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RAXEAI

RAXEAI is a runtime security platform for LLMs and AI agents, delivering multi-layer detection and policy enforcement to give teams full visibility and governance over AI call risks.
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LLM runtime securityAI agent protectionprompt injection detectionAI API gateway securityjailbreak & injection blockingon-prem AI securityAI security policy governance

Features of RAXEAI

AI API reverse-proxy gateway that inspects every request and enforces policies.
Host-level sensor that flags prompt injection, jailbreaks and data exfiltration at runtime.
In-app SDK for inline checks inside popular LLM and agent frameworks.
Hybrid rule + ML detection covering known threats and anomalous behavior.
Granular actions: ALLOW, FLAG, BLOCK, LOG for staged risk mitigation.
Risk scoring and structured alerts to speed up incident analysis.
Audit logs and governance dashboard for tracing policy hits and posture.
Built-in virtual key manager with rotation, revocation and usage audit.
Token tracking, budget caps and spend alerts for AI cost control.
Uncovers unauthorized AI API usage to surface Shadow AI risks.

Use Cases of RAXEAI

Pre-flight an internal AI assistant in LOG-ONLY mode to map prompt-injection & jailbreak exposure.
Real-time inspection of inputs/outputs for customer-service bots and knowledge Q&A systems.
Central policy blocking & audit when AI agents call external tools or model endpoints.
Consolidate access rules, keys and call logs across multiple models via a single gateway.
Deploy runtime shields on-prem or in private clouds for data-sensitive teams.
Platform engineers roll out detection as K8s Sidecar or DaemonSet with zero code changes.
SecOps tune rules and thresholds continuously using alerts, risk scores and dashboards.

FAQ about RAXEAI

QWhat is RAXEAI?

RAXEAI is a runtime security platform for LLMs and AI agents that detects and remediates risks across the AI call chain.

QWhich risk types does RAXEAI detect?

It focuses on prompt injection, jailbreak bypass, agent hijacking, data exfiltration and privacy leakage at runtime.

QHow does RAXEAI handle risky requests?

Policies can ALLOW, FLAG, BLOCK or LOG; teams can start in monitor mode and move to enforced blocking.

QWhat deployment options are supported?

Gateway, host and in-app layers: K8s DaemonSet, Sidecar, Systemd service or direct SDK integration.

QWhich models or ecosystems can RAXEAI connect to?

OpenAI, Anthropic, Azure OpenAI, Gemini, Bedrock and common LLM/agent frameworks are listed as compatible.

QDoes RAXEAI provide audit and observability?

Yes—structured detection results, risk scores and full logs enable audit trails and policy tuning.

QHow does RAXEAI approach privacy and data handling?

Materials emphasize local or private-environment processing, suiting teams with strict data-residency rules.

QHow can I get pricing or trial access?

The site routes visitors to “Contact Engineer / Request Demo”; POC scope and commercial details are confirmed with the team.

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