Kong
Enterprise AI governance requires a composed stack connecting high-level risk and compliance oversight with real-time runtime enforcement. While oversight tools manage inventories, approvals, and evidence, AI gateways apply policy controls directly to live model, API, MCP, and agent traffic. Implementing both runtime controls and structured governance frameworks ensures comprehensive security, observability, and cost management across your entire AI estate.
AI adoption is outpacing the speed at which many organizations can establish standardized oversight. In fact, Stanford HAI reported that 78% of surveyed organizations were using AI in 2024, a significant jump from 55% the previous year (AI Index Report 2025AI Index Report 2025) [1]. To keep up, organizations need the right AI governance tools to transform abstract policies into repeatable decisions, concrete evidence, and reliable controls.
However, true AI governance extends well beyond basic legal compliance and contractual obligations. That is where model governance specifically comes into play, covering the documentation, evaluation, approval, and monitoring of individual models. Comprehensive AI governance takes model governance a step further by incorporating portfolio decisions, access policies, human oversight, incident handling, and real-time runtime enforcement.
To build a practical and effective program, teams should connect four foundational pillars:
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Accountability: Establishing clearly named owners, defining decision rights, setting up approvals, and outlining escalation paths.
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Risk and security: Conducting evaluations, ensuring data protection, enforcing access controls, building resilience, and preparing for incident response.
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Operational control: Implementing continuous monitoring and enforceable policies to manage models, APIs, tools, agents, and overall costs.
Runtime AI governance provides a policy architecture that creates, manages, applies, and audits policies in real-time while a request or agent action is in progress. An AI gateway acts as the enforcer, applying controls directly to the agent traffic and the controls it intermediates. For example, Kong AI GatewayKong AI Gateway applies runtime policy controls for AI traffic spanning LLMs, MCPs, and agent-to-agent interactions.
Governing model access and prompts
For the traffic it intermediates, an AI gateway can seamlessly authenticate callers while applying your configured access, quota, routing, and logging policies. Depending on the product and configuration, it may also inspect incoming prompts or sanitize defined sensitive data automatically.
While a standard API gateway authenticates, routes, limits, and observes HTTP traffic, an AI gateway layers on AI-specific controls. These can include token accounting, model selection, semantic routing or caching, and prompt-response policies. AI connectivity extends API management; it doesn't replace it, since agents rely on APIs to pull valuable information.
Event management further extends an agent’s capabilities from event streaming platforms like Kafka to make contextful decisions beyond the single data points an API provides. Event GatewaysEvent Gateways create a governed approach to event streams for agent access, much like AI gateways and API gateways for their respective traffic.
Agentic AI governance must constrain actions well beyond the model. The OWASP Foundation recommends downstream authorization rather than relying on an LLM’s independent judgment (OWASP guidance on excessive agencyOWASP guidance on excessive agency) [9]. Best practices include enforcing least privilege, scoping credentials, requiring approvals, applying rate limits, and maintaining audit logs. Gateways can support mediated traffic perfectly, but downstream systems always retain final authorization responsibility.
Start your search by identifying control gaps rather than shopping by vendor categories. Map out your AI inventory, assign owners, trace access paths, list agent tools, identify regulated use cases, define evidence needs, and establish deployment boundaries.
Evaluate the stack against these questions:
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Coverage: Does it cover required models, APIs, MCP tools, agents, SaaS AI, and data?
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Action: Does it report, enforce policy, or both?
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Identity: Does user identity flow into authorization and attribution?
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Evidence: Are decisions, requests, tool calls, exceptions, and changes auditable?
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Alignment: Can teams map controls to NIST, ISO, and applicable regulation without falsely implying certification?
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Deployment: Does it support required cloud, hybrid, residency, and isolation models?
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Integration: Can risk, security, platform, and engineering systems exchange context and evidence?
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Operations: Can teams manage policy declaratively and monitor reliability, latency, and cost?
An AI governance platform helps organizations define policies, assign accountability, inventory AI systems, assess risk, document decisions, monitor behavior, and produce evidence. Depending on the product, it may also enforce access or runtime policies directly or integrate with systems that do.
What are the four pillars of AI governance?
A practical four-pillar model is accountability, transparency, risk and security, and operational control. These pillars connect ownership and evidence with lifecycle monitoring and enforceable technical policy.
What are examples of AI governance?
Examples include approving high-risk use cases, maintaining a model inventory, testing models, and restricting model or tool access. Other controls include redacting sensitive data, logging agent actions, and enforcing token budgets. The right controls depend on the system, users, data, and regulatory context.
What are the best AI governance platforms?
The best AI governance platforms are the ones that cover your specific oversight and enforcement gaps without creating disconnected policy silos. Most enterprises need a composed stack spanning risk and evidence management, model governance, security, observability, and runtime controls rather than one product category.
What is the difference between an AI gateway and a standard API gateway?
A standard API gateway manages API authentication, routing, rate limits, and observability. For traffic it intermediates, an AI gateway can add AI-specific controls. These may include model routing, token metering, prompt and response policies, semantic caching, and provider credential management. Available capabilities depend on the product and configuration.
Event streams create contextful decision points for business on critical data found in Kafka. An event gateway helps reduce the risk of exposing Kafka, simplifying access and auditing especially when AI agents are consuming event streams. An event gateway also reduces the operational overhead by eliminating infrastructure sprawl, saving costs and time.
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: Native MCP support for connecting agents to enterprise tools and data sources SDKs and frameworks op
Alex Drag
You Can't Govern What You Can't See AI spending will reach $2.59 trillion in 2026. I regularly like to share what we're seeing in production at Kong. Not projections or analyst forecasts, but actual traffic flowing through Kong AI Gateway from
Augusto Marietti
Your infrastructure already has the raw materials: compute (VMs, containers, serverless), event streaming (Kafka, Kinesis, Pub/Sub, RabbitMQ), data stores (warehouses, databases, object storage), and AI endpoints (any hosted or self-hosted LLM). Tho
Hugo Guerrero
The Problem: As organizations adopt agentic AI, a massive gap exists between compliance approvals and network reality. Often, AI governance is relegated to manual reviews and "paper approvals," meaning network firewalls and gateways have no context
Alex Rice
You can see this visualized in the diagram below. As you move to the right, you get smaller and smaller circles — more services, deployed faster, in a more distributed manner to add resiliency and features. As you move to the right, your control and
Kong
Agents are ultimately decision makers. They make those decisions by combining intelligence with context, ultimately meaning they are only ever as useful as the context they can access. An agent that can't check inventory levels, look up customer his
Alex Drag
The Shifting Economic Landscape: The AI token economy in 2026 is evolving, and enterprise leaders must distinguish between low-cost input tokens and high-premium output tokens to maintain profitability. Agentic AI Financial Risks: The transition t
Dan Temkin
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: Native MCP support for connecting agents to enterprise tools and data sources SDKs and frameworks op
Alex Drag
You Can't Govern What You Can't See AI spending will reach $2.59 trillion in 2026. I regularly like to share what we're seeing in production at Kong. Not projections or analyst forecasts, but actual traffic flowing through Kong AI Gateway from
Augusto Marietti
Your infrastructure already has the raw materials: compute (VMs, containers, serverless), event streaming (Kafka, Kinesis, Pub/Sub, RabbitMQ), data stores (warehouses, databases, object storage), and AI endpoints (any hosted or self-hosted LLM). Tho
Hugo Guerrero
The Problem: As organizations adopt agentic AI, a massive gap exists between compliance approvals and network reality. Often, AI governance is relegated to manual reviews and "paper approvals," meaning network firewalls and gateways have no context
Alex Rice
You can see this visualized in the diagram below. As you move to the right, you get smaller and smaller circles — more services, deployed faster, in a more distributed manner to add resiliency and features. As you move to the right, your control and
Kong
Agents are ultimately decision makers. They make those decisions by combining intelligence with context, ultimately meaning they are only ever as useful as the context they can access. An agent that can't check inventory levels, look up customer his
Alex Drag
The Shifting Economic Landscape: The AI token economy in 2026 is evolving, and enterprise leaders must distinguish between low-cost input tokens and high-premium output tokens to maintain profitability. Agentic AI Financial Risks: The transition t
Dan Temkin
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: Native MCP support for connecting agents to enterprise tools and data sources SDKs and frameworks op
Alex Drag
You Can't Govern What You Can't See AI spending will reach $2.59 trillion in 2026. I regularly like to share what we're seeing in production at Kong. Not projections or analyst forecasts, but actual traffic flowing through Kong AI Gateway from
Augusto Marietti
Your infrastructure already has the raw materials: compute (VMs, containers, serverless), event streaming (Kafka, Kinesis, Pub/Sub, RabbitMQ), data stores (warehouses, databases, object storage), and AI endpoints (any hosted or self-hosted LLM). Tho
Hugo Guerrero
The Problem: As organizations adopt agentic AI, a massive gap exists between compliance approvals and network reality. Often, AI governance is relegated to manual reviews and "paper approvals," meaning network firewalls and gateways have no context
Alex Rice
You can see this visualized in the diagram below. As you move to the right, you get smaller and smaller circles — more services, deployed faster, in a more distributed manner to add resiliency and features. As you move to the right, your control and
Kong
Agents are ultimately decision makers. They make those decisions by combining intelligence with context, ultimately meaning they are only ever as useful as the context they can access. An agent that can't check inventory levels, look up customer his
Alex Drag
The Shifting Economic Landscape: The AI token economy in 2026 is evolving, and enterprise leaders must distinguish between low-cost input tokens and high-premium output tokens to maintain profitability. Agentic AI Financial Risks: The transition t
Dan Temkin
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