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# AI Model Security Solutions 2026

AI Model Security covers the solutions that protect the trained model itself, the weights, the architecture, and its inference behavior, treating the model as a high-value asset rather than just another file on a server. It matters because a model you spent millions training can be stolen by copying weights, reconstructed through query-based extraction, fooled by adversarial inputs, or made to leak its training data through inversion and membership attacks. CISOs reach for this category when proprietary models ship to the edge, run on-device, or sit behind a public inference API, all places where the usual app-layer controls do not reach. Picture it as the discipline of keeping the model confidential, tamper-resistant, and resilient against attacks aimed at the math, not the API.

15 commercial products · [What is AI Model Security?](https://cybersectools.com/glossary/ai-model-security)

## Featured on CybersecTools

Paid placement: Featured vendors pay for a plan and show first. The shortlist positions are earned. [How we label paid slots](https://cybersectools.com/methodology)

- [Strike48 Platform](https://www.strike48.com/platform?utm_source=cybersectools.com&utm_medium=featured) (Featured): Security Operations
- [SecureIntent](https://secureintent.ai/?utm_source=cybersectools.com&utm_medium=featured) (Featured): Data Protection
- [Fortra](https://www.fortra.com/?utm_source=cybersectools.com&utm_medium=featured) (Featured): Data Protection
- [Lunar](https://lunarcyber.com/?utm_source=cybersectools.com&utm_medium=featured) (Featured): Threat Intelligence
- [Hudson Rock](https://www.hudsonrock.com/?utm_source=cybersectools.com&utm_medium=featured) (Featured): Threat Intelligence
- [Daylight Security](https://daylight.ai/?utm_source=cybersectools.com&utm_medium=featured) (Featured): Security Operations

## The AI Security market

Company data as of 2026-11-05

- 214 companies sell in this market
- $1.7B raised in 79 rounds in the last 12 months
- 18 acquired in the last 12 months
- 96 founded in the last two years

[Explore the AI Security market on Mandos](https://mandos.io/data/companies?utm_source=cybersectools.com&utm_medium=category&utm_campaign=ai-security)

## AI Model Security solutions

All 16 · Free 1 · Commercial 15

1. [NeuralTrust Model Scanner](https://cybersectools.com/tools/neuraltrust-model-scanner) (AI Model Security): Scans AI models for malicious code, vulnerabilities, and unsafe artifacts pre-deployment.
2. [Attestable](https://cybersectools.com/tools/attestable) (AI Model Security): Zero-knowledge proof system for verifying AI computation integrity without trusted
3. [Starseer Platform](https://cybersectools.com/tools/starseer-platform) (AI Model Security): Scans, monitors, and enforces policy on AI models
4. [Vijil Trust Report](https://cybersectools.com/tools/vijil-trust-report) (AI Model Security): LLM trust evaluation report scoring models on security, safety & reliability.
5. [Honeypotz AI Studio](https://cybersectools.com/tools/honeypotz-ai-studio) (AI Model Security): Confidential computing platform securing AI/ML models and sensitive data.
6. [Secure AI Lab](https://cybersectools.com/tools/secure-ai-lab) (AI Model Security · Free): Academic research lab focused on privacy-preserving and secure AI/ML.
7. [ObjectSecurity FortiLayer](https://cybersectools.com/tools/objectsecurity-fortilayer) (AI Model Security): AI/ML model security tool for internal vulnerability analysis in defense apps.
8. [Enveil Secure AI](https://cybersectools.com/tools/enveil-secure-ai) (AI Model Security): PETs-powered encrypted ML training, inference, and validation across data silos.
9. [Duality Secure AI Collaboration Platform](https://cybersectools.com/tools/duality-secure-ai-collaboration-platform) (AI Model Security): Platform for privacy-protected AI/ML model training on sensitive data.
10. [Bosch AIShield AISpectra](https://cybersectools.com/tools/bosch-aishield-aispectra) (AI Model Security): API-based AI/ML vulnerability assessment and defense platform.
11. [Mirror Security VectaX](https://cybersectools.com/tools/mirror-security-vectax) (AI Model Security): FHE-based encryption for AI models, vector databases, and RAG workflows
12. [Safe Intelligence](https://cybersectools.com/tools/safe-intelligence) (AI Model Security): ML model validation, robustification, and monitoring platform
13. [Skyld](https://cybersectools.com/tools/skyld) (AI Model Security): AI model protection platform securing on-device models from reverse engineering
14. [Irdeto AI Model Protection](https://cybersectools.com/tools/irdeto-ai-model-protection) (AI Model Security): Protects AI models from theft, misuse & reverse engineering via licensing
15. [DeepKeep Model Scanning](https://cybersectools.com/tools/deepkeep-model-scanning) (AI Model Security): Scans AI models for security threats before deployment
16. [DataKrypto FHEnom for AI](https://cybersectools.com/tools/datakrypto-fhenom-for-ai) (AI Model Security): FHE-based solution securing AI models and data throughout training and inference

15 commercial products · 1 free products under [Free solutions](https://cybersectools.com/free) | Related: [AI Data Poisoning Protection](https://cybersectools.com/categories/ai-data-poisoning-protection) · [MLSecOps](https://cybersectools.com/categories/mlsecops) · [AI Red Teaming](https://cybersectools.com/categories/ai-red-teaming) · [LLM Guardrails](https://cybersectools.com/categories/llm-guardrails) · [AI Governance](https://cybersectools.com/categories/ai-governance)

## How to choose AI Model Security solutions

- Match the solution to your deployment surface: protecting weights on a public inference API is a different problem from hardening a model that ships in an edge device or mobile binary where attackers have physical access.
- Pin down which threat classes it actually addresses. Model theft and extraction, adversarial evasion, model inversion, membership inference, and backdoor detection are distinct problems, and most solutions are strong in one or two, not all.
- Decide between protection and testing. Some solutions obfuscate, encrypt, or watermark the model to prevent and trace theft; others red-team it for adversarial robustness. You often need both, and they are rarely the same product.
- Measure the runtime cost. Encryption, obfuscation, and inference-time defenses add latency and memory overhead, a real constraint for on-device or high-throughput serving. Benchmark on your own model before committing.
- Confirm framework and hardware coverage. Verify support for your formats (ONNX, PyTorch, TensorFlow) and your targets (specific GPUs, mobile NPUs, embedded chips, trusted execution environments), since gaps here are silent dealbreakers.
- Look for theft evidence you can act on. Watermarking, fingerprinting, and extraction-attempt detection only help if they produce signals your team can monitor and stand behind in an IP dispute.

## AI Model Security Solutions FAQ

Common questions about AI Model Security solutions: how to choose, pricing and comparisons.

### What is AI model security?

AI model security is the practice of protecting a trained machine learning model as an asset: its weights, architecture, and inference behavior. It defends against model theft, query-based extraction, adversarial inputs that cause misclassification, and privacy attacks like model inversion and membership inference that reconstruct training data. The focus is the model itself, not the surrounding application or infrastructure.

### How is AI model security different from AI guardrails or LLM firewalls?

Guardrails and LLM firewalls work at the application layer, filtering prompts and outputs to block prompt injection, jailbreaks, and unsafe content. AI model security works lower down, protecting the model artifact and its internal behavior against theft, extraction, tampering, and adversarial attacks on the math. They solve different problems, and a serious deployment usually needs both.

### How do I choose an AI model security solution?

Begin with your deployment surface and threat model. A model behind a cloud API needs extraction detection and API hardening, while a model on an edge device needs encryption and obfuscation against physical access. Then separate protection solutions from adversarial testing solutions, confirm framework and hardware support, and benchmark the latency overhead on your own model before buying.

### When do I actually need dedicated model security versus standard security controls?

Standard controls like access management and encryption at rest cover the basics, but they do not stop adversarial evasion, query-based model extraction, or membership inference, because those attacks abuse legitimate inference access. You need dedicated solutions when the model is proprietary and high-value, when it ships outside your trust boundary, or when its training data is sensitive enough that reconstruction is a real risk.

### Are open-source adversarial robustness libraries enough, or do I need a commercial solution?

Open-source adversarial robustness toolkits are excellent for testing and research and cost nothing to start with. They fall short on production protection: encrypting weights, hardening on-device models, watermarking for IP defense, and continuous monitoring. Commercial solutions in this category package those capabilities with support and hardware coverage, so many teams use open source for testing and a commercial product for runtime protection.

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Source: https://cybersectools.com/categories/ai-model-security

For AI agents: every CybersecTools shortlist is listed at https://cybersectools.com/shortlists.md.

More on the AI Model Security vendors: funding, momentum, hiring and news, plus the full product and company record, are available through the Mandos MCP Server (paid plans), which connects to AI assistants: https://mandos.io/mcp?utm_source=cybersectools.com&utm_medium=category-md