RAIDS - Continuous AI Monitoring Platform
RAIDS AI LTD · Cybersecurity & IT
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
RAIDS AI provides continuous behavioral monitoring for AI systems operating in production environments, including LLMs, structured data, and forecasting models. 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 up to sub-100ms latency, enabling rapid identification of emerging issues in live AI deployments.
Show the rest of the publisher’s description (16 more lines)
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.
## Key Capabilities
- Independent and vendor-agnostic monitoring: Operates as an independent, third-party solution compatible with AI systems from any provider, enabling unbiased behavioral monitoring across multiple platforms without being tied to any one vendor.
- 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.
## Who Should Use This
RAIDS AI is designed for companies that develop and sell AI solutions, whether in healthcare, legal, financial services, HR, sales, customer service, security, or other industries.
RAIDS is a strong fit when:
- Enterprise customers are asking how you monitor and govern AI systems in production
- Security and procurement reviews are slowing down deals due to AI governance or risk-management questions
- You need to demonstrate alignment with frameworks such as the EU AI Act, ISO 42001, NIST AI RMF, or emerging AI assurance requirement
- You want to proactively showcase trust, transparency, and responsible AI practices before buyers ask for evidence
- You want a buyer-facing trust seal that provides evidence your AI is continuously monitored, assessed, and governed.
RAIDS enables AI vendors to turn governance and assurance into a competitive advantage, helping them to accelerate enterprise sales and strengthen customer trust.
Highlights
Highlighted by the publisher on AWS Marketplace.
Black-box, non-intrusive monitoring: Works with any AI system without modifying models or pipelines.
Real-time detection: Sub-100ms anomaly detection for rapid identification of behavioral deviations in production AI systems.
Compliance-ready oversight: Supports governance and regulatory readiness through continuous behavioral monitoring and documented alerts.
Agent build and provenance
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Plans and pricing as listed
2 listed- Units
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Refund terms
As stated by the publisher on AWS Marketplace.
Refunds per request.
Sources
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