APEX

Centralized visibility and monitoring across AI applications deployed on different providers (OpenAI, Anthropic, Google, etc.) and custom models. Aggregates logs, usage metrics, and performance data from heterogeneous AI infrastructure into a single pane of glass.

Real-time detection and blocking of prompt injection attacks before they reach AI models. Analyzes incoming prompts for malicious patterns, jailbreak attempts, and injection payloads at the point of interaction.

Implements rate limiting and quota controls to prevent abuse and manage resource consumption. Allows organizations to set limits on API calls, tokens, or interactions per user, team, or application.

Acts as a central gateway for AI interactions, providing a unified API interface and integration points for multiple AI providers and custom models. Handles request routing, authentication, and protocol translation.

Enables deployment and management of custom or fine-tuned AI models alongside third-party providers. Provides infrastructure for hosting, versioning, and monitoring custom models within the governance framework.

Analyzes user interaction patterns with AI systems to detect anomalies, unusual behavior, or potential insider threats. Tracks behavioral baselines and alerts on deviations that might indicate misuse or compromise.

Identifies and blocks attempts to circumvent AI model safety guidelines and restrictions. Detects patterns of model abuse including attempts to generate harmful content, bypass content policies, or extract restricted information.

Automatically generates compliance documentation and maintains immutable audit trails of all AI interactions. Supports regulatory requirements like HIPAA, SOC2, and other compliance frameworks by documenting data handling, access controls, and model decisions.

Monitors and enforces data handling policies to prevent personally identifiable information (PII) and sensitive data from being sent to AI models. Detects and blocks transmission of regulated data types before they reach external or untrusted models.

Implements granular access controls determining which users, teams, or applications can access specific AI models. Manages permissions, role-based access, and enforces least-privilege principles across AI deployments.

Tracks and analyzes costs and usage patterns across multiple AI providers and models. Provides visibility into spending, cost per interaction, and usage trends to optimize AI spending and identify cost anomalies.

Monitors AI model performance metrics including accuracy, latency, error rates, and output quality. Tracks degradation over time and alerts on performance anomalies that might indicate model drift or infrastructure issues.

Allows organizations to define and enforce custom policies and guardrails for AI usage. Enables configuration of rules around content types, data handling, model selection, and interaction patterns that align with organizational standards.

Provides real-time alerts and threat intelligence for security incidents related to AI usage. Detects suspicious patterns, potential breaches, and security threats with immediate notification to security teams.