Top 10 AI Agent Security platforms
AI agents are no longer just answering questions. They are making decisions, calling APIs, and performing actions on behalf of users. This shift from static models to autonomous AI systems has created new exposure points that traditional security tools were never designed to handle.
Organizations adopting agentic AI now face threats such as prompt injection, memory manipulation, and tool abuse, where a single misstep can expose sensitive data or trigger unintended actions. The result is a growing need for AI agent security platforms that protect these systems at runtime, verify model integrity, and enforce behavioral boundaries.
This article reviews ten of the most effective platforms available today and explains how they help enterprises defend and monitor autonomous agents safely at scale.
What qualifies as agentic system security
Agentic systems go beyond standard generative AI applications. Instead of waiting for a single prompt and returning an answer, an agent can plan, make choices, and use tools such as APIs, databases, or third-party software to complete tasks. That autonomy introduces new and evolving attack surfaces that require security measures specifically designed for this kind of behavior.
Agent autonomy, tool use, and persistent memory
An AI agent often combines reasoning, memory, and the ability to act. It can remember context between interactions, call external functions, and chain outputs from one model into the inputs of another. Each of these steps can be targeted by attackers. A malicious input might alter the agent’s memory, trick it into retrieving restricted data, or misuse a connected application. Traditional perimeter security cannot catch these internal manipulations, which is why runtime inspection and behavioral validation are critical.
Guardrails and runtime controls
Guardrails define how an AI agent should behave. They filter prompts, restrict responses, and enforce policy before the model executes an action. Runtime controls, in contrast, operate after deployment. They monitor every transaction, flag anomalies, and block unsafe behavior as it happens. A secure agentic architecture uses both: guardrails to shape intent and runtime controls to prevent exploitation in real time.
Together, these measures form the foundation of agentic system security, a discipline focused on visibility, containment, and trust across autonomous AI workflows.
The market for AI agent security platforms is expanding quickly, but not every tool delivers the same level of coverage or maturity. To make this list credible and practical, each platform was reviewed using a consistent framework that reflects how enterprises actually deploy and secure autonomous systems.
We focused on five evaluation criteria:
Threat coverage. The ability to detect and prevent key risks such as prompt injection, model or data poisoning, memory manipulation, and unsafe tool execution.
Runtime integration. How effectively a product fits into real agent workflows, including support for APIs, model gateways, and cloud or on-prem environments.
Governance alignment. Compliance with standards like NIST AI RMF, OWASP GenAI Security, and the EU AI Act, which guide modern AI assurance programs.
Operational readiness. Usability, latency, and ease of adoption within existing SOC, SIEM, and MLOps stacks.
Transparency and evidence. Open documentation, clear metrics, and proof of efficacy through testing or third-party validation.
This method ensures that every tool featured here supports both defensive security and continuous observability, enabling organizations to protect agentic AI systems with confidence.
Top 10 AI Agent Security Platforms
As enterprises deploy agentic systems at scale, specialized security platforms have emerged to help them stay in control. The following ten solutions represent a mix of open-source projects, enterprise platforms, and community frameworks that address the main risks of autonomous AI. Each entry highlights what the tool does best, where it fits, and who it serves.
1. NeuralTrust Agent Security Suite
NeuralTrust provides a unified platform for protecting AI agents in production. It combines behavior threat detection, adaptive red teaming, and policy-based runtime controls that operate at the gateway level.
The suite includes real-time monitoring for abnormal actions, automatic masking of sensitive data, and audit-ready logging aligned with OWASP GenAI and NIST AI RMF. It can secure both single-agent applications and complex multi-agent systems by isolating behavior and enforcing contextual policies.
Best for: Enterprises seeking measurable, infrastructure-level security that connects runtime protection with continuous evaluation and compliance.
2. NVIDIA NeMo Guardrails & NIM
NeMo Guardrails allows teams to define policy-based rules that keep AI agents within safe behavioral limits. NIM adds small, optimized microservices for detecting unsafe or off-topic outputs. Together, they create a layered safety framework that can operate with any model provider.
Both components are designed for low latency and scalable deployments, running efficiently on standard GPUs. They support custom logic and enterprise integration through Python hooks and reference architectures.
Best for: Teams developing custom agents that need low-latency safeguards and flexible, self-managed control.
3. Palo Alto Networks AI Runtime Security
Palo Alto’s AI Runtime Security platform inspects prompts, responses, and tool calls in real time. It blocks prompt injections, data leaks, and malicious actions using the same infrastructure that powers its enterprise threat detection systems.
It integrates with existing SOC tools, applies centralized policies across multiple AI applications, and connects directly with data loss prevention and malware filtering services. This makes it ideal for organizations already operating within Palo Alto’s security ecosystem.
Best for: Large enterprises that need consistent runtime control and audit-ready visibility across many AI agents.
4. Microsoft PyRIT
Microsoft PyRIT is an open-source framework for red-teaming generative AI systems. It automates adversarial testing by sending controlled attacks against models, then scores the results to highlight vulnerabilities.
Teams can extend it with their own scenarios, integrate it into CI pipelines, and track results over time. PyRIT provides reproducible tests for prompt injection, data extraction, and other high-risk behaviors, making it a useful pre-deployment tool.
Best for: Security teams that want a repeatable, automated way to probe models and agents during development.
5. garak (NVIDIA)
garak is NVIDIA’s open-source toolkit for probing large language models and AI agents. It comes with a wide set of adversarial tests that expose common weaknesses, including data leakage, prompt manipulation, and output failures.
It can run locally or through APIs, making it easy to integrate into development pipelines. Results can be used to guide fine-tuning or adjust guardrails. garak is maintained by an active research community, ensuring continuous updates as new vulnerabilities appear.
Best for: Technical teams seeking a no-cost, flexible testing framework for agentic AI applications.
6. Robust Intelligence AI Firewall & Guardrails (Cisco)
Robust Intelligence, now part of Cisco, offers an external AI firewall that validates model behavior before deployment and filters prompts and responses at runtime. It detects unsafe actions such as data exposure, injection attacks, and hallucinations.
The platform links pre-release validation with live monitoring and automatically adjusts policies as threats evolve. It can be deployed as SaaS or on-prem and integrates with MLOps workflows and policy frameworks.
Best for: Enterprises that require lifecycle security and continuous adaptation to new attack patterns.
7. SailPoint
SailPoint offers identity protection for AI, from model scanning to runtime detection. It identifies threats such as model poisoning, data leakage, and prompt-based manipulation. The platform integrates with SIEM tools and CI/CD pipelines to maintain coverage as models evolve.
Its black-box protection approach means it can secure models even when the source code or training data is unavailable. The platform also aligns with governance frameworks like NIST and supports enterprise compliance programs.
Best for: Organizations that want broad, end-to-end protection with strong integration into security and compliance workflows.
8. OWASP GenAI Security & Privacy Resources
OWASP’s Generative AI Security Project provides open guidance for mitigating threats in agentic and generative systems. It includes the GenAI Top 10, AI Security & Privacy Guide, and testing checklists that translate app-sec best practices into AI contexts.
The resources help teams design threat models, benchmark controls, and build internal standards without relying on a specific vendor. They are free, community-maintained, and updated as new risks emerge.
Best for: Security leaders who want a vendor-neutral foundation to align engineering, risk, and governance teams.
9. Llama Guard (Meta)
Llama Guard is a moderation system designed to classify prompts and responses according to policy categories such as violence or criminal activity. It supports multimodal inputs, allowing analysis of both text and images, and can be self-hosted to maintain full data control.
Enterprises can tune Llama Guard to match their own compliance standards and deploy it within private environments for low-latency operation. It is particularly effective in workflows where user-generated content is common.
Best for: Organizations that require customizable, on-prem moderation for text and image-based interactions.
10. OpenAI Moderation
OpenAI Moderation provides a managed API for detecting and filtering harmful content in text or image form. It returns probability scores for categories like hate, harassment, or self-harm, allowing developers to set custom thresholds or trigger reviews.
It is simple to implement, updated automatically, and included with OpenAI’s API services. While not a full runtime defense, it serves as a baseline safety layer for any application relying on OpenAI models.
Best for: Teams that need fast, reliable moderation without self-hosting or complex configuration.
Buying checklist for agentic AI security
Choosing the right platform requires more than comparing features. The goal is to ensure that every investment directly reduces risk while supporting long-term AI governance. Use the checklist below to evaluate your options before purchase or deployment.
Threat coverage. Confirm that the tool addresses core agentic risks such as prompt injection, data leakage, model poisoning, and unsafe tool execution. Look for both preventive controls and runtime detection.
Integration and ecosystem fit. Check compatibility with your existing stack. Native support for API gateways, CI/CD pipelines, and SIEM tools reduces setup time and ensures consistent visibility.
Performance and scalability. Review latency per request, throughput under load, and ability to scale across multiple agents or environments without bottlenecks.
Governance and compliance alignment. Verify support for recognized frameworks such as NIST AI RMF, OWASP GenAI, or ISO/IEC 42001. Built-in logging and audit trails simplify regulatory reporting.
Operational usability. Evaluate dashboards, policy templates, and alerting systems. A product that your team can easily maintain is more valuable than one that requires specialized expertise.
Support and community. Prioritize tools with active development, regular updates, and responsive vendor or open-source communities.
Total cost and ROI. Balance licensing and maintenance costs against potential breach impact. A reliable, well-integrated platform can pay for itself by preventing a single incident.
Applying these criteria ensures that your chosen AI agent security platform delivers both protection and measurable business value.
Conclusion: Security as the enabler of autonomy
AI agents are redefining how enterprises operate, automating complex tasks and learning from continuous interaction. Yet autonomy without control quickly turns into risk. The security layer that surrounds these systems determines whether they become a strategic asset or a liability.
A strong AI agent security strategy blends multiple layers: guardrails that shape behavior, runtime controls that monitor actions, and governance that keeps decisions accountable. No single tool covers it all, but combining specialized platforms such as those reviewed here, can achieve reliable, measurable protection.
Security is not a barrier to innovation. It is what allows teams to scale agentic AI safely and confidently. Investing in the right platforms ensures that as agents grow more capable, they remain predictable, auditable, and aligned with human intent.
