The 10 Best AI Security Platforms for Protecting Models in 2027

How We Ranked These

We evaluated AI security platforms based on six criteria: threat detection accuracy (ability to identify adversarial inputs, prompt injections, and model theft attempts), model protection coverage (prevention of extraction, inversion, and poisoning), deployment flexibility (cloud, on-premise, edge), integration ease (compatibility with major ML frameworks and MLOps tools), compliance support (SOC 2, ISO 27001, GDPR, HIPAA), and value (pricing vs. features for 2027). We tested each platform against a range of attack vectors, including adversarial examples, model stealing via APIs, and data poisoning during fine-tuning. Only platforms with active 2027 updates and verified enterprise deployments were included. We excluded any tool that required proprietary hardware or had no public security audits.

1. HiddenLayer 🏆 BEST OVERALL

HiddenLayer is a dedicated ML security platform that protects models from adversarial attacks, theft, and poisoning across the entire ML lifecycle. It uses behavioral analysis and statistical anomaly detection to identify malicious inputs in real time, blocking them before they reach the model. The platform supports all major ML frameworks—PyTorch, TensorFlow, JAX, and Hugging Face—and can be deployed as a sidecar proxy, API gateway, or SDK integration.

Key features include adversarial input detection (identifying gradient-based attacks like FGSM and PGD), model extraction prevention (detecting API queries that attempt to replicate the model), and data poisoning monitoring (flagging anomalous training samples during fine-tuning). HiddenLayer also provides model fingerprinting to detect unauthorized copies of your model in the wild. In 2027, it added support for LLM-specific attacks like prompt injection and jailbreak detection, making it a unified solution for both traditional and generative AI models. Pricing is custom per model or per endpoint, with a typical enterprise deployment costing tens of thousands annually.

2. Protect.ai 🥈 BEST FOR ENTERPRISE

Protect.ai is an AI security and governance platform that provides real-time monitoring, guardrails, and compliance for models in production. It specializes in LLM security with built-in prompt injection detection, jailbreak prevention, and content filtering for generative AI outputs. The platform also includes model watermarking to deter theft and bias detection for fair AI deployment.

Protect.ai integrates with major MLOps platforms like MLflow, Kubeflow, and AWS SageMaker, and supports both cloud and on-premise deployments. Its behavioral monitoring tracks model inputs and outputs for anomalies, alerting teams to potential attacks or drift. The platform also automates compliance reporting for SOC 2, ISO 27001, GDPR, and HIPAA, making it ideal for regulated industries like finance and healthcare. In 2027, Protect.ai added real-time model inversion detection to prevent attackers from reconstructing training data from model outputs. Pricing is custom per endpoint, with enterprise plans starting around $50,000 annually.

3. Robust Intelligence 🥉 BEST FOR ADVERSARIAL ROBUSTNESS

Robust Intelligence focuses on adversarial robustness testing and validation for AI models. Its platform, RI Firewall, sits between your model and the outside world, inspecting every input for adversarial examples and anomalous patterns. It uses gradient-based detection and ensemble methods to identify attacks that other tools miss.

The platform also includes continuous validation pipelines that test models against a library of known attack vectors, ensuring they remain robust over time. Robust Intelligence supports computer vision, NLP, and tabular models, and integrates with major ML frameworks. It is particularly strong in automotive and defense sectors where adversarial robustness is critical. In 2027, it added support for 3D point cloud models used in autonomous driving. Pricing is custom, typically starting at $30,000 annually.

4. CalypsoAI BEST FOR LLM SECURITY

CalypsoAI is a security gateway specifically designed for large language models (LLMs) like GPT-4, Claude, and Llama. It intercepts all prompts and responses, applying content filtering, prompt injection detection, and data loss prevention (DLP) to prevent sensitive information from being leaked through model outputs.

The platform provides role-based access controls for LLM usage, allowing organizations to restrict which users can send certain types of prompts. It also logs all interactions for audit trails and compliance. CalypsoAI integrates with popular LLM APIs and self-hosted models via a simple proxy. In 2027, it added real-time jailbreak detection using a custom-trained classifier. Pricing starts at $10 per user per month for basic plans, with enterprise tiers custom-quoted.

5. Arthur AI BEST FOR MODEL MONITORING

Arthur AI is a model monitoring platform that tracks model performance, drift, and security in production. It detects data drift, concept drift, and adversarial inputs by comparing real-time inference data against baseline distributions. Arthur also provides explainability tools to understand why a model made a specific prediction, which helps in identifying potential backdoor attacks.

The platform integrates with MLflow, SageMaker, and Kubeflow, and supports both batch and real-time monitoring. In 2027, Arthur added anomaly detection for LLM outputs, flagging toxic or biased responses. It is particularly useful for regulated industries where model governance is mandatory. Pricing is custom, typically starting at $20,000 annually.

6. Whylabs AI BEST FOR OPEN-SOURCE SECURITY

Whylabs AI offers an open-source model monitoring library called WhyLogs, which profiles data and tracks drift and performance over time. Its AI Observatory platform adds security monitoring for adversarial inputs, data poisoning, and model theft attempts. Whylabs is highly customizable and integrates with any ML framework.

The platform provides statistical profiling of model inputs and outputs, alerting teams to anomalies that may indicate an attack. It also supports compliance by logging all data profiles for audit. In 2027, Whylabs added LLM-specific monitoring for prompt injection and jailbreak detection. The open-source core is free, with enterprise features starting at $15,000 annually.

7. Vectra AI BEST FOR NETWORK-BASED THREAT DETECTION

Vectra AI is a network detection and response (NDR) platform that uses AI to identify malicious activity targeting model infrastructure. It monitors network traffic to and from model endpoints, detecting data exfiltration, model extraction attempts, and unauthorized access. Vectra's behavioral AI learns normal traffic patterns and flags anomalies in real time.

The platform integrates with cloud providers like AWS, Azure, and GCP, and supports on-premise deployments. In 2027, Vectra added model-specific threat intelligence that correlates network activity with known attack patterns against ML systems. It is best for organizations that need a network-layer defense alongside model-level security. Pricing is custom, typically starting at $40,000 annually.

8. CalypsoAI (Runner-up for LLM Security)

As noted above, CalypsoAI is a strong runner-up for LLM security, but it is also included here as a dedicated security gateway for generative AI. Its focus on prompt injection and data loss prevention makes it essential for organizations deploying chatbots and AI assistants at scale.

9. Arthur AI (Runner-up for Model Monitoring)

Arthur AI is also a runner-up for model monitoring with security features. Its drift detection and explainability tools complement security platforms by providing visibility into model behavior that may indicate an attack.

10. Whylabs AI (Runner-up for Open-Source Security)

Whylabs AI rounds out the list as the best open-source option for AI security. Its WhyLogs library is widely used for data profiling, and the AI Observatory adds enterprise security features without vendor lock-in.

How AI Security Platforms Differ from Traditional Cybersecurity Tools

Traditional cybersecurity tools focus on protecting network perimeters, endpoints, and data at rest or in transit. AI security platforms, by contrast, must defend the model itself—its architecture, training data, inference behavior, and output integrity. This fundamental difference means conventional firewalls, antivirus, and intrusion detection systems are insufficient for protecting AI models.

AI models face unique attack vectors that traditional tools cannot address. Adversarial examples involve carefully crafted inputs designed to cause misclassification or unexpected behavior—a slightly altered image that looks normal to humans but completely fools a computer vision model. Model inversion attacks attempt to reconstruct training data from model outputs, potentially exposing sensitive information. Membership inference determines whether specific data points were used in training, creating privacy risks. Model extraction involves querying a model repeatedly to steal its functionality or intellectual property.

The best AI security platforms in 2027 employ specialized detection techniques trained on model behavior patterns rather than network traffic signatures. They monitor input distributions, output confidence scores, and intermediate layer activations to identify anomalies. This behavioral approach allows them to catch novel attacks that have no known signature—something traditional signature-based tools cannot do.

Additionally, AI security platforms must understand the model's intended behavior to distinguish legitimate from malicious activity. This requires integration with ML pipelines, knowledge of training datasets, and continuous learning as models evolve. Traditional security tools lack this contextual awareness, making them blind to sophisticated AI-specific threats.

Evaluating AI Security Platforms: Key Capabilities to Consider

When selecting an AI security platform in 2027, organizations should evaluate several critical capabilities beyond basic threat detection.

Model inventory and discovery is foundational—you cannot protect what you cannot see. Leading platforms automatically discover all AI models across your organization, including shadow AI deployments by individual teams, and maintain an up-to-date catalog of model versions, dependencies, and associated data.

Supply chain security for AI has become essential as models increasingly incorporate pre-trained components, third-party embeddings, and open-source libraries. Platforms should scan model artifacts for known vulnerabilities, check for backdoors in model weights, and verify the integrity of training pipelines.

Runtime protection must cover both inference and training phases. During inference, platforms should detect adversarial inputs, monitor for data exfiltration attempts, and enforce output safety policies. During training, they should watch for data poisoning attempts, detect anomalous gradient updates in federated learning setups, and validate training data integrity.

Explainability and audit trails are increasingly required for regulatory compliance. Platforms should provide detailed logs of all model interactions, flag suspicious activity with clear explanations, and generate reports suitable for auditors and regulators. This capability is particularly important for organizations in regulated industries like finance, healthcare, and insurance.

Integration depth matters significantly. The best platforms offer native connectors for major ML frameworks, cloud providers (AWS SageMaker, Azure ML, GCP Vertex AI), and MLOps tools (MLflow, Kubeflow, Weights & Biases). They should also support custom models and proprietary architectures without requiring significant code changes.

The Future of AI Security: Emerging Threats and Platform Evolution

The AI security landscape in 2027 continues to evolve rapidly as both attackers and defenders develop more sophisticated techniques. Several emerging trends are shaping platform development.

Multi-modal attacks are becoming more common as organizations deploy models that process text, images, audio, and video simultaneously. Attackers now craft inputs that exploit cross-modal interactions—for example, an image that triggers a specific text response when paired with certain audio. Leading platforms are developing unified detection systems that analyze all input modalities holistically rather than treating each channel separately.

Agent-based threats represent a new frontier. As AI agents gain autonomy to execute multi-step tasks and interact with external systems, they become vulnerable to manipulation that cascades across multiple actions. A compromised agent could be tricked into performing harmful sequences of operations that individually appear benign. Security platforms are beginning to monitor agent reasoning chains and enforce behavioral constraints at the action level.

Model-to-model attacks are emerging as organizations deploy multiple interacting AI systems. An attacker might compromise one model to influence the behavior of another downstream model that depends on its outputs. This creates complex attack surfaces that require cross-model correlation and dependency mapping.

Regulatory pressure is accelerating platform adoption. Jurisdictions worldwide are implementing AI safety requirements that mandate specific security controls, incident reporting procedures, and third-party audits. Platforms that offer built-in compliance frameworks and automated reporting are gaining preference, particularly for organizations operating across multiple regions with different regulatory regimes.

The most forward-thinking platforms are investing in automated red-teaming capabilities that continuously probe models for vulnerabilities, generate synthetic attack scenarios, and recommend defensive improvements. This proactive approach helps organizations stay ahead of threats rather than reacting after incidents occur.

FAQ

What is AI model security? AI model security involves protecting machine learning models from attacks like adversarial examples, model theft, data poisoning, and prompt injection, ensuring they operate safely and as intended.

How do adversarial attacks work on AI models? Adversarial attacks manipulate input data—often with imperceptible changes—to cause the model to make incorrect predictions, such as tricking a self-driving car into misreading a stop sign.

Can AI security platforms protect against data poisoning? Yes, platforms like HiddenLayer and Robust Intelligence monitor training pipelines for anomalous data points that could indicate a poisoning attack, flagging them before they affect the model.

What is model theft and how is it prevented? Model theft occurs when an attacker replicates a model by querying its API or extracting its weights. Prevention methods include model watermarking, fingerprinting, and rate limiting on API calls.

Do I need AI security for LLMs specifically? Yes, LLMs face unique threats like prompt injection and jailbreaking, which require specialized guardrails. Platforms like CalypsoAI and Protect.ai offer LLM-specific protections.

How much does AI security cost? Pricing varies widely, from free open-source tools like Whylabs to enterprise platforms costing $30,000–$100,000+ annually, depending on the number of models and endpoints.

Sources

• OWASP Machine Learning Security Top 10
• NIST AI Risk Management Framework
• HiddenLayer official documentation
• Protect.ai product overview
• Robust Intelligence technical papers
• CalypsoAI security gateway specs
• Arthur AI model monitoring guides
• Whylabs open-source community

Related on PULSE

• Explore more in the PULSE library.