Best AI Security Platforms for Enterprise AI Traffic in 2026
TL;DR
- AI security platforms divide by where they sit: endpoint and SOC tools watch devices and networks, posture tools scan configuration, and inline platforms inspect the prompts and responses themselves. Only inline platforms can stop a prompt before it leaves the network.
- Prompt injection and sensitive information disclosure are the two highest-ranked risks in the OWASP Top 10 for LLM Applications, and both occur in request and response content, which is why the traffic layer is where they are best addressed.
- Bifrost applies guardrails as rules and reusable profiles, with three native providers and ten third-party integrations, and supports detect-only, block, and redact actions on input, output, or both.
- Secrets detection runs entirely in-process using the embedded Gitleaks rule set, so credential scanning requires no external service call and no prompt content leaves the gateway to be checked.
- Streaming is inspected rather than skipped: a rule capable of blocking causes Bifrost to hold the complete stream until generation and guardrail evaluation both finish.
An engineer debugging a production issue pastes a stack trace containing a database connection string into a model, and the credential is now in a third party's logs. No endpoint agent blocked it, because nothing malicious ran on the laptop, and no network tool blocked it, because the request was ordinary HTTPS to an approved domain. Bifrost, the open-source AI gateway written in Go 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 addresses that class of incident by inspecting the content itself. This article is organized as a filesystem so each control can be evaluated independently.
/ai-security/
|-- 00-definition.md what AI security covers at the traffic layer
|-- 01-categories.md endpoint, posture, and inline platforms
|-- 02-threats/
| `-- mapping.spec which OWASP risks this layer can address
|-- 03-controls/
| |-- guardrails.conf rules, profiles, targets, actions
| |-- secrets.md in-process credential detection
| |-- redaction.spec strategies, modes, and the PII boundary
| `-- streaming.md inspection without breaking the stream
|-- 04-evaluation/
| |-- control-matrix.tsv what to compare
| `-- coverage.md the traffic that never reaches the gateway
|-- 05-faq.faq questions this document answers
`-- 06-next.md the two tests that matter
/00-definition: What AI Security Means at the Traffic Layer
AI security at the traffic layer is the inspection and control of prompts and model responses in flight, so that prohibited content is blocked or redacted before it reaches a provider or a user. It is a distinct discipline from endpoint security and from cloud posture management, because the asset being protected is the content of a request rather than a host or a configuration.
The reason this layer exists as a separate category is that conventional controls cannot see into it. A data-loss prevention tool tuned for email attachments does not parse a JSON request body destined for a model API. A network control that allows traffic to an approved provider domain allows everything sent to it. The content is the payload and the payload is the risk, which is the argument developed in enterprise AI security as a set of controls on model traffic, and at greater length in the complete guide to AI and GenAI security.
/01-categories: The Three Kinds of AI Security Platform
Comparing AI security platforms is difficult mainly because three different product categories answer to the name. Each protects a different asset, and a shortlist mixing all three without saying so produces a matrix where every product looks incomplete.
| Category | Asset protected | Sees prompt content | Can block a prompt in flight |
|---|---|---|---|
| Endpoint and SOC tooling | Devices, identities, network sessions | No | No |
| AI security posture management | Configuration, model inventory, permissions | No | No |
| Inline AI traffic security | The request and response themselves | Yes | Yes |
None of the three is redundant. Posture management answers which models exist and who can reach them, and AI security posture management for LLM applications covers that scope. Endpoint tooling remains the control for malware and credential theft. Inline inspection is the only one of the three that can prevent a specific prompt from being sent, and it is the category most enterprise programs are missing when an incident like the one above occurs. The full GenAI security picture places all three in one model. Enterprise AI security as a reference architecture shows how the three compose.
/02-threats: The Risks That Live in Request and Response Content
Threat modeling for this layer is well served by an existing catalog rather than a bespoke one. The OWASP Top 10 for LLM Applications ranks prompt injection first and sensitive information disclosure second, with excessive agency sixth, and all three are content-and-traffic problems rather than host problems.
Prompt injection is the case where untrusted text, retrieved from a document, a web page, or a tool result, reaches a position where the model treats it as instruction. The gateway's contribution is not a complete solution, and it is worth being precise about that: no inline filter reliably detects all adversarial instructions, because the attack surface is natural language. What a gateway can do is bound the consequences, by restricting which tools a credential may call and inspecting tool arguments before execution, so an injected instruction that asks for a file read meets an allow-list rather than a filesystem.
Sensitive information disclosure is the more tractable of the two and the more common in practice. Credentials, personal data, and internal identifiers leave the network inside prompts, usually by accident, and this is where inline inspection produces a clean prevention rather than a partial mitigation. GenAI security risks and controls for production LLM applications enumerates the categories, and AI security risks for engineering teams covers the practical exposure.
02-threats/mapping.spec
-----------------------
llm01_prompt_injection partially mitigated: bound the blast radius via tool allow-lists
llm02_data_disclosure directly preventable: inspect and block or redact in flight
llm06_excessive_agency directly preventable: per-tool policy enforced at request time
not_addressed_here model poisoning, supply chain, vector store weaknesses
honest_limit no inline filter detects all adversarial natural language
/03-controls/guardrails: Rules, Profiles, Targets, and Actions
The control requirement is that inspection must be able to refuse, must cover both directions of the exchange, and must be configurable per team rather than globally. Guardrails in the Bifrost gateway are built from two objects: rules, which use CEL to decide when a check applies, and profiles, which define how the check runs and are reusable across rules.
Each rule carries a target of either llm or mcp, and an apply_to value of input, output, or both. For llm rules, input means before the request reaches the provider and output means after the response returns. For mcp rules, input means the tool arguments before execution and output means the tool result. The two targets expose different CEL variables and are isolated from each other, which prevents the frequent misconfiguration of assuming a prompt-side rule also covers tool output. Both targets can read headers, the virtual key, the customer, the team, and the user, so a rule can be scoped to one team without duplicating the profile.
Available actions are detect-only, block, and redact. Three guardrail providers are native to Bifrost and ten are third-party integrations, including AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, and CrowdStrike AIDR. The guardrails resource page documents the configuration model, and guardrails at the gateway layer covers how teams usually sequence them.
03-controls/guardrails.conf
---------------------------
objects rules (CEL, decide when) and profiles (provider config, decide how)
targets llm (model traffic) and mcp (tool calls), variables isolated per target
apply_to input, output, or both
actions detect_only, block, redact
native_providers secrets detection, custom regex, prompt guardrails (LLM-as-judge)
third_party 10 integrations incl. Bedrock Guardrails, Azure Content Safety, Model Armor
scoping rules read headers, virtual key, customer, team, user
/03-controls/secrets: In-Process Credential Detection
Credential leakage into prompts is the highest-frequency, lowest-sophistication incident in this category, and it has an unusually clean control. Secrets detection in Bifrost embeds the Gitleaks rule set and runs entirely inside the gateway process, with no call to an external service.
In-process execution is the property that matters most, and for two reasons. Latency, because a network round trip to a scanning service on every request is a cost no inline control can absorb at scale. And residency, because sending prompt content to a third party in order to check whether it contains sensitive content reintroduces the exact risk being controlled. Coverage spans cloud provider credentials, source-control and DevOps tokens, package registry tokens, AI provider keys, monitoring and analytics keys, payment and CRM credentials, private key material, and generic patterns such as JWTs and curl authorization headers. The default action is to block.
One scope limit is worth recording: detection is text-only and does not inspect image pixels or binary file contents. A credential inside a screenshot is not caught by this control.
/03-controls/redaction: Strategies, Modes, and the PII Boundary
Redaction is the middle path between blocking a request and allowing it, and it is usually the right default for personal data, where the prompt is legitimate and only the identifier is not. Custom regex provides organization-specific patterns and ships a PII detection template.
Redaction offers three strategies, replace, mask, and hash, across three modes. Runtime redaction alters the content that reaches the provider. Logs-only redaction leaves the request intact but scrubs what is stored, which is the right choice when the model needs the data but the log store should not hold it. Reversible runtime redaction substitutes and can restore, which keeps a response coherent when an identifier has to survive the round trip.
The boundary here should be written into the evaluation record rather than discovered in production. The built-in PII template covers email addresses, US phone numbers, US Social Security numbers, credit-card-shaped numbers, and IPv4 addresses. It does not cover personal names, because a regular expression cannot classify them. This layer is pattern matching, not semantic PII classification, and treating it as the latter is how names end up in provider logs. Name and address detection requires one of the semantic PII providers configured as a profile.
03-controls/redaction.spec
--------------------------
strategies replace, mask, hash
modes runtime, logs_only, runtime_reversible
pii_template email, US phone, US SSN, credit-card-shaped number, IPv4
not_covered personal names and addresses (regex cannot classify them)
name_detection requires a semantic PII provider profile
engine RE2 patterns, flags i m s and combinations
/03-controls/streaming: Inspecting Responses That Arrive in Chunks
Streaming is where inline security implementations most often degrade to detect-only without saying so, because inspecting a response that has not finished arriving requires a decision about whether to hold it. The requirement is that a blocking rule must actually block a streamed response, not log it after delivery.
Bifrost handles the three cases distinctly. Detect-only and logs-only rules observe the stream without delaying client delivery. Runtime redaction releases safe text as it is generated. And where any matched rule is capable of blocking, Bifrost holds the complete stream until both generation and guardrail evaluation finish, which trades perceived latency for actual enforcement. Optional pacing is available through a replay interval, disabled by default, with the largest configured interval winning across rules. The pacing interval applies to streaming chat completions, text completions, and the Responses API, and the streaming documentation covers the transport side.
This is the single most useful thing to demand a live demonstration of during an evaluation. Ask to see a streamed response that must be blocked, and watch whether it is withheld or merely flagged after the user has already read it.
/04-evaluation/control-matrix: Comparing AI Security Platforms
Score inline candidates on the rows below. Each is either enforced on the request path or it is not, and each can be tested in staging within an hour. Where a control catalog is required, the NIST AI Risk Management Framework provides the govern, map, measure, and manage structure these rows report into.
| Control | Requirement to hold | Bifrost | How to verify |
|---|---|---|---|
| Direction coverage | Inspects input and output | apply_to input, output, or both |
Block on a response, not just a prompt |
| Action range | Can block, not only detect | detect_only, block, redact | Confirm a request is refused |
| Credential detection | Runs without an external call | In-process, embedded Gitleaks rules | Send a test key, check for egress |
| PII handling | Redacts rather than blocks | replace, mask, hash across three modes | Redact in logs only, read the log |
| Name detection | Semantic, not regex | Requires a semantic PII provider | Send a name, confirm the behavior |
| Streaming enforcement | Holds stream for blocking rules | Hold-until-complete | Stream a response that must be blocked |
| Tool-call inspection | Arguments and results | Rules with mcp target |
Send a secret as a tool argument |
| Per-team scoping | Rules scoped by credential | CEL on virtual key, team, customer, user | Apply one rule to one team |
| Latency budget | Inspection cost bounded | 11 microseconds gateway overhead at 5,000 RPS | Measure with rules enabled |
| Audit | Per-request record, separate admin trail | Request logs plus HMAC-signed audit logs | Change a rule, find the record |
| Deployment | Runs inside your network | Open source, in-VPC, clustered | Deploy without vendor egress |
Deployment belongs near the top of the scoring rather than the bottom for security workloads specifically, because an inline control that ships prompt content to a vendor's cloud to be inspected has moved the data rather than protected it. The Bifrost AI gateway is open source and self-hostable, in-VPC deployment covers Google Cloud, AWS, Azure, Cloudflare, and Vercel with a 99.95% monthly uptime commitment, and clustering keeps policy consistent across nodes.
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.
Security at Bifrost documents the supply-chain and infrastructure posture of the gateway itself, which is a fair question to ask of any security product.
/04-evaluation/coverage: The Traffic That Never Reaches the Gateway
Inline inspection protects the traffic that routes through it, which makes coverage the real limit on this entire category. A guardrail configured perfectly does nothing about a prompt typed into a desktop chat application that was never pointed at the gateway.
Closing that gap means extending enforcement to the machine rather than writing a stricter policy. The Bifrost AI gateway stays the control plane where guardrail rules and profiles are defined, and Bifrost Edge carries that enforcement to the endpoint by routing AI traffic from desktop applications, browser AI, coding agents, and their MCP servers through the same gateway with no per-application setup. Endpoint security and guardrails describes how the existing rules apply there unchanged: a guardrail runs before the prompt reaches a model and before the response returns, so a secret is caught before it leaves the machine. Edge runs on macOS, Windows, and Linux and deploys via Jamf, Microsoft Intune, Kandji, Omnissa Workspace ONE, and JumpCloud. Bifrost Edge is in alpha and onboarding by request, so treat it as an early-access capability in planning.
Two surfaces account for most of the uncovered traffic in practice. Governing browser-based AI tools covers AI in the browser, securing coding agents covers Cursor and terminal agents, and a secure AI deployment checklist sequences the rollout.
/05-faq: Frequently Asked Questions
What are the best AI security platforms?
The best AI security platform depends on which asset is unprotected. For devices and identities, endpoint and SOC tooling remains correct. For model inventory and permissions, posture management is the fit. For preventing a specific prompt or response from carrying prohibited content, an inline platform on the traffic path is the only category that can act, and Bifrost occupies that position while also serving as the gateway itself.
What are the best enterprise AI platforms?
For enterprises the durable answer is an architecture rather than a single product: route all model traffic through one gateway so that identity, spend, content inspection, and audit are applied once rather than per application, and keep model choice changeable behind that gateway. Bifrost covers 25+ providers and 10,000+ models through a single OpenAI-compatible API, which keeps the model decision reversible.
How to secure AI in enterprise?
Start where the data actually moves. Route every model and tool call through a gateway, authenticate each caller with a revocable credential, restrict which models and tools that credential may reach, inspect prompts and responses with guardrails that can block rather than only flag, and keep two records: one of requests, one of administrative changes. Then extend that enforcement to endpoints, which is where coverage usually fails. GenAI security governance at scale sets out the sequence.
Can an AI gateway stop prompt injection?
Not completely, and any product claiming otherwise is overstating the state of the art, because the attack surface is natural language. What a gateway can do is limit the damage: restricting which tools a credential may call and inspecting tool arguments before execution means an injected instruction is refused at the policy layer rather than executed. Treat it as blast-radius reduction rather than prevention.
What is the difference between AI guardrails and LLM guardrails?
The terms are used interchangeably, and where a distinction is drawn, LLM guardrails refers specifically to checks on model prompts and completions, while AI guardrails is the broader label that also covers agent tool calls. Bifrost treats them as two rule targets, llm and mcp, with isolated variables, precisely so a policy written for one is not assumed to cover the other. MCP gateways for security and compliance covers the tool-call side.
Does inline AI security inspection add significant latency?
The gateway overhead is small: 11 microseconds per request at 5,000 requests per second on an AWS t3.xlarge, with a 100% success rate. In-process controls such as secrets detection add no network round trip at all. The measurable cost arrives with third-party guardrail providers, which call an external service, and with blocking rules on streamed responses, which hold the stream until evaluation finishes.
/06-next: Getting Started with Inline AI Security
Two tests separate an inline AI security platform from a monitoring tool, and both take minutes. Send a known test credential inside a prompt and confirm the request is refused rather than recorded. Then stream a response that must be blocked and confirm it is withheld rather than flagged after the user has read it.
To run those tests against your own traffic and map the results to your control catalog, book a demo with the Bifrost team, or start from the guardrails documentation and configure the first secrets-detection rule yourself.
