Top AI Security Platforms for Enterprise Teams in 2026
TL;DR
- AI security platforms protect the three surfaces of enterprise AI use: the data in prompts, the behavior of models, and the actions agents take.
- The category spans AI gateways that govern traffic, runtime protection for prompts and responses, model and supply-chain security, and red-teaming, and most enterprises combine more than one.
- Bifrost leads for teams that need to govern and secure all LLM traffic at the source, enforcing access control, guardrails, and audit logging at the gateway across every model and provider.
- Consolidation reshaped the market in 2025: Protect AI joined Palo Alto Networks, Prompt Security joined SentinelOne, and Lakera joined Check Point.
- The right choice depends on where the risk sits: gateway control for traffic and governance, model-security platforms for the ML supply chain, and runtime tools for application-layer defense.
Enterprise teams evaluating AI security platforms in 2026 are protecting three things at once: the sensitive data in their prompts, the behavior of their models, and the actions their agents can take. Bifrost, the open-source AI gateway built by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability, and it approaches security from the traffic layer where every model request can be governed. This guide ranks the top AI security platforms for enterprise teams and explains the criteria that separate them.
What to Look for in an AI Security Platform
The best AI security platform for a team depends on which risk surface it needs to cover, but five criteria apply across the category. A platform should govern access, inspect content, control cost, produce compliance evidence, and fit the team's deployment model without adding latency or lock-in.
| Criterion | Why it matters | What to check |
|---|---|---|
| Coverage of LLM traffic | Uninspected requests are ungoverned risk | Does every request pass through the control? |
| Access and identity | Least privilege limits blast radius | Per-team keys, RBAC, revocation |
| Content guardrails | Injection and leakage live in content | Real-time input and output validation |
| Compliance evidence | Auditors need proof controls ran | Signed, retained, exportable logs |
| Deployment model | Sensitive data cannot leave the boundary | In-VPC, on-prem, air-gapped options |
These criteria mirror the reference architecture for governing model traffic that enterprise teams use to structure an evaluation.
Weight the criteria by where the risk concentrates. For most enterprises, the highest-volume exposure is data moving through model traffic, so coverage and content guardrails carry the most weight, followed by compliance evidence in regulated settings. A platform that scores well on a narrow surface but leaves traffic uninspected still leaves the largest gap open. The LLM gateway buyer's guide breaks these trade-offs down in more detail for teams building a shortlist.
AI Security Platforms Compared at a Glance
The platforms below cover different parts of the AI security surface, and the table summarizes where each fits before the detailed entries. Bifrost leads because it governs and secures LLM traffic at the source, the control point every request already passes through.
| Platform | Primary focus | Best-fit surface |
|---|---|---|
| Bifrost | AI gateway and security control plane | LLM traffic governance and guardrails |
| Lakera (Check Point) | Runtime GenAI application security | Prompt injection and content defense |
| Protect AI (Palo Alto Networks) | MLSecOps and model security | ML supply chain and model scanning |
| HiddenLayer | AI detection and response | Adversarial ML and model protection |
| CalypsoAI | Model red-teaming and runtime defense | Offensive testing and inference control |
A broader field is surveyed in the best enterprise AI security platforms guide; this list focuses on the platforms enterprise teams shortlist most often.
1. Bifrost
Bifrost is an open-source AI gateway that unifies access to over a thousand models behind a single API and makes security a property of the traffic layer. Every request routes through one control point, so access control, guardrails, rate limits, and audit logging apply to every model, provider, and application uniformly, which is what makes it the strongest fit for enterprise AI security.
Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.
Top capabilities:
- Governance and access control through virtual keys, with per-team budgets, rate limits, and instant revocation, plus role-based access control and data access scoping.
- Real-time guardrails that validate inputs and outputs against content-safety and secrets-detection policies, extended to MCP tool calls for agent safety.
- Compliance-grade audit logging with signed, exportable audit trails supporting SOC 2, GDPR, HIPAA, and ISO 27001.
- Enterprise deployment in your own network through in-VPC and on-prem options, with published performance benchmarks of 11 microseconds of overhead at 5,000 requests per second.
Bifrost also secures the agent surface, not just chat traffic. As an MCP gateway, it centralizes the external tools an agent can reach, applies tool-level filtering per virtual key, and runs guardrails on tool executions, so an agent cannot call a tool or take an action outside its policy. This closes the excessive-agency risk that grows as teams move from single prompts to autonomous agents.
Because these controls share one control point, Bifrost is both an AI security control plane and the routing and observability layer, so teams do not run security separately from the rest of their AI infrastructure. Teams migrating from a self-managed proxy can evaluate it as a drop-in alternative, keeping their existing SDK code and changing only the base URL.
2. Lakera (Check Point)
Lakera is a runtime GenAI application security platform focused on real-time defense against prompt injection, data leakage, and unsafe content. It positions itself at the application layer, inspecting prompts and responses as they flow between an application and a model, and it became part of Check Point Software in 2025 as the anchor of a broader AI security stack.
Best for: teams that want a dedicated runtime guardrail product for prompt-injection and content defense at the application layer.
Lakera's strength is depth in prompt-level threat detection, backed by a large corpus of adversarial examples gathered from public red-teaming exercises. For teams that would rather enforce the same class of protection at the traffic source rather than wiring a detector into each application, Bifrost runs input and output guardrails at the gateway so every application inherits the policy without per-app integration, and can route to a specialized detection provider on the same profile when a workload needs it.
3. Protect AI (Palo Alto Networks)
Protect AI is an MLSecOps platform focused on securing the machine learning supply chain: scanning models for threats, tracking model provenance, and managing risk across the ML lifecycle. It was acquired by Palo Alto Networks in 2025 and folded into that vendor's broader AI security portfolio.
Best for: teams whose primary risk is the ML supply chain, including model scanning, artifact integrity, and MLSecOps workflows.
Protect AI addresses build-time and model-artifact risk, including scanning open-source models for malicious code before they reach production. That is a real and distinct exposure, but it sits earlier in the lifecycle than live traffic. It pairs naturally with a traffic-layer control: Bifrost governs the runtime path where prompts and responses actually move, which is where LLM security tools for enterprise applications concentrate their controls.
4. HiddenLayer
HiddenLayer is an AI detection and response platform focused on protecting machine learning models from adversarial attacks, model theft, and tampering. Its approach centers on monitoring model behavior to detect attacks against the model itself, a discipline drawn from traditional detection and response applied to ML.
Best for: teams protecting proprietary models from adversarial ML attacks and model extraction.
HiddenLayer's focus is the model as an asset, which matters most for teams that train and ship their own proprietary models and treat model weights as intellectual property. For organizations whose exposure is instead the data flowing to third-party models, the control that matters is at the request path, where Bifrost enforces guardrails and governance across all model traffic regardless of which provider serves a request.
5. CalypsoAI
CalypsoAI is a model security platform focused on red-teaming and runtime defense, offering offensive testing to find weaknesses and inference-time controls to enforce policy on model interactions. It targets teams that want to probe models adversarially before deployment and constrain them in production.
Best for: teams that prioritize offensive testing and red-teaming alongside runtime inference controls.
Red-teaming finds weaknesses; enforcement closes them. A red-team result is only useful if the fix is applied to every request afterward, and that requires a control point in production. Bifrost provides the enforcement half at the gateway, applying guardrail policies that every request must pass, and pairing them with the audit evidence that a red-team finding was actually remediated in production traffic.
Common AI Security Risks These Platforms Address
The platforms in this list address a shared set of AI security risks, even though each emphasizes a different surface. The risks map closely to the OWASP framework for LLM applications, which gives a common vocabulary for comparing coverage.
- Prompt injection, the top entry in the OWASP Top 10 for LLM Applications 2025, where crafted input overrides intended instructions.
- Sensitive information disclosure, where prompts or responses leak PII, secrets, or proprietary data.
- Improper output handling, where unvalidated model output reaches downstream systems.
- Excessive agency, where an agent takes actions beyond what it should be permitted to do.
Gartner's forecast that over 40% of AI data breaches will stem from cross-border GenAI misuse by 2027 underlines why these controls belong at the point where traffic leaves the organization.
The value of enforcing at the gateway is that a single control point addresses several of these risks at once. Access control limits which models and data a request can reach, input guardrails catch injection and sensitive data before a prompt is sent, output guardrails catch leakage and improper output before a response returns, and tool-level policy constrains agent actions to limit excessive agency. Rather than buying a separate product for each risk, a team covers the shared core at one layer and reserves specialized tools for the surfaces a gateway does not touch. A fuller treatment of the risk landscape is in this guide to AI and GenAI security.
How to Choose an AI Security Platform for Your Team
Choosing an AI security platform starts with locating your primary risk. If the exposure is data leaving through model traffic, a gateway is the right control; if it is the ML supply chain, a model-security platform fits; if it is application-layer attacks, a runtime tool applies. Most enterprises combine a traffic control plane with one specialized tool.
For the majority of teams, the traffic layer is where the largest and most immediate risk sits, because every prompt and response passes through it and most sensitive data moves there. Starting with a gateway gives access control, guardrails, and audit in one enforcement point, and it also closes the shadow AI gap when extended to the endpoint, an approach detailed in shadow AI risks, governance, and security.
A practical sequence is to secure the traffic layer first, then layer specialized tools onto the surfaces a gateway does not cover. Deploy the gateway to govern access and inspect content, add a model-security platform if you ship proprietary models, and bring in red-teaming before major releases. Sequencing this way means the highest-volume risk is covered from day one, and each later addition targets a specific, known gap rather than duplicating controls. Teams shortlisting options can compare the field in the enterprise AI security platforms overview and the top AI security platforms roundup.
Frequently Asked Questions
What are AI security platforms?
AI security platforms are tools that protect enterprise use of AI across three surfaces: the data in prompts, the behavior of models, and the actions agents take. The category spans AI gateways that govern traffic, runtime tools that defend prompts and responses, model-security platforms that secure the ML supply chain, and red-teaming tools. Most enterprises combine a traffic control plane with one or more specialized products.
What is the best AI security platform for enterprise teams?
The best platform depends on the primary risk surface. For governing and securing all LLM traffic, Bifrost leads, because it enforces access control, guardrails, and audit logging at the gateway that every request passes through. For ML supply-chain risk, a model-security platform fits better, and for adversarial model attacks, a detection-and-response tool applies. Many teams run a gateway plus one specialized platform.
How are AI security platforms different from traditional security tools?
Traditional security tools inspect networks, endpoints, and code, but AI security risks live in the content and behavior of model interactions. A prompt can exfiltrate data, a response can leak secrets, and an agent can take an unintended action, none of which packet or file inspection catches. AI security platforms add controls that read and act on request and response content, and that govern which models and tools an application can reach.
Which AI security companies were acquired recently?
The market consolidated in 2025. Palo Alto Networks acquired Protect AI, SentinelOne acquired Prompt Security, and Check Point acquired Lakera. These moves fold specialized AI security capabilities into larger platform vendors. Independent platforms such as HiddenLayer and CalypsoAI continue to focus on model protection and red-teaming, while open-source options like Bifrost cover the traffic-governance layer.
Do AI security platforms work with any model provider?
It depends on the platform, but traffic-layer tools are provider-agnostic by design. Bifrost sits in front of over a thousand models across supported providers through one API, so its access control, guardrails, and audit logging apply the same way whether a request goes to OpenAI, Anthropic, a cloud provider, or a self-hosted model. Model-security and runtime tools vary in provider coverage and should be checked against your stack.
Try Bifrost for Enterprise AI Security
Choosing among AI security platforms comes down to where your risk sits, and for most enterprise teams the largest exposure is the LLM traffic itself. Bifrost governs and secures that traffic at the source, with access control, guardrails, and audit logging in one enforcement point across every model and provider, and it runs inside your own network when data sensitivity requires it. To evaluate it against your requirements, explore the Bifrost platform, review the enterprise capabilities, or book a demo with the Bifrost team.
