Top 8 AI Gateway Solutions with Real-Time Threat Detection In 2026

What Are AI Gateway Solutions?

An AI Gateway with real-time threat detection acts as a smart traffic cop for all interactions between your apps and AI models. It intercepts prompts and model responses to instantly block malicious attacks, enforce security rules, and track data usage.

AI gateway solutions manage, secure, and monitor all interactions with AI models, providing a unified entry point for requests and responses. By abstracting the underlying complexity of multiple AI models, APIs, and tools, AI gateways help organizations maintain consistent access policies, control usage, and enforce security across all AI-powered systems.

In this article:

• What Is AI Gateway Threat Detection?
• AI Gateway Threat Detection Solutions at a Glance
• AI Threats an AI Gateway Should Detect
• Key AI Gateway Real-time Threat Detection Capabilities
• Notable AI Gateway Threat Detection Solutions

What Is AI Gateway Threat Detection?

AI gateway threat detection refers to the processes and tools within an AI gateway that identify and mitigate security risks associated with AI model interactions. These threats can range from prompt injection attacks to unauthorized data access and model abuse. Threat detection systems continuously monitor incoming and outgoing traffic, analyzing prompts, responses, and API calls for signs of malicious intent or policy violations.

Effective AI gateway threat detection leverages a combination of:

• Rule-based heuristics
• Machine learning
• Anomaly detection

This enables real-time identification of suspicious activities and rapid response to emerging attack vectors. By acting as a security checkpoint, the AI gateway helps prevent attacks from reaching underlying models and sensitive backend systems, reducing the risk of data leaks, unauthorized actions, and operational disruptions.

Related content: Read our guide on how LLM gateways work 

AI Gateway Threat Detection Solutions at a Glance

The table below summarizes the key differences between the solutions covered in this guide. We explore each one in more detail in the sections that follow.

AI Threats an AI Gateway Should Detect

1. Prompt Injection

Prompt injection is a technique where attackers manipulate input prompts to influence or subvert the behavior of AI models. By carefully crafting prompts, attackers can bypass restrictions, extract confidential data, or cause the model to perform unintended actions. This threat is particularly dangerous in systems where prompts are dynamically constructed from user input, as it opens avenues for indirect exploitation.

How AI gateways help:

To counter prompt injection, AI gateways need mechanisms to validate and sanitize inputs before they reach the model. This includes pattern recognition to detect suspicious language or structures, as well as context-aware filtering to block potentially harmful content. Continuous monitoring and updating of detection rules are essential, as attackers frequently evolve their techniques to bypass static defenses.

2. Sensitive Data Exposure

Sensitive data exposure occurs when AI models inadvertently reveal confidential or personally identifiable information (PII) in their responses. This can happen due to poorly designed prompts, model training on sensitive datasets, or unintentional leaks during interactions. The risk is amplified when models have access to internal databases or organizational knowledge bases.

How AI gateways help:

AI gateways mitigate this threat by implementing data loss prevention (DLP) filters and response inspection policies. These tools scan both prompts and outputs for sensitive patterns, such as credit card numbers, personal identifiers, or proprietary information. By blocking or redacting unsafe responses in real-time, the gateway acts as a safeguard against accidental or malicious data leaks.

3. Malicious or Unauthorized Tool Calls

AI systems are increasingly integrated with external tools and APIs to perform actions beyond basic text generation. Attackers may exploit these integrations by crafting prompts that trigger unauthorized or malicious tool calls, potentially leading to data modification, system compromise, or unintended transactions. The complexity of these interactions makes them challenging to secure using traditional methods.

How AI gateways help:

To address this risk, AI gateways enforce strict access controls and validation for tool-initiated actions. They monitor all outbound calls, verifying that each request aligns with user permissions and organizational policies. By maintaining an auditable log of tool interactions, gateways also support incident response and forensic analysis in the event of suspicious activity.

4. Model and API Abuse

Model and API abuse involves exploiting the underlying AI infrastructure to perform resource-intensive operations, bypass usage quotas, or harvest model outputs at scale. Common abuse scenarios include automated scraping, denial-of-service attacks, and attempts to reverse-engineer proprietary models through repeated querying. Such abuse can degrade service quality, increase costs, and expose intellectual property.

How AI gateways help:

AI gateways defend against abuse by implementing rate limiting, authentication, and anomaly detection. These controls restrict the frequency and volume of requests, flag suspicious patterns, and block abusive clients in real time. Continuous monitoring ensures that legitimate usage is not disrupted while preventing attackers from exploiting system resources or extracting sensitive model behavior.

Related content: Read our article about API security

5. Data Poisoning and Retrieval Attacks

Data poisoning occurs when attackers inject malicious data into the training or operational datasets of AI models, aiming to corrupt their behavior or induce specific failures. Retrieval attacks, on the other hand, involve crafting queries that force the model to reveal memorized training data, including confidential or proprietary information. Both threats undermine model integrity and trust.

How AI gateways help:

AI gateways aid in detecting and blocking anomalous data submissions or retrieval attempts. They employ heuristics, content filtering, and statistical analysis to flag suspicious inputs or outputs. By isolating and alerting on potential poisoning or leakage events, gateways help maintain model reliability and prevent exploitation through data manipulation.

6. Agent Identity and Authorization Abuse

In multi-agent systems, attackers may attempt to spoof agent identities or escalate privileges to access restricted resources. Abuse of agent identity and authorization can lead to unauthorized actions, data breaches, or manipulation of business workflows. This risk is exacerbated in environments where agents interact with numerous services and data sources.

How AI gateways help:

AI gateways enforce identity verification and granular authorization checks for every agent interaction. They track agent sessions, validate credentials, and ensure that actions are performed only by authorized entities. Comprehensive logging and auditing capabilities further support the detection and investigation of identity-based attacks, strengthening the overall security posture.

Key AI Gateway Real-time Threat Detection Capabilities

AI gateways need to evaluate agent, model, tool, and API activity as it occurs rather than relying only on post-event logs. Effective real-time detection combines behavioral analysis, identity context, sensitive data inspection, and inline policy enforcement to identify both malicious attacks and legitimate agents that begin acting outside their intended roles:

• Continuous agent behavior monitoring: Tracks agent actions across complete sessions, including prompts, tool calls, API requests, accessed applications, and returned data. This helps detect sudden changes in behavior that may indicate manipulation, compromise, or an agent becoming stuck in an uncontrolled loop.
• Persona and policy deviation detection: Compares each action against the agent’s assigned role, approved tools, permissions, and expected workflows. The gateway can flag attempts to access unauthorized systems, perform actions outside the agent’s job description, or exceed established operational boundaries.
• Abnormal tool-call detection: Identifies unusual tool usage patterns, such as excessive retries, rapid call sequences, fabricated parameters, repeated access failures, unexpected write attempts, or millions of automated requests generated by a malfunctioning agent. Behavioral detection is important because valid credentials alone do not prove that an action is legitimate.
• Prompt and tool manipulation detection: Inspects requests and agent behavior for signs of prompt injection, indirect prompt injection, malicious tool instructions, parameter tampering, and attempts to override system policies. Detection should cover both direct user prompts and untrusted content retrieved from external applications or data sources.
• API attack and business logic abuse detection: Analyzes how agents interact with APIs to identify credential stuffing, data scraping, enumeration, account takeover attempts, transaction abuse, and misuse of legitimate application workflows. This allows organizations to detect threats that may not match traditional vulnerability signatures.
• Automated bot and AI-driven traffic analysis: Uses behavioral signals to distinguish legitimate users and approved agents from malicious bots, unauthorized AI crawlers, and AI-enhanced automation. Detection should remain effective even when traffic uses generic user agents, rotating infrastructure, valid accounts, or other techniques designed to evade static controls.
• Sensitive data exposure detection: Scans agent requests, tool parameters, API responses, and model outputs for personally identifiable information, credentials, financial records, proprietary data, and other sensitive content. The gateway can alert, redact, or block transactions before protected data leaves an authorized environment.
• Identity-aware risk analysis: Correlates activity with the authenticated user, agent identity, service account, OAuth token, assigned persona, and requested resource. This context helps distinguish normal operations from credential misuse, excessive privilege, token abuse, or unauthorized agent access.
• Rate and volume anomaly detection: Detects unexpected increases in requests, token use, tool invocations, failed actions, or data retrieval. Per-agent, per-persona, and per-tool baselines can expose denial-of-wallet attacks, runaway workflows, resource exhaustion, and attempts to overwhelm downstream applications.

Notable AI Gateway Threat Detection Solutions

How we selected these solutions: We shortlisted AI gateway and AI security platforms based on their ability to detect and respond to AI threats in real time, including prompt injection, sensitive data exposure, tool and API abuse, model attacks, and rogue agent behavior.

Purpose-Built AI Security and Runtime Threat Detection Platforms

1. Cequence AI Gateway

Best for: Governing agentic AI access to enterprise apps and APIs

Strengths: Agent personas, inline DLP, behavioral containment, audit trails

Things to consider: Dashboard and policy workflows still maturing; AI Gateway is a newer module expanding quickly

Cequence AI Gateway sits between AI agents and enterprise applications and governs what each agent is allowed to do. It authenticates an agent and then verifies every subsequent action, enforcing policy inline in the request path for the full session and on every tool call. Actions are judged on both identity and behavior, so an agent that holds valid credentials is still stopped when it acts outside its assigned role.

The gateway converts existing internal, external, and SaaS APIs into Model Context Protocol (MCP) compatible tools and exposes them through trusted registries of vetted MCP servers, APIs, and skills. Detection begins at the first action rather than after a warm-up period, which is aimed at containing agents that begin behaving abnormally.

Key features include:

• Agent Personas: A plain-English job description generates a tailored persona that limits an agent to only the tools, APIs, skills, and permissions it needs, applying least-privilege boundaries automatically.
• Agentic zero trust enforcement: The gateway authenticates agents against OAuth 2.1 identity providers, manages token lifecycles, and binds sessions to their originating IP address to prevent token theft and reuse.
• Sensitive data protection: DLP scanning inspects agent requests and MCP server responses across more than 100 detection types, and can monitor, redact, or block sensitive data across a sequence of tool calls, with integration into existing DLP infrastructure.
• Automated tool risk scoring and rate limiting: Built-in guardrails score tool risk and rate-limit activity to constrain agent behavior at runtime.
• AI discovery: The platform surfaces sanctioned and shadow AI, identifying agents, MCP servers, and LLM providers across the enterprise from existing SIEM logs.
• Monitoring and audit trails: Real-time visibility into AI-to-tool traffic records user, agent, and tool behavior and which applications and API calls each agent uses, with findings exportable to SIEM and SOC workflows.

Limitations (as reported by users on Gartner Peer Insights):

• Console responsiveness: A subset of users have asked for a faster dashboard and more intuitive policy management, feedback the team is actively building into the roadmap.
• Modular packaging: Capabilities are organized into modules, so procurement teams get the most value by mapping out desired functionality up front.
• Newer AI Gateway offering: The AI Gateway is one of the platform’s newest modules, with new capabilities shipping frequently.

Source: Cequence

2. Palo Alto Networks Prisma AIRS

Best for: End-to-end AI security across the full AI lifecycle

Strengths: Runtime protection, model scanning, red teaming, AI-SPM

Things to consider: Expensive with complex licensing; full value requires deep investment in the Palo Alto ecosystem

Prisma AIRS is a centralized platform that secures AI agents, applications, models, and data from development through deployment. It groups its work into three functions: discovering shadow AI across the environment, assessing new risks through continuous testing, and protecting AI interactions against runtime threats. The platform includes an AI Gateway control plane that discovers, governs, and secures enterprise AI activity from a single point.

At runtime, Prisma AIRS monitors AI behavior and enforces controls to prevent manipulation, data exposure, and unsafe actions during live interactions. It also verifies agent identity and applies real-time security to stop unauthorized agent actions as deployments scale.

Key features include:

• AI Runtime Security: Monitors live AI interactions and applies safeguards against prompt injection, sensitive data leakage, unsafe output, and model denial-of-service attacks.
• Agent Security: Verifies every agent identity and enforces real-time controls to block unauthorized actions across the agent ecosystem.
• AI Model Security: Scans third-party models for tampering, malicious scripts, and deserialization attacks before adoption.
• AI Red Teaming: Simulates real-world attacks, including multi-turn and multi-agent attacks, to identify weaknesses in AI applications and agents before runtime.
• AI Posture Management: Provides visibility and control over AI training and inference data, the integrity of agents and apps, and access to deployed models.
• Shadow AI discovery: Builds a full inventory of AI agents, apps, and models and maps how they connect across the environment.

Limitations (as reported by users on Gartner Peer Insights):

• Cost and licensing: Reviewers consistently describe it as expensive, with licensing complexity that puts it out of reach for many smaller organizations.
• Ecosystem dependence: Organizations outside the broader Palo Alto Networks ecosystem see meaningfully diminished value, tying the platform’s strongest capabilities to a larger vendor commitment.
• Feature maturity: Rapid product iteration leaves some capabilities still maturing, and buyers report having to independently verify that components assembled from multiple acquisitions actually work together as one platform.
• Throughput and residency: Public analysis notes the runtime network intercept is constrained by a per-vCPU transaction ceiling and routes traffic to the US region regardless of where an organization operates, a real limitation for high-volume or data-residency-sensitive deployments.

Source: Palo Alto Networks

3. Cisco AI Defense

Best for: Securing AI at build time and runtime at the network layer

Strengths: Network guardrails, red teaming, supply chain scanning

Things to consider: Difficult to configure; strongest value limited to organizations with an existing Cisco network footprint

Cisco AI Defense secures AI whether an organization is using third-party AI applications or building its own. It works across three functions: discovering AI assets and users across distributed cloud environments, detecting vulnerabilities through algorithmic testing, and protecting production applications with runtime guardrails. Enforcement happens at the network level without agents or libraries, which separates AI development from security operations.

The platform extends to agentic systems and MCP, scanning MCP servers for malicious assets and inspecting agent actions and tool calls in real time. It detects agent-specific threats such as memory poisoning, tool misuse, privilege escalation, and intent hijacking, and aligns detections to standards including NIST, MITRE ATLAS, and the OWASP LLM Top 10.

Key features include:

• AI Runtime Protection: Guardrails embedded in the network block adversarial attacks and harmful responses in real time, including prompt injection, denial of service, and data leakage.
• AI Model and Application Validation: Algorithmic red teaming identifies safety and security vulnerabilities across models in seconds rather than through weeks of manual testing.
• AI Cloud Visibility: Automatically inventories AI models, applications, data sources, MCP servers, and agent processes across distributed environments.
• AI Supply Chain Risk Management: Scans model files, repositories, and MCP servers for malicious code, poisoned data, and unsafe tools before they enter production.
• AI Access: Monitors and manages access to third-party AI applications and enforces policies that limit sensitive data exposure.
• Threat intelligence integration: Detections draw on Cisco’s AI research lab and Talos threat intelligence, with instant platform updates for emerging attacks.

Limitations (as reported by users on Gartner Peer Insights):

• Documentation clarity: Reviewers frequently describe the documentation as hard to follow, slowing teams down as they learn to use features correctly.
• Feature complexity: Certain capabilities are powerful on paper but difficult to configure and operate correctly in practice.
• Licensing structure: Reviewers of Cisco products describe the licensing as complex, with additional licenses required to unlock functionality that buyers often expect to be included.

Source: Cisco

4. WitnessAI

Best for: Governing employee and agent AI use at the network level

Strengths: Intent-based detection, agent tool governance, discovery

Things to consider: Unproven at scale; effectively limited to enterprise-scale deployments

WitnessAI operates at the network layer between users and AI models, intercepting and analyzing AI interactions without endpoint agents or browser extensions. It organizes its work into Observe, Protect, Control, and Attack functions that cover discovery, runtime defense, governance, and red teaming across both human employees and AI agents. Because it sits on the network path, it covers native applications such as Windows Copilot and Office 365 that endpoint-only tools can miss.

Its detection engine classifies the intent behind each prompt rather than matching keywords, which is aimed at catching multi-turn attacks and advanced prompt injection. The platform extends monitoring to agent interactions through MCP, tracking which servers agents connect to and which tools they invoke.

Key features include:

• Runtime AI defense: Bidirectional protection blocks threats such as prompt injection before they reach models and agents, and filters harmful outputs before they reach users or trigger actions.
• Intent-based classification: Machine learning models analyze conversations and context to detect suspicious behavior that evolves across sessions.
• Agent tool-access governance: An organization-wide approved-tool list of MCP servers and tools is enforced at the network for every agent, and each blocked tool call generates an audit record with user, agent, tool, and rule.
• AI discovery and cataloging: The platform scans the network against a catalog of thousands of AI applications to reveal which tools employees use, which agents are running, and associated AI spend.
• Guardrails and data protection: Integrated controls tokenize sensitive information, defend models against manipulation, and enforce rules of engagement for autonomous agents.
• Intelligent routing: AI requests are routed by risk, cost, and purpose, sending sensitive queries to secure internal models.

Limitations (based on publicly available sources):

• Limited review base: The platform is relatively unproven, with a thin base of verified reviews and users reporting rough edges a more established product would likely have resolved.
• Integration effort: Public sources cite integration timelines that regularly run longer than expected, adding real deployment risk.
• Catalog dependence: Network-level discovery is limited to a tracked catalog of supported AI applications, so anything outside that catalog demands extra configuration work, and unpublished pricing makes it harder to evaluate against alternatives.

Source: WitnessAI

5. HiddenLayer AISec Platform

Best for: Securing AI models and agents across the lifecycle

Strengths: Model scanning, runtime detection and response, red teaming

Things to consider: Delivers limited value unless adopted as a full end-to-end platform

HiddenLayer’s AISec Platform secures AI models, pipelines, and agentic systems across their lifecycle. It combines four areas of work: discovery of AI assets, supply chain security through model validation, runtime detection and response, and continuous attack simulation. The platform is model-agnostic and agentless, operating on model artifacts and inference behavior without requiring access to model weights, training data, or prompts.

It builds a living inventory of AI across an environment, including shadow AI, and scans models for malware, backdoors, and vulnerable dependencies before they reach production. In production, it detects and responds to AI attacks such as prompt manipulation, model extraction, unauthorized tool usage, and data leakage.

Key features include:

• AI Runtime Security: Detects and responds to attacks against models and agents in production without accessing sensitive data or proprietary models.
• Model scanning: Analyzes models across many formats for malicious code, backdoors, tampering, and unknown components before deployment.
• AI attack simulation: Continuously runs adversarial simulations to surface weaknesses in AI systems before attackers reach them.
• Agentic and MCP security: Protects autonomous agents and MCP-based systems from prompt injection, unsafe tool use, and harmful autonomous actions.
• Model genealogy and AIBOM: Tracks how a model was trained, fine-tuned, and modified over time and generates an AI Bill of Materials for supply chain audits.
• Posture and integrations: Provides organization-wide governance and posture management with connectors for cloud, CI/CD, SIEM/SOAR, API gateways, and MLOps tools.

Limitations (based on publicly available sources):

• Platform-level value: Independent analysis notes the value proposition depends on adopting the combined platform; purchased piecemeal, individual capabilities are difficult to distinguish from point solutions already on the market.
• Scale dependency: Meaningful return only materializes once models are running in production at real scale, leaving little payoff for experimental or single-application footprints.
• Model-centric focus: Because the platform works on model artifacts and inference behavior rather than acting as an inline network gateway, it cannot stand alone and must be paired with other controls to close the gap.

Source: HiddenLayer

6. Cloudflare AI Security for Apps

Best for: Detecting and blocking LLM threats at the network edge

Strengths: Endpoint discovery, prompt and PII detection, WAF rules

Things to consider: Core detection is locked behind the Enterprise plan and remains an unfinished beta

Cloudflare AI Security for Apps, part of the Firewall for AI product, sits in front of LLM-powered applications as part of Cloudflare’s reverse proxy and analyzes traffic at the network edge. It is model-agnostic and works whether an application uses a third-party model such as OpenAI or Gemini, a self-hosted model, or a custom build, applying consistent protections across all of them. It supports the standard request formats used by several providers and applies a default-secure posture when a pattern is unknown.

The service detects and mitigates threats including prompt injection, sensitive information disclosure, and unbounded consumption. It also discovers LLM-powered endpoints across an organization’s web properties, a capability available on all Cloudflare plans.

Key features include:

• Automatic LLM endpoint discovery: Identifies LLM-powered endpoints across web properties regardless of where they are hosted or which model is used, surfacing shadow AI.
• Prompt injection detection: Scores incoming prompts for injection attempts on a spectrum, writing the result to a field that can drive WAF and rate-limiting rules.
• Customizable threat detection: Custom topics let teams define categories, inspect prompts, and output a relevance score used to log, block, or handle requests.
• WAF rule builder mitigation: Detection fields plug into the familiar WAF rule builder so teams can block or challenge unauthorized actions and leaked sensitive data.
• Broad model support: Out-of-the-box support for OpenAI, Anthropic, Google Gemini, Mistral, Cohere, xAI, and DeepSeek formats.
• Unified posture visibility: Discovered endpoints are reviewable in one place, with a Wiz partnership adding a wider security posture view.

Limitations (based on publicly available sources):

• Plan gating: The prompt injection detection field requires a Cloudflare Enterprise plan with Firewall for AI enabled, putting the core protection behind the highest, priciest tier.
• Beta maturity: Core capabilities remain in beta rather than generally available, with model response handling and additional safety categories still only on the roadmap.
• Coverage constraints: Detection currently handles only JSON content-type requests and only works when the WAF is enabled with traffic proxied through Cloudflare, leaving other formats and architectures unprotected.

Source: Cloudflare

7. Kong AI Gateway

Best for: Governing LLM, MCP, and agent-to-agent traffic centrally

Strengths: Prompt guards, PII sanitization, token quotas, A2A control

Things to consider: Steep learning curve, and the guardrails that matter most sit behind the Enterprise tier

Kong AI Gateway governs generative and agentic AI traffic through a single gateway that covers LLM, MCP, and agent-to-agent (A2A) connectivity. Because all model traffic flows through it, the gateway acts as a single control point that sees prompts, responses, the identity behind each call, and the guardrail verdicts applied to each request. It provides a unified API interface so teams can work with multiple AI providers and switch between them.

For MCP, the gateway can generate MCP servers on top of Kong-managed APIs and govern how agents discover and consume them, enforcing authentication for server access. For multi-agent systems, it observes A2A traffic and captures telemetry on every call.

Key features include:

• LLM policy enforcement: Applies PII sanitization to stop data leakage, along with semantic prompt guards and access control to protect resources and enforce compliance.
• MCP governance: Generates MCP servers and tools, enforces authentication for server access, and optimizes context and token spend.
• Agent-to-agent governance: Observes A2A traffic, captures telemetry including payloads, latency, token usage, and errors, and enforces centralized authentication and authorization with per-call audit records.
• AI quota management: Sets user, model, and time-bound quotas on consumption and token spend, with showback and chargeback across the enterprise.
• AI observability: Provides Layer 7 observability on AI traffic, tracking consumption, tool usage, and token spend with logging and tracing.
• Multi-LLM support: A unified API interface routes across multiple providers and supports failover between them.

Limitations (as reported by users on G2):

• Learning curve: Users consistently report a steep learning curve for advanced features and custom plugins, a real obstacle for teams new to API gateways.
• Enterprise-gated security: Core AI security capabilities, including prompt guard, PII sanitization, and content safety, are withheld from every tier below Enterprise or Konnect.
• Documentation and setup: Reviewers cite real documentation gaps for complex configurations, compounded by setup complexity and enterprise pricing that puts the platform out of reach for smaller teams.

Source: Kong

8. F5 AI Guardrails

Best for: Runtime guardrails for prompt injection and data leakage

Strengths: Large threat library, agent guardrails, flexible deployment

Things to consider: Prompt injection detection works in English only, leaving other languages unprotected

F5 AI Guardrails provides runtime security for AI models, applications, and agents, inspecting prompts and responses to detect and act on threats. It is model-agnostic and enforces consistent policy across public and proprietary models, with deployment across public cloud, private cloud, and on-premises or fully air-gapped environments. Its protection draws on an AI threat library that adds attack patterns monthly, maintained by F5 Labs threat research.

The product defends against prompt injection, data exfiltration, and jailbreak attacks, and enforces controls on model and agent privileges. It logs every enforcement action with the reasoning behind it, and can export metrics and enforcement data to a third-party SIEM.

Key features include:

• Prompt injection defense: Protects against prompt injection, data exfiltration, and jailbreak attacks, backed by a threat library of more than 10,000 attack patterns updated monthly.
• Secure agentic AI: Prevents excessive agency and privilege escalation and audits and blocks unauthorized tool calls and agent actions.
• Semantic data security: Detects and prevents leakage of standard and custom sensitive data categories during AI interactions.
• Custom policy creation: Lets teams build bespoke, policy-driven controls through a natural language interface, with day-one templates for PII, EU AI Act, PCI, and PHI.
• Agent visibility and logging: Records system prompts, instructions, model reasoning, and tool calls for single- and multi-agent systems with audit-ready traceability.
• Flexible deployment: Applies the same guardrails across AWS, Azure, and Google Cloud, private cloud, and air-gapped environments for any similarly formatted AI model or agent.

Limitations (based on publicly available sources):

• Language coverage: F5’s prompt injection processor supports English-language prompts only; injection attempts crafted in any other language pass through undetected unless a customer separately configures upstream language filtering.
• Platform context: AI Guardrails covers only one piece of the AI lifecycle; full coverage requires adopting additional F5 components beyond what’s included here.
• Operational setup: Deployment requires Kubernetes and Helm charts, adding real configuration overhead for teams provisioning the processors.

Source: F5

Conclusion

AI gateway solutions have become a foundational security control for organizations deploying AI applications, agents, and large language models in production. By inspecting prompts, responses, tool calls, and API interactions in real time, they help prevent prompt injection, data leakage, unauthorized actions, model abuse, and other emerging AI threats before they reach critical systems. As AI ecosystems continue to expand across employees, applications, and autonomous agents, organizations should prioritize gateways that combine identity-aware policy enforcement, behavioral analysis, inline protection, and comprehensive monitoring to provide consistent governance and security across the entire AI environment.