AIAI Governance StackFree kit

HiddenLayer

AI detection and response plus red teaming to protect machine learning models and AI products

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What HiddenLayer does

HiddenLayer is an AI security company focused on protecting machine learning models and AI-powered products from adversarial attack, theft, and misuse. Founded in 2022 in Austin, Texas by James Ballard, Tanner Burns, and Chris Sestito, the company built its reputation on Machine Learning Detection and Response (MLDR), a non-invasive, software-based approach that monitors the inputs and outputs of AI models to detect attacks in real time and respond through alerting, isolation, profiling, or deception. HiddenLayer has expanded into a broader AI security platform that includes model scanning for malicious or vulnerable artifacts, automated red teaming to probe models and applications before deployment, and AI risk and governance tooling. It serves enterprise security teams, ML engineers, and regulated organizations that treat models as critical, unprotected assets. Backed by a $50M Series A led by M12 (Microsoft's venture fund) and Moore Strategic Ventures with participation from Booz Allen, IBM, and Capital One Ventures, HiddenLayer is among the better-funded pure-play AI security vendors. Its distinguishing strength is runtime detection and response tailored specifically to machine learning models rather than only pre-deployment testing.

Key capabilities

  • Machine Learning Detection and Response (MLDR)
  • Model scanning
  • Automated red teaming
  • Real-time attack detection
  • AI risk and governance reporting
  • Response actions (alert, isolate, deceive)

Best for

Enterprises and regulated organizations needing runtime protection and detection for production ML models

Limitations

Historically ML-model centric; buyers seeking full agentic AI governance or compliance certification may need complementary tooling.

Framework coverage

HiddenLayer does not publish explicit mappings to the major AI governance frameworks. That is common for tools in the red-teaming & ai security category, where the value is technical rather than documentary — but it means you will be responsible for evidencing how it satisfies your obligations.

Compare HiddenLayer

Head-to-head against the closest tools in its category.

HiddenLayer alternatives

Other tools solving a similar problem in Red-Teaming & AI Security.

End-to-end AI security and governance platform to discover, monitor, red-team and prove enterprise AI

EU AI ActNIST AI RMFISO/IEC 42001

Continuous automated AI red teaming and security testing for enterprise AI systems

SOC 2

End-to-end security for the AI and machine-learning supply chain

NIST AI RMFSOC 2Open-source

AI Firewall and automated model validation to secure AI from build to production

NIST AI RMF

Open-source tool for evaluating and red-teaming LLM apps, agents, and RAG systems

Open-source

Continuous AI red teaming for agents, LLMs, and generative AI applications

See the full HiddenLayer alternatives guide →

AI Governance Tool Selection Kit

A vendor-comparison worksheet plus EU AI Act, NIST AI RMF and ISO/IEC 42001 requirement checklists — so you can shortlist tools against the obligations that actually apply to you.

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