HiddenLayer AISec Platform
AI security platform protecting agentic, generative, and predictive AI across the full lifecycle.
Info last updated on May 23, 2026
Vendor Information
HiddenLayer AISec Platform Overview
HiddenLayer's AISec Platform provides comprehensive security for AI systems across the entire machine learning lifecycle from development through production. The platform's Machine Learning Detection and Response (MLDR) capability, the world's first, automatically detects and prevents sophisticated cyberattacks targeting ML models. Unlike competitors requiring access to raw data or algorithms, HiddenLayer's non-invasive approach analyzes only mathematical representations of model inputs and outputs, protecting intellectual property without workflow changes.
Platform 2.0 introduces Model Genealogy for complete lineage tracking and automated AI Bill of Materials (AIBOM) generation that catalogs all components, dependencies, libraries, frameworks, and datasets for full AI supply chain visibility. The platform protects against all 64 MITRE ATLAS attack types including inference attacks, model extraction, jailbreaking, model poisoning, and data poisoning, with automated red teaming and runtime monitoring. HiddenLayer integrates seamlessly with TensorFlow, PyTorch, scikit-learn, and major cloud providers, and is exclusively selected by Microsoft as the sole scanning tool in Azure AI Studio.
Founded in Austin, Texas in 2022, HiddenLayer has raised $56 million from M12 (Microsoft's Venture Fund), Moore Strategic Ventures, IBM Ventures, and others. The company won RSA 2024 Innovation Sandbox as Most Innovative Startup, achieved zero bypasses in DEF CON red-team testing, and serves Fortune 100 customers including U.S. Air Force and Space Force. HiddenLayer holds SOC 2 Type II certification with NIST, MITRE ATLAS, and FedRAMP alignment.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Detects and throttles adversarial query patterns designed to reconstruct model weights or replicate model behavior through repeated inference.
Embeds imperceptible markers in model outputs enabling detection of unauthorized model copying, redistribution, or derivative deployment.
Applies rate limiting, anomaly detection, and abuse pattern blocking to model inference endpoints, distinct from general API security.
Evaluates model behavior against adversarial input perturbations (FGSM, PGD, CW attacks) to quantify robustness before production deployment.
Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.
Maps what data (including PII, PHI, and IP) flows into and out of AI models and APIs, identifying unintended exposure of sensitive data to external LLM services.
Detects adversarial inputs designed to override AI system instructions, extract training data, or manipulate model outputs. Detection approaches range from pattern matching to secondary model evaluation.
Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.
Identifies AI systems processing sensitive or regulated data without appropriate controls: unencrypted PII in model inputs, PHI flowing to external APIs, IP in fine-tuning datasets.
Monitors inference API traffic for abuse patterns, rate limit violations, prompt injection attempts, and data exfiltration through model responses.
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