RAIDS - Continuous AI Monitoring Platform

RAIDS provides continuous behavioral monitoring for AI systems operating in production environments. Unlike traditional AI observability tools that focus on performance metrics, RAIDS analyzes AI inputs and outputs to detect deviations from normal system behavior as they occur.

Using deep learning-based anomaly detection, RAIDS establishes a baseline of typical AI behavior and continuously monitors for anomalies that may indicate security risks, operational failures, data misuse, or unintended system behavior. Detection occurs in real time with sub-100ms latency (23ms average), enabling rapid identification of emerging issues in live AI deployments.

RAIDS operates as a black-box, non-intrusive solution, monitoring AI systems in parallel without introducing additional overhead or changes to existing models, pipelines, or workflows. This ensures continuous oversight without affecting operational performance. The platform is independent and vendor-agnostic, allowing organizations to monitor AI systems from any provider using a consistent, unbiased approach. Human-in-the-loop feedback is used to continuously refine behavioral baselines and improve detection accuracy over time.

• Independent and vendor-agnostic monitoring: Operates as an independent, third-party solution compatible with AI systems from any provider, enabling unbiased behavioral monitoring without vendor lock-in.

• Black-box, non-intrusive monitoring: Analyzes AI inputs and outputs without requiring access to model internals, training data, or proprietary algorithms. Operates in parallel with production systems, introducing no additional overhead or disruption to operations.

• Real-time anomaly detection: Sub-100ms detection latency for tabular AI workloads, with near-real-time detection for LLM-based systems, enabling rapid identification of behavioral deviations in production environments.

• Behavioral baselining: Learns typical system behavior over time and detects both sudden anomalies and gradual behavioral drift.

• Human-in-the-loop learning: Incorporates feedback to continuously refine behavioral baselines, improving detection accuracy and reducing false positives over time.

RAIDS is designed for organizations operating AI systems in production, including:

• Chief Information Security Officers (CISOs) responsible for managing AI-related security and risk exposure

• Heads of AI / Machine Learning accountable for reliable and predictable AI behavior across deployments

• Platform and Engineering Teams operating AI infrastructure that requires continuous monitoring without system disruption

• Enterprises in regulated or high-risk environments requiring ongoing oversight of AI behavior