[Home](https://cybersectools.com/) / [Categories](https://cybersectools.com/categories) / [AI Security](https://cybersectools.com/categories/ai-security) / MLSecOps # MLSecOps Solutions 2026 MLSecOps is the practice of building security into the machine learning lifecycle, from training data and model artifacts through deployment and runtime. The solutions here defend against threats that traditional AppSec misses: poisoned training data, tampered model files, adversarial inputs that fool a model at inference, and model theft or extraction. When your organization is shipping ML models or generative AI into production, this is the category that secures the parts your SAST scanner and WAF never see. It matters most to security teams supporting data science and AI engineering groups that have moved faster than the controls around them. 8 commercial products · [What is MLSecOps?](https://cybersectools.com/glossary/mlsecops) ## 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) ## MLSecOps solutions All 8 · Free 0 · Commercial 8 1. [NeuralTrust Observability](https://cybersectools.com/tools/neuraltrust-observability) (MLSecOps): Tracing, analytics, and observability platform for LLM pipelines and GenAI apps. 2. [SafeLiShare Confidential AI Collaboration Platform](https://cybersectools.com/tools/safelishare-confidential-ai-collaboration-platform) (MLSecOps) 3. [Jozu Hub + Agent Guard](https://cybersectools.com/tools/jozu-hub-agent-guard) (MLSecOps): On-prem security & governance platform for AI/ML models on Kubernetes. 4. [Openlayer ML Testing](https://cybersectools.com/tools/openlayer-ml-testing) (MLSecOps): ML testing platform for validating models pre/post-deployment via CI/CD. 5. [Protopia AI Stained Glass Engine](https://cybersectools.com/tools/protopia-ai-stained-glass-engine) (MLSecOps): Creates privacy-preserving transforms to protect sensitive data in AI/ML training. 6. [Advai Platform](https://cybersectools.com/tools/advai-platform) (MLSecOps): AI testing & monitoring platform for secure, compliant AI deployment. 7. [JFrog ML](https://cybersectools.com/tools/jfrog-ml) (MLSecOps): Platform for building, deploying, managing & monitoring AI/ML workflows & models 8. [Trend Micro Secure AI Factory](https://cybersectools.com/tools/trend-micro-secure-ai-factory) (MLSecOps): End-to-end platform for secure enterprise AI deployment with compliance controls 8 commercial products | Related: [AI Data Poisoning Protection](https://cybersectools.com/categories/ai-data-poisoning-protection) · [AI Model Security](https://cybersectools.com/categories/ai-model-security) · [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 MLSecOps solutions - Match the solution to your ML risk profile: model scanning and supply chain controls if you consume external models, data integrity and robustness testing if you train your own. - Confirm it covers the lifecycle stages you care about: training data, model artifacts and registries, and runtime inference, not just one slice. - Check integration with your actual ML stack, including the model registry, training pipeline, and serving infrastructure you already run. - Look for explicit mapping to MITRE ATLAS and the OWASP ML and LLM Top 10 so findings translate into a framework your auditors and board recognize. - If you run generative AI in production, prioritize runtime guardrails and automated red teaming for prompt injection and data leakage. - Decide whether you need a focused point solution for one threat class or a broader platform that spans data, models, and runtime in one console. ## MLSecOps Solutions FAQ Common questions about MLSecOps solutions: how to choose, pricing and comparisons. ### What is MLSecOps? MLSecOps is the discipline of securing the machine learning lifecycle end to end. It covers training data integrity, the model supply chain (artifacts, registries, dependencies), and the deployed model itself against runtime attacks like adversarial inputs and model extraction. Think of it as DevSecOps applied to ML, addressing risks that conventional application security controls were never designed to catch. ### How is MLSecOps different from MLOps? MLOps is about reliably building, deploying, and operating models: pipelines, versioning, monitoring, and retraining. MLSecOps adds the adversarial and trust layer on top of that. It asks who tampered with the training data, whether a model file is malicious, and how a deployed model behaves when an attacker probes it. MLOps keeps models running; MLSecOps keeps them from being weaponized or compromised. ### What threats do MLSecOps solutions actually defend against? The common ones are data poisoning during training, malicious model artifacts such as deserialization payloads in pickle files, adversarial examples crafted to flip a prediction at inference, model extraction or inversion that steals the model or its training data, and prompt injection against LLM-backed applications. Many solutions also map findings to frameworks like MITRE ATLAS and the OWASP ML and LLM Top 10. ### How do I choose an MLSecOps solution? Begin with where your risk concentrates. If you consume third-party or open-source models, prioritize model scanning and supply chain controls. If you train your own models, weight data integrity and adversarial robustness testing. For generative AI in production, look at runtime guardrails and red teaming. Then confirm it fits your existing ML platform and registry, and whether it maps results to MITRE ATLAS or the OWASP ML Top 10. ### Do I need a dedicated MLSecOps solution, or can existing security solutions cover it? Existing solutions cover part of it. Your container scanner sees the runtime image and your SAST sees the serving code, but neither understands a poisoned dataset, a malicious serialized model, or an adversarial input. Dedicated MLSecOps solutions fill that gap. Most teams keep their current AppSec stack and add ML-specific scanning, robustness testing, and runtime defenses rather than replacing anything. --- Source: https://cybersectools.com/categories/mlsecops For AI agents: every CybersecTools shortlist is listed at https://cybersectools.com/shortlists.md. More on the MLSecOps 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