10 Best AI Security Platforms for Models, Data, and Agents in 2026

AI security has changed faster than most enterprise security stacks have. A bad prompt can now become a tool call, a database write, an OAuth request, or an action inside another system.

That changes the failure mode completely. The problem is no longer just an AI model producing a bad answer; it is what the model or agent can do after that answer. Also, as companies give AI agents greater access, the number of ways things can go wrong grows.

So, this blog covers the best AI security platforms in 2026, what each one protects, and when to choose them.

But before looking at the platforms, it helps to understand exactly what needs to be protected.

Summary

A compact comparison table for quick glance; go through the article for details.

Why AI Security Matters

The more useful AI becomes, the more access we give it. Agents now work across browsers, APIs, SaaS applications, databases, source code, MCP servers, cloud infrastructure, and enterprise identities.

That creates a few very different security problems:

• Prompt injection: An attacker can manipulate what a model or agent does by embedding instructions in prompts, files, websites, or retrieved data.

• Data leakage: Sensitive company information can leave through prompts, model responses, tools, browser sessions, or connected applications.

• Data and model poisoning: Compromised training data, model files, dependencies, or retrieval sources can change how an AI system behaves.

• Agent permissions: An agent with excessive access can turn a model mistake into a real change in Gmail, GitHub, Salesforce, cloud infrastructure, or another system.

• MCP and tool attacks: Agents increasingly rely on external tools and MCP servers, creating additional avenues for malicious instructions, unsafe tools, and exposure of sensitive data.

• Shadow AI: Employees can install AI applications, browser extensions, coding agents, and other tools before security teams even know they exist.

• Recovery: Once an autonomous system changes or deletes something, prevention is only half the problem. Teams also need to understand what happened and recover safely.

This is also why comparing every AI security platform into a single bucket does not really work.

• Some products are trying to protect the AI model, application, prompts and data.

• Others are protecting the endpoint, identity, network, SOC and enterprise systems the AI touches.

Let's start with the first group.

Best platforms that secure AI models and data against adversarial threats

These platforms are built specifically to secure AI systems themselves, protecting models, applications, prompts, data and agent workflows against adversarial threats.

1. Palo Alto Networks: Full-lifecycle security for AI applications, models, data and agents.

Palo Alto Networks' Prisma AIRS covers several stages of the AI security lifecycle rather than focusing on a single guardrail. It includes AI Runtime Firewall and Runtime API, AI Model Security, automated AI Red Teaming, posture management, and AI Agent Security.

The runtime layer can inspect prompts and model responses for prompt injection, sensitive data leakage, and unsafe outputs. Model Security scans AI models separately for risks such as malicious code, deserialization vulnerabilities, and neural backdoors.

Availability

Prisma AIRS Runtime Security, Model Security and AI Red Teaming are currently shipped capabilities. Palo Alto's documentation also lists AI Gateway as a July 2026 addition, together with newer agent-focused red-teaming capabilities.

Pros

• Covers model scanning, runtime security and AI red teaming

• Inspects prompts and model responses

• Detects risks inside AI model files before deployment

• Includes security controls designed specifically for AI agents

• Can test agent attacks such as privilege misuse and tool chaining

Cons

• It is a broad platform with several separate security components

• Runtime Firewall deployments have infrastructure and deployment requirements to consider

When to choose

Choose Palo Alto Networks when you want to secure multiple stages of the AI lifecycle, rather than adding separate products for model scanning, red teaming, and runtime protection.

And if your biggest concern is less about the model file and more about what enters and leaves the application at runtime, the next option takes a more guardrail-focused approach.

2. Check Point: Runtime guardrails for AI applications, with dedicated agent security still in Early Access.

Check Point's AI security stack now incorporates Lakera's technology. Its AI Guardrails layer can inspect AI workflows for prompt attacks, sensitive data leakage, malicious links, unsafe content, and agent behaviour.

Check Point is also building a broader AI Agent Security product that discovers agents, evaluates their configuration, inventories connected tools and MCP servers, and applies runtime policies to prompts, model outputs and tool interactions.

Availability

The important distinction is maturity.

AI Guardrails is available as a standalone runtime layer. The broader Check Point AI Agent Security product remains in Early Access as of August 8, 2026. Its documentation also says native platform runtime integrations remain on the roadmap.

Pros

• Runtime prompt defence for AI applications

• Sensitive-data leakage controls

• Content moderation and malicious-link detection

• Agent discovery and risk assessment

• Policies that can inspect tool calls and tool responses

Cons

• Full AI Agent Security is still Early Access

• The mature Guardrails product and newer agent-security layer should not be treated as having the same level of maturity

When to choose

Choose Check Point when runtime guardrails are the immediate problem you need to solve, especially if agent discovery and agent behaviour controls are something you also want to start evaluating.

Palo Alto and Check Point focus heavily on protecting the AI execution path. SentinelOne takes a slightly different route by combining AI application security with an existing security operations platform.

3. SentinelOne: AI red teaming and agent governance alongside an AI-powered SOC platform.

SentinelOne's AI security story now has two sides. Prompt Security focuses on protecting AI usage and AI applications, while Purple AI applies agentic AI to traditional security operations.

Prompt AI Red Teaming is designed to simulate attacks such as prompt injection, jailbreaks, privilege escalation and data poisoning. Prompt AI Agent Security adds discovery and governance for AI agents and MCP servers.

Availability

This is another platform where the maturity labels matter.

Purple AI Auto Investigation is GA and available to Purple AI Analyst customers. Prompt AI Agent Security is Preview. Prompt AI Red Teaming has been announced, but SentinelOne's announcement does not establish it as GA, so I would treat it as an announced capability rather than a generally available one.

Pros

• Tests AI applications against AI-specific attacks

• Adds agent and MCP discovery to the wider security platform

• Combines AI application security with endpoint, cloud, identity and SOC security

• Purple AI can perform cross-stack autonomous security investigations

Cons

• Prompt AI Agent Security remains Preview

• Maturity varies considerably between established Singularity products and the newer Prompt AI capabilities

When to choose

Choose SentinelOne when you want AI application security and to be connected to the same platform that handles endpoint, cloud, and security operations.

These three primarily focus on securing AI itself. The next group works from the other direction.

AI-powered platforms that protect traditional enterprise infrastructure

These platforms already protect enterprise infrastructure and are now extending those controls to AI agents, AI usage and autonomous operations.

4. CrowdStrike: Falcon security extended into enterprise AI usage, AI applications and MCP agents.

CrowdStrike AIDR instruments AI-powered applications so teams can log AI interactions, detect threats inside prompts and responses, and apply security policies before data reaches the model or user.

It supports application collectors, cloud collectors, gateways and agentic collectors. Its MCP proxy can sit between an MCP client and server to inspect tool traffic, block tool poisoning and stop sensitive values from moving through MCP exchanges.

Availability

AIDR and its documented application, browser, cloud, gateway and MCP collector options are currently present in CrowdStrike's product documentation.

Pros

• Extends Falcon into enterprise GenAI and AI applications

• Detects prompt injection and jailbreak attempts

• Inspects sensitive data in prompts and responses

• Protects MCP traffic between agents and tools

• Connects AI security events to the wider CrowdStrike platform

Cons

• AI-specific capabilities are newer than CrowdStrike's core endpoint and cloud stack

• The MCP proxy only sees MCP exchanges routed through it and does not inspect model prompts outside those interactions

When to choose

Choose CrowdStrike when Falcon already protects a large part of your environment, and you want to extend its visibility and policy layer to AI applications and MCP-based agents.

CrowdStrike follows the activity. Zscaler focuses more heavily on controlling the paths agents and users take to applications, data and other services.

5. Zscaler: Zero Trust controls extended to AI applications, endpoints and agent-to-agent communication.

Zscaler's AI Security portfolio now covers AI asset discovery, access to AI applications, AI governance and protection for AI infrastructure.

Its 2026 additions include AI Broker for MCP and A2A communications, and Endpoint AI Security for AI activity within browsers, extensions, and plugins.

Availability

Zscaler currently markets and documents its broader AI Security platform. AI Broker and Endpoint AI Security were introduced as part of its 2026 product expansion, but the available product material does not clearly label every newly announced capability as GA.

So I would describe those features as announced 2026 capabilities unless the specific deployment documentation establishes otherwise.

Pros

• AI application and asset visibility

• Zero Trust controls for enterprise AI access

• MCP and A2A policy direction through AI Broker

• Endpoint visibility into AI use in browsers, plugins and extensions

• DLP and governance around enterprise AI usage

Cons

• Some of its newest agent-focused capabilities are recent 2026 additions

• Its core strength is access, traffic and data controls rather than scanning model files for malicious artefacts

When to choose

Choose Zscaler when the main question is not “is this model file safe?” but “which user or agent can reach this application, data source or service, and what are they allowed to do?”

Once agents have access, however, another problem appears: identity.

6. Okta: Identity and least-privilege access for AI agents.

Okta approaches AI security by treating agents as identities rather than treating them as another application.

Okta for AI Agents lets organisations register agents, assign owners, give them scoped access, replace standing credentials with short-lived tokens, govern MCP-server access, review permissions, and deactivate agents when necessary. Core Okta for AI Agents is now generally available.

Availability

Okta for AI Agents is GA.

The GA product includes agent discovery and onboarding, managed least-privilege connections, MCP-server governance, lifecycle management, access reviews, agent deactivation and audit telemetry.

Okta separately lists secure agent-to-agent delegation, Agent Gateway, additional threat detection and human-in-the-loop controls among capabilities it plans to add in the months ahead.

Pros

• Gives AI agents individually governed identities

• Replaces long-lived credentials with scoped access

• Governs access to APIs, applications, secrets and MCP servers

• Adds lifecycle controls and access reviews for agents

• Records tool calls and authorisation decisions for auditing

Cons

• Does not replace prompt injection or model-security products

• Several broader multi-agent security controls are still future additions

When to choose

Choose Okta when the biggest question is simple but dangerous: what can this agent access, and for how long?

Identity tells you what an agent can do. Dynatrace is useful when you need to understand what the agent is actually doing once it is running.

7. Dynatrace: Observability for AI agents and the infrastructure underneath them.

Dynatrace's AI Observability product traces AI applications, LLMs and agents along with the production systems they depend on.

Teams can trace a request across agent handoffs, tool calls, retrieval steps, and model interactions while also tracking latency, failures, and infrastructure dependencies.

Dynatrace is also moving deeper into autonomous operations, including agents for incident triage and remediation while retaining human oversight.

Availability

AI Observability is currently available.

Not every agentic feature is equally mature. For example, Dynatrace Query Agent is currently in Preview, while additional autonomous operations capabilities were announced in July 2026.

Pros

• Traces AI agents alongside applications and infrastructure

• Shows tool calls, prompts, responses and dependencies

• Helps debug complex multi-step AI workflows

• Connects agent activity to production telemetry

• Adds AI-assisted investigation and operations

Cons

• Observability-first rather than a dedicated model-security platform

• Some newer autonomous-agent capabilities remain Preview or recently announced

When to choose

Choose Dynatrace when the difficult part is figuring out what an agent did, which systems it touched, where the failure happened and what changed downstream.

The same visibility problem exists on employee devices, especially as AI applications and agents spread outside centrally managed projects.

8. Fortinet: Endpoint and network security with growing controls for enterprise AI usage.

Fortinet is extending its existing endpoint, network and SOC stack into AI governance rather than building a separate AI model-security product.

FortiEndpoint combines endpoint protection, EDR, ZTNA, secure access and data protection. It can also provide visibility into sanctioned and shadow AI usage across endpoints.

Availability

The distinction between maturity here is particularly important.

Core FortiEndpoint endpoint, EDR and access capabilities are available today. However, Fortinet explicitly marks blocking unauthorised AI tools and automatically inspecting sensitive data shared with AI agents and GenAI applications as coming in 2H 2026.

Pros

• Combines endpoint protection, EDR, ZTNA and data security

• Provides visibility into enterprise AI usage

• Can identify sanctioned and shadow AI activity

• Fits into Fortinet's wider network and security environment

Cons

• Some important AI-governance and AI-focused DLP controls are not available yet

• Primarily protects the surrounding enterprise environment rather than the internal security of an AI model

When to choose

Choose Fortinet when endpoints, networks and enterprise data remain the main assets you need to protect, but AI applications and agents are becoming another source of activity inside that environment.

And even with good prevention, something eventually goes wrong. That is where the final platform differs from most on this list.

9. Rubrik

Governance and recovery for actions taken by AI agents.

Rubrik's angle is less about blocking prompt injection and more about what happens when an agent has legitimate access but takes the wrong action.

Rubrik Agent Cloud can govern which MCP resources and tools agents can access, issue scoped short-lived tokens for tool calls, and apply policy controls around agent behaviour. Agent Rewind is designed around recovering from unwanted or destructive agent-driven changes.

Availability

Rubrik Agent Cloud is an active product with documented capabilities for Agent Govern and Agent Rewind. Its current product material does not label those core capabilities as Early Access.

Pros

• Governs agent access to tools and enterprise resources

• Uses scoped, short-lived credentials

• Creates an audit trail around agent actions

• Adds a recovery path when autonomous actions go wrong

Cons

• Primarily focused on governance, resilience and recovery

• Does not replace dedicated model scanning, red teaming or prompt-layer security

When to choose

Choose Rubrik when agents can modify important enterprise systems or data, and you care as much about recovering safely as you do about stopping the action in the first place.

These five primarily focus on securing enterprise systems related to AI. The final platform works at the layer in between, controlling how AI agents access and use those systems.

Composio: Platform that controls AI agent access to enterprise tools and applications

As agents gain access to more tools, APIs, and SaaS applications, another layer is becoming increasingly important: controlling what an agent can connect to and what it is allowed to do once connected.

That is where the Composio MCP Gateway fits.

Composio MCP Gateway sits in that layer, controlling which tools agents can access, which actions are allowed, and how users authenticate.

It supports:

• team-specific MCP endpoints,

• toolkit and action-level restrictions,

• SSO,

• SCIM provisioning and

• audit logs for tool execution.

So rather than replacing prompt security, model scanning or endpoint protection, its role is narrower: limiting what an agent is allowed to touch once it can act.

And that brings us back to the larger point: AI security still starts with deciding what the AI should be allowed to access.

Conclusion

AI security is becoming essential because AI is no longer sitting inside a chat window. It can authenticate, retrieve data, call tools, change files and take actions across the same systems people use every day.

But no platform can compensate for bad security habits.

Giving an agent admin access everywhere, leaving old OAuth connections active, approving every MCP server, reusing credentials, skipping MFA, or allowing destructive actions to run without approval creates problems before any security product is involved.

So start with the boring stuff: use MFA, remove unused access, keep permissions narrow, review OAuth and MCP connections, separate sensitive data, and require approval before consequential actions.

Then choose the platform that protects the part of the stack you actually need.

The best AI security stack still starts with deciding what the AI should be allowed to touch.Sources {toggle="true"}