The 7 best AI security tools in 2026 reviewed: CrowdStrike Falcon AIDR, Prisma AIRS, FortiAI, SkopeAI, Lakera Red, Cyera AI Guardian, and Secure AI Factory.
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AI is no longer just a feature in your security stack. It's an attack surface. In 2026, every organization deploying LLMs, AI agents, or generative AI tools is also deploying new risk: prompt injection, model tampering, training data poisoning, sensitive data leakage through ChatGPT, and autonomous agents that can be hijacked mid-task. The threat model changed faster than most security teams could react.
The tools in this roundup exist specifically to address that gap. Not AI-assisted security tools, those are everywhere now. These are tools built to secure AI systems themselves: the models, the agents, the data pipelines, the prompts, and the APIs that tie it all together. Some focus on runtime protection. Others on posture management, red teaming, or governance. A few try to do all of it.
Picking the right one depends heavily on where your AI risk actually lives. Are you building internal LLM applications? Letting employees use public AI tools? Running AI agents with access to production systems? The answers change which tool belongs in your stack. This roundup cuts through the noise and tells you what each tool actually does, who it's built for, and where it falls short.
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CrowdStrike Falcon AIDR is built for organizations that are already deep in the Falcon ecosystem and now need to extend that coverage to their AI deployments. The core value proposition is straightforward: if you're running AI models, AI agents, or LLM-powered applications in production, Falcon AIDR gives you detection and response capabilities across those components using the same agent and console you're already using for endpoint and cloud workloads. No new agent to deploy. No new console to learn. That operational simplicity is the real differentiator here, not the AI-specific detection logic itself.
Where Falcon AIDR stands apart from peers like Prisma AIRS is in its integration depth with CrowdStrike's broader threat intelligence and XDR capabilities. When an anomaly is detected in an AI model or a suspicious prompt pattern emerges, that signal can be correlated with endpoint telemetry, identity data, and threat intelligence from the Falcon platform. That cross-domain correlation matters when you're investigating whether a prompt injection attempt is part of a broader attack chain or an isolated probe. The NIST coverage here skews toward detection and response (DE.CM, DE.AE, RS.AN), which reflects the product's operational focus rather than a pre-deployment posture management play.
The honest trade-off: Falcon AIDR is a cloud-only deployment, and it's squarely aimed at mid-market and enterprise organizations. If you're not already a CrowdStrike customer, the switching cost to adopt this tool is significant. You're not buying just an AI security module. You're buying into the Falcon platform. For shops already running Falcon for EDR or identity protection, adding AIDR is a natural extension. For everyone else, the integration story doesn't apply and the value proposition weakens considerably.
Also worth noting: the product is relatively new in the AI security space, and the database lists no third-party integrations beyond the Falcon platform itself. If your AI stack spans multiple clouds or involves non-CrowdStrike tooling, you'll need to evaluate how much of your AI risk surface this actually covers versus what falls outside the Falcon agent's reach.
Palo Alto Networks Prisma AIRS
Prisma AIRS is the most feature-complete AI security platform in this roundup, covering the full lifecycle from pre-deployment scanning to runtime protection to posture management. The breadth is real: automated AI red teaming, model vulnerability scanning for deserialization attacks and malicious scripts, runtime protection against prompt injection and sensitive data leaks, and AI agent security that specifically addresses agentic threats like identity impersonation and memory manipulation. If you're building or deploying LLM-powered applications at scale, this is the platform that maps most directly to the OWASP Top 10 for LLMs attack surface.
The MCP (Model Context Protocol) threat detection capability is worth calling out specifically. As AI agents increasingly use MCP to connect to external tools and data sources, that protocol surface becomes an attack vector. Prisma AIRS includes both MCP threat detection and a standalone MCP server for secure AI integration, which puts it ahead of most competitors on agentic security. The AI posture management component also addresses a real operational gap: most teams have no inventory of what AI models are running, what permissions they have, or what data they can access. Prisma AIRS gives you that visibility.
The trade-off is complexity and cost. This is an enterprise platform with enterprise pricing. The feature set assumes you have a security team with the bandwidth to operationalize posture findings, triage red team results, and tune runtime policies. If you're a mid-market team with two people covering cloud security, you may find yourself paying for capabilities you can't fully use. The cloud-only deployment model also means air-gapped or on-premises AI deployments are out of scope.
For organizations that are serious about AI security as a discipline, not just a checkbox, Prisma AIRS is the most thorough option in this list. It covers ID.RA, PR.DS, PR.PS, DE.CM, and DE.AE across the NIST framework, which is the broadest NIST coverage of any tool here. The automated red teaming capability alone, using an adaptive agent that simulates real attacker behavior, is something most organizations would otherwise need to hire a specialized firm to perform.
Fortinet FortiAI
FortiAI is a different kind of tool from the others in this roundup. It's not purely an AI security platform in the sense of securing AI systems. It's a security platform that uses AI to improve security operations, while also including a SecureAI pillar that protects AI infrastructure. That dual nature is both its strength and its source of confusion. The FortiAI-Protect and FortiAI-Assist pillars are about using AI to do better threat detection, alert triage, and threat hunting. FortiAI-SecureAI is about protecting LLMs and AI workloads from attacks like data poisoning and adversarial inputs.
The integration story here is the strongest in the roundup. FortiAI connects natively with FortiGate, FortiGuard, FortiSandbox, FortiNDR, FortiWeb, FortiEDR, FortiAnalyzer, FortiManager, FortiSIEM, and FortiSOAR. If you're running a Fortinet-heavy environment, this is the obvious choice. The Security Fabric integration means AI-driven threat intelligence flows across your firewall, EDR, SIEM, and SOAR without custom connectors. That's a meaningful operational advantage for teams that are already standardized on Fortinet.
The hybrid deployment model is also notable. Unlike most tools in this roundup that are cloud-only, FortiAI supports hybrid deployments. That matters for organizations with on-premises AI workloads or data sovereignty requirements that prevent full cloud deployment. The zero-trust access controls for AI models and LLM data leakage prevention in the SecureAI pillar address real risks, particularly for organizations running private LLMs on internal infrastructure.
The gotcha: FortiAI's value is tightly coupled to the Fortinet ecosystem. If you're not running Fortinet products, you're getting a fraction of the capability. The adaptive threat hunting and root-cause tracing features are compelling on paper, but their effectiveness depends on the telemetry available from connected Fortinet products. Evaluate this one honestly against your existing stack before committing.
Netskope SkopeAI
SkopeAI is fundamentally a data security and CASB play that has been extended to address the generative AI threat surface. The core problem it solves is one that most security teams are already dealing with: employees using ChatGPT, Copilot, Gemini, and other public AI tools to process sensitive data, and security teams having no visibility into what's being shared. SkopeAI's ML-based cloud DLP with Train Your Own Classifiers (TYOC) technology lets you build custom classifiers for your specific data types, which is more practical than relying on pre-built templates that don't know what your proprietary data looks like.
The UEBA component is where SkopeAI differentiates from pure-play AI security tools. It detects anomalous behavior patterns including malicious insiders, compromised accounts, and data exfiltration, and it does this in the context of cloud and AI tool usage. If a user suddenly starts uploading large volumes of data to an AI tool they've never used before, that's a signal. The ML-based device identification and IoT anomaly detection are useful additions for organizations with complex device environments, though they're secondary to the core AI data protection use case.
SkopeAI is the right choice if your primary AI security concern is data leakage through public AI tools rather than securing internally built AI applications. It's also the most accessible tool in this roundup for SMBs, with company size fit listed across SMB, mid-market, and enterprise. The cloud-only deployment is standard for a CASB-adjacent product. The NIST coverage focuses on PR.DS (Data Security) and DE.CM (Continuous Monitoring), which aligns with its data protection orientation rather than a full AI security posture play.
The limitation to be aware of: SkopeAI doesn't do AI red teaming, model vulnerability scanning, or AI agent security. If your risk is in the AI systems you're building rather than the AI tools your employees are using, this isn't the right tool. It's excellent at what it does, but what it does is a subset of the full AI security problem.
Check Point Lakera Red
Lakera Red is a focused tool that does one thing: red team your generative AI applications before attackers do. The three attack vectors it tests, direct manipulation (prompt injection, jailbreaking), indirect manipulation (backdoor injection, persistent data source manipulation), and infrastructure attacks (privilege escalation, unauthorized access), map directly to the real-world attack patterns being used against LLM applications today. This isn't a broad platform play. It's a specialist tool for a specific phase of the AI security lifecycle.
The Gandalf threat intelligence angle is worth understanding. Gandalf is a public AI security game that has attracted a large community of AI security researchers attempting to extract secrets from progressively hardened LLMs. That community generates real-world attack data that feeds into Lakera Red's testing methodology. It's a legitimate source of adversarial intelligence, not a marketing claim. The risk-based vulnerability prioritization and collaborative remediation guidance for Product, Security, and Engineering teams reflects the reality that fixing AI vulnerabilities requires cross-functional coordination, not just a security team ticket.
Lakera Red fits best in organizations that are actively building GenAI applications and need to validate their security posture before deployment or after significant model updates. It's accessible to SMBs through enterprise, and the cloud deployment model keeps the operational overhead low. The NIST coverage is narrow: ID.AM, ID.RA, and PR.PS, which reflects its pre-deployment assessment focus rather than runtime protection.
The trade-off is scope. Lakera Red doesn't do runtime protection, posture management, or data loss prevention. It's a red teaming tool. If you need continuous monitoring of AI systems in production, you'll need to pair it with something else. Think of it as the penetration testing component of your AI security program, not the whole program. For teams that have already deployed GenAI applications without formal security testing, this is the most direct path to understanding your actual exposure.
Cyera AI Guardian
Cyera AI Guardian approaches AI security from a data-centric angle, which makes sense given Cyera's background in data security posture management. The specific problem it addresses is one that most security teams are only beginning to grapple with: you don't know what data your AI systems can access, what data employees are feeding into public AI tools, or whether your sensitive data is being used to train third-party models without your knowledge. AI Guardian gives you visibility into all three categories of enterprise AI: homegrown applications, embedded AI features in existing software (think Salesforce Einstein or Microsoft Copilot), and public tools like ChatGPT.
The detection of unapproved AI tool installations is a practical capability that addresses a real shadow IT problem. Employees install AI tools the same way they installed Dropbox in 2012. By the time security finds out, sensitive data has already been processed. AI Guardian's monitoring for this behavior, combined with data access control monitoring, gives security teams a way to enforce governance without completely blocking AI adoption, which is the political reality in most organizations right now.
The NIST coverage (ID.AM, ID.RA, PR.DS, DE.CM) reflects a governance and data protection orientation. This is not a runtime threat detection tool. It won't catch a prompt injection attack in progress. What it will do is tell you which AI systems have access to your most sensitive data, whether that access is appropriate, and whether employees are moving data to AI tools they shouldn't be using. That's a different but equally important problem.
For security teams that are primarily concerned with data governance and compliance around AI adoption, AI Guardian is the most purpose-built option in this roundup. It works across SMB, mid-market, and enterprise. The cloud deployment model is standard. The limitation is that it doesn't address the security of AI systems themselves, only the data exposure risks around them. If you need to secure the models and agents, not just the data they touch, you'll need to pair this with a tool like Prisma AIRS or Falcon AIDR.
Trend Micro Secure AI Factory
Trend Micro Secure AI Factory is the most infrastructure-focused tool in this roundup, and it's solving a problem that most of the other tools don't touch: securing the physical and virtual infrastructure on which enterprise AI runs. Built in collaboration with NVIDIA and Dell Technologies, it's designed to be factory-installed on NVIDIA DGX systems or Dell PowerEdge XE9680 servers. That hardware-level integration is unusual in the AI security space and reflects a recognition that AI infrastructure security starts at the silicon and OS layer, not just the application layer.
The pre-hardened operating systems and real-time container security for AI workloads address a gap that exists in most enterprise AI deployments: teams spin up GPU clusters and AI workloads using the same default configurations they'd use for any compute workload, without accounting for the specific attack surface of AI inference and training environments. The AI scanner component that assesses systems before deployment for data leakage and prompt injection vulnerabilities is a pre-deployment gate that most organizations currently lack entirely.
The multi-environment deployment support is a genuine differentiator. On-premises datacenters, cloud-native via SaaS control plane, and air-gapped clusters are all supported. For organizations with data sovereignty requirements or regulatory constraints that prevent cloud-only deployments, this is one of the few AI security tools that can actually meet those requirements. The NIST coverage is the broadest in the roundup, spanning GV.SC (supply chain risk), ID.AM, ID.RA, PR.DS, PR.PS, PR.IR, and DE.CM, which reflects the platform's ambition to cover the full AI security stack.
The trade-off is that this tool is purpose-built for organizations deploying dedicated AI infrastructure at scale. If your AI deployment is SaaS-based or you're using managed AI services from a cloud provider, Secure AI Factory's hardware-level controls don't apply. This is a mid-market to enterprise play, and realistically it's most relevant for organizations building private AI infrastructure rather than consuming AI as a service. The NVIDIA and Dell partnership also means the tool is optimized for that specific hardware stack, which may or may not match your environment.
How to Choose the Right Tool
The AI security market is fragmenting fast, and most vendors are claiming to solve the entire problem. They're not. Before you evaluate any tool, map your actual AI risk surface: Are you building internal LLM applications? Letting employees use public AI tools? Running AI agents with access to production systems? Deploying dedicated AI infrastructure? The answers determine which category of tool you need, and most organizations need more than one.
Identify whether your risk is in AI systems you build or AI tools your employees use. Tools like Prisma AIRS and Falcon AIDR are built for securing AI systems you own and operate. Tools like SkopeAI and AI Guardian are built for controlling how employees interact with external AI services. These are different problems requiring different solutions.
Check deployment model compatibility before anything else. Most tools in this space are cloud-only. If you have data sovereignty requirements, on-premises AI workloads, or air-gapped environments, your options narrow significantly to tools like FortiAI (hybrid) and Secure AI Factory (on-premises, air-gapped). Don't evaluate a cloud-only tool for an on-premises deployment.
Assess your existing vendor ecosystem honestly. Falcon AIDR's value is almost entirely dependent on already running CrowdStrike. FortiAI's integration depth only matters if you're running Fortinet products. If you're not in those ecosystems, the integration story doesn't apply and you're paying for a weaker standalone product.
Determine whether you need pre-deployment testing, runtime protection, or both. Lakera Red is a red teaming tool for pre-deployment assessment. Prisma AIRS and Falcon AIDR provide runtime detection. SkopeAI and AI Guardian focus on continuous monitoring of data exposure. Conflating these phases leads to buying the wrong tool for your actual gap.
Evaluate AI agent security coverage specifically if you're running agentic workflows. Autonomous AI agents that can take actions in production systems represent a distinct threat model from static LLM applications. Prompt injection into an agent that has write access to your database is a different severity than prompt injection into a chatbot. Prisma AIRS has the most explicit coverage of agentic threats including MCP protocol attacks.
Consider team size and operational capacity. A platform like Prisma AIRS generates posture findings, red team results, and runtime alerts that require a team to operationalize. If you're a three-person security team, you need a tool that surfaces actionable signal without requiring constant tuning. SkopeAI and AI Guardian have narrower scopes that are more manageable for smaller teams.
Look at NIST framework coverage relative to your compliance requirements. If you're subject to frameworks that require supply chain risk management (GV.SC) or technology infrastructure resilience (PR.IR), check which tools actually cover those categories. Secure AI Factory has the broadest NIST coverage. Lakera Red has the narrowest, which is fine if you're using it as one component of a larger program.
Factor in whether you need data governance controls or threat detection controls. These are different capabilities. Data governance (AI Guardian, SkopeAI) tells you what data AI systems can access and whether employees are misusing AI tools. Threat detection (Falcon AIDR, Prisma AIRS) tells you when AI systems are being actively attacked. Most mature AI security programs need both.
Frequently Asked Questions
What is the difference between AI security tools and AI-powered security tools?
AI-powered security tools use machine learning to improve threat detection, alert triage, or anomaly detection in traditional security workflows. AI security tools are designed to protect AI systems themselves, including LLMs, AI agents, training data, and prompts. This roundup covers the latter category, though some tools like FortiAI blur the line by doing both.
Do I need a dedicated AI security tool if I'm only using managed AI services like Azure OpenAI or AWS Bedrock?
Yes, because the cloud provider secures the model infrastructure, not your application layer. Prompt injection, sensitive data leakage through your application, and employee misuse of AI tools are your responsibility regardless of which managed service you use. Tools like SkopeAI and AI Guardian address exactly this gap.
What is prompt injection and why does it matter for enterprise security?
Prompt injection is an attack where malicious input manipulates an LLM into ignoring its instructions or taking unintended actions. When AI agents have access to production systems, databases, or APIs, a successful prompt injection can result in data exfiltration, unauthorized actions, or privilege escalation. It's the SQL injection of the AI era.
Can I use one of these tools to meet AI-related compliance requirements like the EU AI Act?
Several tools in this roundup address compliance controls, particularly Secure AI Factory (data sovereignty, regulatory compliance) and Prisma AIRS (posture management, risk assessment). However, compliance with frameworks like the EU AI Act involves governance, documentation, and risk management processes that go beyond what any single security tool provides.
How do AI red teaming tools differ from traditional penetration testing?
Traditional pen testing targets network infrastructure, applications, and authentication systems using known exploit techniques. AI red teaming specifically tests LLM applications for vulnerabilities like prompt injection, jailbreaking, indirect manipulation through data sources, and model extraction. Lakera Red automates this process using attack patterns derived from real-world adversarial research.
Is AI security only relevant for large enterprises, or do smaller organizations need it too?
Any organization using AI tools in workflows that touch sensitive data has exposure. SMBs using ChatGPT or Copilot for business processes face the same data leakage risks as enterprises. Tools like SkopeAI, AI Guardian, and Lakera Red explicitly support SMB deployments and are more accessible entry points than full enterprise platforms.
Conclusion
AI security is not a future problem. If your organization is using LLMs, AI agents, or generative AI tools today, you have an attack surface that most traditional security tools don't cover. The seven tools in this roundup represent the current state of the market: some focused on runtime protection, some on posture management, some on data governance, and one on the infrastructure layer that everything else runs on. None of them covers the entire problem alone. The right approach is to identify your specific AI risk surface, match it to the tools that address that surface, and build a stack that covers pre-deployment testing, runtime monitoring, and data governance. Browse the full AI security category on CybersecTools at /tools to see additional options, and use the comparison feature at /compare to evaluate these tools side by side against your specific requirements.
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