ConfidenceAI

ConfidenceAI is an enterprise-grade, regulator-ready LLM runtime-security platform. It sits between your app and the model to inspect prompts and responses in real time, apply policy decisions, and log everything—whether you deploy on-prem, in a private cloud, or fully air-gapped.

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ConfidenceAILLM security platformPrompt injection protectionEnterprise AI governanceAir-gapped AI securityPrivate LLM deploymentLLM audit logsAI policy enforcement

Features of ConfidenceAI

Inline inspection and policy enforcement between app and model

Rule-based and semantic risk analysis on both prompts and responses

Three action outcomes: Allow, Block, Flag

Run modes: enforce, shadow, bypass

RBAC plus threshold-based policy management

Single control plane for events, roles, webhooks and audit logs

Deploy on bare-metal, private cloud, Kubernetes, Docker

SIEM/SOC ready with standard log formats

Use Cases of ConfidenceAI

Keep LLM traffic inside a local network for government or finance workloads

Observe risk events in shadow mode before launching an AI assistant

Block prompt-injection attempts or policy-violating outputs in production

Apply tiered security policies by team, model or endpoint

Stream audit logs to your SIEM for centralized alerting and forensics

Reuse one policy set across multiple models to avoid config sprawl

Run an AI security gateway in air-gapped or restricted networks

FAQ about ConfidenceAI

QWhat is ConfidenceAI?

ConfidenceAI is an enterprise runtime-security layer that sits between your application and any LLM, detecting risks and enforcing policies on every interaction.

QWhat risks does ConfidenceAI address?

It focuses on prompt injection, data leakage (including PII), policy violations, and anomalous behavior.

QHow does ConfidenceAI process a single LLM request?

Each request goes through rule/pattern matching, semantic analysis, risk scoring, and a final decision—Allow, Block, or Flag.

QWhere can ConfidenceAI be deployed?

You can deploy it on-prem, in a private VPC, or as Kubernetes sidecars/DaemonSets and Docker containers.

QCan ConfidenceAI monitor without blocking?

Yes—use shadow mode for observation only, or enforce mode to actively block requests.

QDoes it integrate with existing SOC workflows?

Yes, it exports standardized logs and events that feed directly into SIEM/SOC tools.

QAre performance benchmarks published?

Marketing materials mention low latency and high single-CPU throughput, but you should validate against your own workload and the latest official docs.

QWhere can I find pricing or edition details?

No public pricing is listed; contact the ConfidenceAI sales team or check the website for up-to-date plans and quotes.

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