HiddenLayer is a cutting-edge cybersecurity platform focused on securing machine learning (ML) models from adversarial attacks, data manipulation, and unauthorized use. As AI adoption grows, so do threats targeting ML infrastructure. HiddenLayer delivers enterprise-grade AI threat detection and model protection tools to ensure your models perform as intended—safely and reliably.
The platform is built for organizations deploying AI in production, offering inference-layer monitoring, adversarial threat detection, and attack surface protection for ML models across industries like finance, healthcare, defense, and technology.
Features
HiddenLayer offers a robust suite of features tailored to the unique needs of AI security:
Inference-Layer Threat Detection
Monitors model inputs and outputs in real time to detect adversarial activity or model probing attempts.Model-Agnostic Protection
Works with any model architecture or framework (e.g., PyTorch, TensorFlow, Scikit-learn) without retraining or modifying model code.ML Attack Surface Reduction
Identifies and mitigates security risks such as model theft, membership inference, and data leakage.Adversarial Input Detection
Detects evasion attacks and input manipulation tactics used to trick AI models into incorrect predictions.Model Fingerprinting
Creates cryptographic fingerprints to ensure model authenticity and detect tampering or replacement.Runtime Monitoring
Provides continuous observability into the model’s behavior and input/output patterns.SIEM & SOAR Integration
Easily connects with security tools such as Splunk, Microsoft Sentinel, and Palo Alto Cortex for centralized incident response.Forensic & Compliance Reporting
Logs all threats, inputs, and events for compliance with industry regulations and internal audits.
How It Works
HiddenLayer is deployed in the AI inference pipeline—between model input and output—where it continuously monitors for threats:
Agentless Integration
Wraps around existing models without changing architecture or interfering with performance.Inference Monitoring
Tracks every model prediction and inspects inputs and outputs for anomalies, adversarial signals, and behavior deviations.Threat Classification
Uses machine learning and rule-based logic to classify threats in real time, such as data exfiltration or input fuzzing.Response Automation
Sends alerts to SIEM or SOAR systems or triggers real-time remediation steps based on organization policies.Audit Trail and Forensics
Provides detailed visibility into past attacks, attack vectors, and impacted inputs for forensic analysis and regulatory reporting.
Use Cases
1. Protecting AI in Financial Services
Prevent adversarial attacks on credit scoring or fraud detection models that can lead to financial manipulation.
2. Healthcare Model Integrity
Ensure diagnostic and treatment recommendation models are not misled by malicious inputs or tampered datasets.
3. Intellectual Property Protection
Safeguard proprietary ML models from theft or reverse engineering via fingerprinting and usage control.
4. AI Compliance & Governance
Meet internal and regulatory AI governance standards by maintaining audit trails and explaining model behaviors under attack.
5. Adversarial Threat Hunting
Use HiddenLayer’s tools to proactively monitor and block black-box attacks or model inversion attempts.
6. Securing AI APIs
Detect and block abuse or input fuzzing in production ML APIs that expose model logic or sensitive data behavior.
Pricing
As of June 2025, HiddenLayer does not publish public pricing. Pricing is custom-tailored based on:
Number and type of ML models
Deployment architecture (on-prem, cloud, hybrid)
Required integrations and support tiers
Volume of model inferences monitored
To request a custom quote or book a demonstration, visit the HiddenLayer contact page.
Strengths
ML-Specific Security Focus
Purpose-built for protecting machine learning models, not just general application security.Non-Intrusive Deployment
Requires no changes to model architecture or retraining, making integration fast and safe.Model-Agnostic Architecture
Compatible with any ML framework or environment.Enterprise-Ready Integrations
Works seamlessly with popular security stacks (SIEM/SOAR platforms) and monitoring tools.Real-Time Protection
Actively monitors inference activity and flags threats as they occur.Supports AI Governance
Helps meet compliance, audit, and ethical AI requirements by recording threats and model behavior.
Drawbacks
No Free Trial or Public Sandbox
Enterprise clients must schedule a demo to access the product—no trial version currently offered.Focused on Inference Layer
Primarily protects deployed models; limited features for training-time data poisoning or insider threats.Requires Security Team Involvement
Full value is unlocked when integrated with broader SOC processes, which may be a barrier for small ML teams.Limited Public Reviews
As an emerging leader in a specialized field, HiddenLayer has few third-party user reviews or ratings available online.
Comparison with Other Tools
HiddenLayer vs. Robust Intelligence
Robust Intelligence addresses ML validation and robustness. HiddenLayer focuses specifically on live inference security and threat detection.
HiddenLayer vs. TrojAI
TrojAI is designed for backdoor and poisoning detection during model training. HiddenLayer focuses on runtime inference monitoring.
HiddenLayer vs. Microsoft Defender for Cloud
Microsoft’s tool covers broad cloud security. HiddenLayer is laser-focused on ML model security and adversarial defense.
HiddenLayer vs. traditional WAFs or endpoint tools
Web Application Firewalls (WAFs) and antivirus tools cannot detect adversarial inputs designed to manipulate AI. HiddenLayer fills this unique security gap for AI pipelines.
Customer Reviews and Testimonials
While HiddenLayer does not publish customer reviews on its website, it has been recognized by major cybersecurity publications and conferences and is trusted by organizations in:
Financial services
Government and defense
Healthcare AI deployments
AI product companies
A quote from HiddenLayer’s case studies includes:
“We integrated HiddenLayer in under a week and immediately flagged adversarial probing attempts that had gone unnoticed.”
Another enterprise CISO noted:
“HiddenLayer bridges the AI security gap we knew existed but didn’t know how to fix. It’s now a key part of our AI risk strategy.”
Conclusion
As AI becomes embedded in mission-critical business and government functions, securing the models themselves is no longer optional. HiddenLayer offers a highly specialized, effective solution for protecting ML models from real-world attacks—without disrupting performance or requiring model reengineering.
For any organization deploying AI at scale, HiddenLayer provides the tools needed to ensure trust, compliance, and security across the entire ML lifecycle.
