pipelines so releases fail when risk thresholds are exceeded
- Vendor governance: Evaluate model providers with consistent scanning and test reports for procurement and audit
- Incident response: Use findings and logs to triage suspected AI attacks and coordinate remediation across ML and security teams
- Track LLM answer quality and escalate low confidence cases
- Monitor drift and fairness for credit or risk models
- Alert ops when agent tool calls fail or exceed latency
- Compare model or prompt versions before full rollout
- Export reports for audits and leadership reviews
- Correlate traffic spikes with error clusters to triage
Perfect For
appsec engineers, ml engineers, mlops teams, security architects, governance and risk leaders, product owners shipping ai features, enterprise teams with production rag or agent systems
MLOps leaders, platform teams, and product owners who need evaluation, monitoring, and governance to scale models and agents responsibly
Capabilities
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