Best Runtime AI Security Platforms for the Enterprise (2026): 11 Policy Enforcement Platforms Compared

A runtime AI security platform enforces policy on AI applications and agents while they run: it inspects prompts, responses, retrieved content and tool calls, and blocks, masks or logs them before harm happens. The platforms below differ less in what they detect than in where they enforce. Some enforce inside the application, some at the AI gateway, some on the network, some on the developer's laptop, and some at the agent's tool calls. A platform can only enforce policy on traffic it intercepts. This guide compares eleven platforms on the same seven questions, starting with where each one can actually stop an action.

Our recommendation: Pillar Security is the runtime AI security platform we recommend for enterprises that build AI applications and agents. It enforces one policy at every AI gateway the enterprise runs (LiteLLM, Kong, TrueFoundry, Agent Router, Open WebUI, or any other through its API) and on coding agents on developer machines (Claude Code, Codex CLI and Cursor). It inspects tool calls as well as prompts, and it keeps one audit trail for all of them. Choose a network or SASE platform (WitnessAI, Cisco, Check Point, Netskope, Zscaler) if your main problem is employees using public AI tools in the browser. Choose your cloud provider's guardrails if all your AI runs in one cloud and content safety is the main need.

Product facts are as of October 9, 2026, and come from each vendor's public documentation and product pages. Pillar publishes this guide and is assessed on the same rubric as every other platform, including the limitations of its own product.

Key takeaways

• A detector is not an enforcement point. Many products can score a prompt as malicious. Fewer can stop the request, mask the data or block the tool call in the path. Ask every vendor where its verdict is enforced and what happens when its service times out.
• There are five places to enforce, and most enterprises need three of them. The application, the AI gateway, the network or browser, the developer endpoint, and the agent's tool calls. Applications and agents you build are best covered at the gateway and the tool call. Employees' use of public AI tools is best covered on the network or in the browser. Coding agents need the endpoint, because their actions never cross a gateway.
• Agents moved the risk from answers to actions. A guardrail that reads only the user's message misses the instruction hidden in a tool result, a retrieved document or an MCP server's description. Check that the platform inspects tool definitions, calls and results, not just prompts and responses.
• The audit trail is now part of the product. The EU AI Act's logging duties for high-risk systems, ISO/IEC 42001's operation and monitoring controls, and HIPAA's audit-control requirement all ask for records you can produce on demand. Runtime enforcement is where those records come from.
• The market consolidated into suites. Palo Alto Networks owns Protect AI and Portkey, Check Point owns Lakera, F5 owns CalypsoAI, Cisco owns Robust Intelligence, and CrowdStrike owns Pangea. CrowdStrike's documentation says Falcon AIDR reaches end of support on November 30, 2026, with its capabilities continuing in Falcon Guardian. A platform tied to one suite, gateway or cloud is a different bet from an independent one.

What is runtime AI security?

Runtime AI security is the set of controls that inspect and act on AI interactions in production: prompts, model responses, retrieved content, tool calls and agent actions, as they happen.

It sits next to three other disciplines that are often sold under the same name:

A complete program uses all four. This guide covers the third. The AI security frameworks comparison covers how they fit together, and the AI red teaming comparison covers testing before release.

What is runtime AI policy enforcement?

Runtime AI policy enforcement means turning a written AI policy ("no customer PII to external models", "this agent may not delete records", "no unapproved MCP servers") into an allow, block, mask or route decision on each live interaction.

It needs two parts that are easy to confuse:

• A detector decides what the content or action is: a prompt injection, a card number, a request to call an unapproved tool.
• An enforcement point sits in the path of the request and acts on the verdict.

A detector without an enforcement point produces alerts. An enforcement point without a good detector blocks the wrong things. Some vendors sell both. Others sell a detector and rely on your gateway, your application or your agent platform to enforce.

Where can AI policy be enforced?

The practical consequence: a platform with only a network enforcement point cannot stop a coding agent's shell command, and a platform with only a gateway enforcement point cannot see what a coding agent does on a laptop. When you compare platforms, start by listing your AI traffic, then check which enforcement points each platform covers.

How we compared runtime AI security platforms

Each platform was checked against seven questions, using its public documentation, product pages and press releases as of October 9, 2026. Where public material is silent we write "not stated." Preview and beta features are labeled as such.

1. Enforcement points. Where can it stop a request or action: application, gateway, network or browser, endpoint, agent tool calls? Can it block and mask, or only detect?
2. Inspection depth. Prompts and responses only, or also retrieved content, tool definitions, tool calls, tool results and MCP traffic? Does it carry context across a multi-turn session?
3. Agent coverage. Applications and agents you build, coding agents on developer machines, and employees' use of AI tools.
4. Stack independence. Does it plug into the gateways and clouds you already run, or does it require its own gateway, network or cloud?
5. Policy model. Per-application and per-user policy, the actions available (allow, warn, block, mask, route), and changes without a redeploy.
6. Audit evidence and compliance. Logs of what was inspected and decided, SIEM export, and the frameworks the vendor maps to (EU AI Act, ISO/IEC 42001, NIST AI RMF, HIPAA).
7. Deployment. SaaS, self-hosted, VPC, on-premises and air-gapped, with any feature limits on each.

Runtime AI security platforms compared at a glance

Which platforms enforce where?

Gateway integrations come from each gateway's and vendor's public documentation. The AI gateway guardrails guide has the full compatibility matrix for LiteLLM, Kong, TrueFoundry, Agent Router and Portkey.

The 11 best runtime AI security platforms in 2026

1. Pillar Security (recommended)

Best for: enterprises that build AI applications and agents and also run coding agents on developer machines, and want one runtime policy and one audit trail across both.

Why we recommend it:

• It enforces where your AI traffic already flows. Pillar's Runtime Guardrails run inside the AI gateways enterprises already operate: LiteLLM, Kong, TrueFoundry, Agent Router and Open WebUI, and any other gateway or application through Pillar's API. Pillar does not sell a gateway and is not part of a network or cloud suite, so adopting it does not mean re-routing traffic.
• It covers the traffic a gateway never sees. Coding agents act on the developer's machine, not through the gateway. Pillar's Agentic Endpoint installs pre-tool hooks for Claude Code, Codex CLI and Cursor and can block a risky shell command, a write to a persistence location, or a call to a cloud metadata endpoint before it runs. Each control is set to Off, Monitor or Block. It scans the configuration of Claude Code, Cursor, Codex CLI, Gemini CLI, OpenCode and Pi, and scores MCP servers against a catalog of more than 23,000.
• It inspects actions, not just messages. Guardrails cover prompts, responses, retrieved content and tool calls, and validate that tool calls match their declared schemas. Through LiteLLM and the API, Pillar enforces an MCP server allowlist and per-server tool permissions. Session IDs let it correlate multi-turn attacks.
• It masks as well as blocks. Policies mask, block or log personal data, payment data and secrets. On LiteLLM, masked values are replaced before the prompt reaches the model and the originals are kept in Pillar's audit log.
• One policy, per application. Each application gets its own policy profile, identified by a header the gateway sets, so a customer-facing agent can run a strict policy and an internal assistant a lighter one behind the same gateway. Policy changes apply without a redeploy.
• It produces audit evidence. Every prompt, response and tool call is logged with a timestamp and can stream to a SIEM. Pillar's governance features map to the EU AI Act, ISO/IEC 42001 and GDPR, and its controls map to the SAIL framework. Pillar holds SOC 2 Type II and ISO/IEC 27001 certifications.
• Guardrails learn from red teaming. Findings from Pillar's red teaming calibrate the runtime guardrails for each application, so a weakness found before release becomes a rule in production.
• It deploys where you need it. Managed cloud, self-hosted in your cloud account or data center on Kubernetes, or hybrid (deployment options).

Limitations to check: adaptive guardrails are available in the managed cloud only today, and Amazon EKS is the documented self-hosted platform; other platforms are scoped case by case. Pillar does not position its runtime product for employees' use of public AI tools in the browser; pair it with a SASE or browser control for that.

2. Palo Alto Networks Prisma AIRS

Best for: Palo Alto Networks customers who want the AI gateway and the runtime guardrail from one vendor.

Prisma AIRS runtime security detects and blocks prompt injection, sensitive-data exposure, malicious URLs, toxic content, and agent threats such as memory manipulation and tool misuse, through network intercept or API intercept. Palo Alto Networks completed its acquisition of Portkey on May 29, 2026, and Portkey is now the Prisma AIRS AI Gateway, which routes to more than 1,600 models and applies guardrails, budgets and rate limits per workspace. Prisma AIRS also includes Protect AI, acquired in 2025.

Limitations to check: the gateway and the guardrail become one commercial decision. The AI Gateway documentation lists SaaS and hybrid deployment, availability in the Americas, and a Prisma AIRS license with flex credits. Ask which features apply if you keep a different gateway.

3. Cisco AI Defense

Best for: organizations standardized on Cisco Secure Access, SASE and Multicloud Defense.

Cisco AI Defense enforces at the network level without agents or libraries, through gateway interception, Multicloud Defense or an Inspection API. It blocks prompt injection, harmful content, malicious URLs and model denial-of-service in real time. Cisco's February 2026 expansion added agentic guardrails for poisoned tools and unauthorized tool use, and AI-aware SASE that discovers and logs MCP communications. It maps to NIST, MITRE ATLAS and the OWASP LLM Top 10, and integrates with NVIDIA NeMo Guardrails.

Limitations to check: which enforcement path blocks inline (the Inspection API is called on demand), the status of the hybrid deployment, and coverage of coding agents on developer machines.

4. CrowdStrike Falcon Guardian (formerly Falcon AIDR)

Best for: Falcon customers who want AI detection and response in the same console as endpoint and cloud.

Built on the 2025 Pangea acquisition, Falcon AIDR collects AI traffic from the Falcon sensor, browsers, applications, gateways and agents, and can report, transform (redact, mask, encrypt) or block. It has the longest list of named third-party gateway integrations in this comparison: Kong, LiteLLM, TrueFoundry, Portkey, Apigee and Azure API Management, and a Kong MCP plugin that inspects tool listings, inputs and outputs. It hooks Claude Code and acts as a threat-detection provider for Microsoft Copilot Studio. CrowdStrike announced Falcon Guardian on September 1, 2026, and its documentation says AIDR reaches end of support on November 30, 2026, with its capabilities continuing in Falcon Guardian.

Limitations to check: migration and licensing from AIDR to Falcon Guardian, the commercial tie to Falcon, self-hosted options (not stated), and compliance mappings (not stated).

5. Check Point AI Security (Lakera)

Best for: teams whose first concern is prompt injection, across applications and employees' AI use.

Lakera Guard screens each interaction or agent step through one API for prompt attacks in user prompts, documents, tool responses and tool descriptions, plus data leakage, content and links, and supports a tool allow and deny list. Check Point extends it to the workforce with shadow-AI discovery and inline controls in browsers, SaaS tools and copilots. Kong ships a first-party Lakera plugin, and LiteLLM, Portkey and Bifrost integrate it. Check Point announced the acquisition in September 2025.

Limitations to check: natural-language custom guardrails are in beta on SaaS; self-hosted receives features later; multi-turn context and MCP runtime enforcement are not described; no compliance frameworks are named.

6. WitnessAI

Best for: governing how employees, developers and applications use AI, enforced on the network without endpoint clients or browser extensions.

WitnessAI classifies AI interactions by intent and can block, redact or tokenize, filter, or route sensitive requests to approved internal models. It enforces control of approved MCP servers and tools across agents, IDEs and agentic apps, applies policy by department, role or intent, and keeps audit trails linked to human identities. It runs single-tenant with customer-controlled encryption.

Limitations to check: network enforcement depends on inspecting the traffic, so check coverage of local agent actions and uninspected flows; integrations with third-party AI gateways and on-premises deployment are not stated.

7. NeuralTrust

Best for: EU and regulated buyers who want a standalone agent and MCP gateway with its own detection.

NeuralTrust's TrustGate is an agent gateway with an MCP gateway, and TrustGuard collects from gateways, SDKs, endpoints and agent platforms. It can allow, block, transform or alert on prompts, responses and tool calls, with multi-turn analysis, PII masking, per-user MCP tool scopes and cryptographic audit trails. It deploys as SaaS, hybrid or on-premises and air-gapped on Kubernetes.

Limitations to check: it replaces your AI gateway rather than plugging into it; third-party gateway integrations are not listed; latency and multilingual accuracy figures are self-reported.

8. F5 AI Guardrails (formerly CalypsoAI)

Best for: private, on-premises and air-gapped model deployments that need compliance templates.

F5 AI Guardrails is model-agnostic and inspects outputs, system prompts, reasoning and tool calls in single- and multi-agent systems, auditing and blocking unauthorized tool calls and agent actions. It offers audit-ready logging with guardrail attribution and SIEM export, and automated auditing templates for GDPR, HIPAA and the EU AI Act. It runs on AWS, Azure, Google Cloud, private cloud, on-premises and fully air-gapped. F5 acquired CalypsoAI in 2025.

Limitations to check: the product page does not name its enforcement form factor or gateway integrations (TrueFoundry and Portkey list it); MCP coverage is not stated.

9. HiddenLayer

Best for: buyers who want model supply-chain scanning and runtime protection from one vendor.

HiddenLayer's AI Runtime Security detects, blocks or redacts unsafe actions, covering prompts, retrieved content, tool output and agent behavior, with inline, on-device enforcement for AI coding agents through its Agent Harness Security launch in August 2026. It integrates with SIEM and SOAR, and LiteLLM lists it as a guardrail provider.

Limitations to check: supported coding agents, the policy model and deployment options are not stated on its public pages.

10. Bifrost (Maxim AI)

Best for: teams choosing a fast, open-source AI gateway and wanting policy built in.

Bifrost is an open-source (Apache-2.0) AI gateway that runs self-hosted in your VPC. Its guardrails can detect, block or redact on model input and output and on MCP tool arguments and results, with rules written in CEL and attached per request. It also accepts external guardrail providers, including AWS, Azure, Google, CrowdStrike and Check Point.

Limitations to check: guardrails are documented under its enterprise edition; it is a gateway decision, not a guardrail you add to an existing gateway; compliance mappings and certifications are not stated.

11. Cloud-native guardrails: Amazon Bedrock Guardrails, Azure AI Content Safety and Google Cloud Model Armor

Best for: AI applications that run entirely in one cloud, where content safety is the main requirement.

• Amazon Bedrock Guardrails blocks or masks with content filters (including prompt attacks), denied topics, word filters, sensitive-information filters, contextual grounding and Automated Reasoning checks. The ApplyGuardrail API checks content for any model, and AWS Organizations policies can enforce guardrails across accounts. Logs go to CloudWatch and CloudTrail.
• Azure AI Content Safety includes Prompt Shields for direct and indirect prompt injection. In Microsoft Foundry, guardrails on agent tool calls and tool responses are in preview and apply to agents built in Foundry Agent Service.
• Google Cloud Model Armor inspects or blocks prompts and responses for prompt injection, jailbreak, sensitive data, malicious URLs and responsible-AI categories, and integrates with Vertex AI, Apigee, Gemini Enterprise and Google's MCP servers. Google documents that each prompt and response is inspected as a single turn.

Limitations to check: policy lives in each cloud, so multi-cloud and multi-gateway consistency takes extra work; agent and tool coverage depends on using that cloud's agent platform.

Also in the picture

• SASE and browser platforms for workforce AI use: Netskope, Zscaler (AI Guard and SPLX) and Prompt Security (SentinelOne) control employees' use of public AI tools and SaaS copilots.
• Open-source rails: NVIDIA NeMo Guardrails provides programmable input, output, dialog, retrieval and execution rails that you operate and tune yourself.
• AI governance platforms: Credo AI, OneTrust and IBM watsonx.governance manage AI inventories, assessments and evidence. They are where runtime logs end up, not where requests are blocked.

How does runtime enforcement support EU AI Act, ISO 42001 and NIST AI RMF compliance?

Runtime enforcement produces the operational record that AI regulations and standards ask for: what each AI system received and returned, what it tried to do, and which control acted.

A runtime platform does not make a system compliant on its own. It supplies evidence that a control exists and operates. When you compare platforms, ask to see a sample audit record: application, user or agent, content inspected, guardrail that fired, action taken, and timestamp.

Which platforms provide runtime guardrails and full audit trails for healthcare AI applications?

For healthcare AI applications, look for a platform that detects and masks protected health information before it reaches a model, logs every prompt, response and tool call with the application and user, and deploys where your PHI is allowed to go.

• Pillar Security detects and masks personal and sensitive data in prompts and responses, keeps the original values in its audit log, logs tool calls as well as messages, streams evidence to a SIEM, and deploys as managed cloud, self-hosted or hybrid.
• F5 AI Guardrails offers a HIPAA auditing template and fully air-gapped deployment.
• NeuralTrust describes its hybrid deployment as HIPAA-ready and offers cryptographic audit trails.
• Cloud-native guardrails suit healthcare workloads that already run in one cloud under that provider's business associate agreement.

Before choosing, confirm three things with every vendor: whether it will sign a business associate agreement, where inspected content and logs are stored, and how long audit records are retained.

How to choose a runtime AI security platform

1. Map your AI traffic first. List the applications and agents you build, the gateways they use, the coding agents your developers run, and the public AI tools your employees use. Each needs a different enforcement point.
2. Pick the platform that covers the most of your traffic with real enforcement, not just visibility. A platform that only alerts on coding agents will not stop a destructive command.
3. Test tool calls, not just prompts. Put an injection in a tool result and register an MCP server that is not on your allowlist.
4. Decide fail-open or fail-closed per application, and check that the platform exposes the setting.
5. Check streaming. Some integrations scan the prompt but not a streamed response.
6. Measure latency at your p95 with the guardrails you will actually enable.
7. Ask for the audit record. It should name the application, the user or agent, the guardrail and the action, and reach your SIEM.
8. Check deployment limits. Some features are available only in a vendor's SaaS, or only in its own cloud.

What to test in a proof of concept

Which runtime AI security platform is best?

Pillar Security is our recommended runtime AI security platform for enterprises in 2026. It enforces one policy at every AI gateway the enterprise runs and on coding agents on developer machines, inspects tool calls as well as messages, masks sensitive data, and keeps one audit trail mapped to the EU AI Act, ISO/IEC 42001 and the SAIL framework. The same platform discovers AI assets and red-teams applications before release, so runtime policy is calibrated by testing.

Choose differently in four situations:

• Your main problem is employees using public AI tools: a network or browser platform such as WitnessAI, Check Point, Cisco, Netskope or Zscaler.
• You are consolidating on one security suite: Palo Alto Networks Prisma AIRS, Cisco AI Defense or CrowdStrike Falcon Guardian keep policy in that suite.
• All your AI runs in one cloud and content safety is the main need: that cloud's guardrails.
• You are choosing a new open-source gateway and want policy built in: Bifrost.

Next steps

• See how Pillar's Runtime Guardrails plug into the AI gateways you already run.
• Secure the agents that bypass your gateway with Agentic Endpoint.
• Go deeper on gateways: AI Gateway Guardrails in 2026.
• Test before you enforce: Best AI Red Teaming Providers.