AI gateways emerged as AI tools began appearing across teams without any central view, and companies needed a single layer to see which models were running, who was using them, and what they cost. That control layer governs how AI is used, but it doesn’t govern what AI can reach, so a model can run through a well-governed gateway and still have no way to access the customer relationship management (CRM), enterprise resource planning (ERP), or warehouse where the answers to business questions live. CData Connect AI Gateway is built for that part of the problem, and with it your agents and AI tools get governed, live access to hundreds of enterprise sources through managed Model Context Protocol (MCP) access, with model routing, policy enforcement, and thorough logging.
What is an enterprise AI gateway?
An enterprise AI gateway is a central layer between applications or agents and the AI models, tools, and data sources they use, where a company can route, secure, govern, and audit that activity. Palo Alto Networks calls it the centralized control plane for security and AI governance across the enterprise, and IBM describes agent gateways as built for autonomous or semi-autonomous agents that decide which tools to call and in what sequence.
The Palo Alto Networks and IBM definitions center on routing, permissions, and policy for models and agents. Neither addresses whether AI can reach governed, live data in ERP, CRM, and warehouse systems. Connect AI Gateway covers both sides: a Model Gateway for routing, budgets, and failover, and MCP Gateway for governed agent access to hundreds of enterprise sources, with identity and policy enforced at the AI-traffic and data layers.
Dimension | Traditional API gateway | LLM router / security gateway | Data-layer AI gateway (MCP) | CData Connect AI Gateway |
Designed for | Deterministic, stateless microservices | Non-deterministic AI workloads such as models, agents, and tools | Agent and AI tool access to data in enterprise systems | Both: model traffic, plus agent and tool access to live enterprise data |
Routing logic | URL paths, headers, and service discovery | Model capability, cost per token, latency, and provider health | The connection, object, and operation an agent is permitted to use | Models by user, team, or department, with budget checks and health-based failover; tools through scoped Tool Servers |
Security model | HTTP-level: authentication, payload schema, and IP rules | Content-level: prompt injection, personally identifiable information (PII) detection, and output safety | Source-level: the user’s identity and the source system’s own permissions | Identity-based, down to the field; agents inherit user permissions; vaulted credentials; sensitive data redacted or blocked |
What it records | Request count, latency, and error rates | Token usage, cost attribution, and guardrail verdicts | Which user or agent fetched or acted on which source | Token spend by user, team, or department, plus query-level logs of every tool call, including denials; SIEM export |
Business context | Not in scope | Not in scope; prompts pass to the model as sent | Source schemas exposed through tools | Context Engine applies schemas, semantic definitions, and company knowledge to every call, across any model |
Why the data layer, not the model, is where enterprise AI projects fall short
Many enterprise AI pilots deliver little measurable return. MIT’s NANDA report The GenAI Divide: State of AI in Business 2025, as covered by Fortune, found about 95% of companies in its dataset saw little to no measurable P&L impact from generative AI pilots, and it points to a learning gap and flawed integration, not model quality.
Anthropic described the same constraint when it introduced MCP in November 2024: models are trapped behind information silos and legacy systems, and each new source needed its own custom implementation. Enterprise AI projects fall short not because models are weak, but because they can’t reach live, governed ERP, CRM, and warehouse data. Gartner adds that more than 40% of agentic AI projects will be canceled by the end of 2027, mainly because of rising costs, unclear business value, or inadequate risk controls. A data-access layer addresses the connectivity part of that risk, not every cause.
5 criteria for evaluating an enterprise AI gateway
When your security team reviews an AI gateway, five criteria cover what IBM describes as the role of an agent gateway, which is to route requests, manage permissions, and log activity.
MCP governance and centralized control
MCP governance is the ability to centrally control, scope, and monitor which MCP tools and data an agent can use. With Connect AI Gateway, hundreds of sources sit behind one managed MCP endpoint, you grant access by need to know at the source level, and Toolkits expose only the objects and operations an agent needs. IBM places this within the wider job of an agent gateway, which governs which tools an agent can call and what data it can access. To test this, ask the vendor to show one control plane across all connected sources and how access to each is scoped.
Passthrough security with OAuth/SAML
Passthrough security means the gateway carries a user’s own OAuth or Security Assertion Markup Language (SAML) identity to the source system, so that system’s permissions decide what the user can see. In Connect AI Gateway, each source uses per-user authentication or a shared credential, and with per-user authentication an agent sees only what the person driving it is allowed to see. Connect AI Gateway fetches data directly from the source, so no copy lands in an intermediate store.
Microsoft 365 Copilot connectors can enforce source permissions when configured to restrict access to users who have access in the source system. Before you approve, confirm the user’s identity reaches the source system unchanged and that no data is copied outside the source of record.
Audit trails and exportability
Security teams need audit logs they can export to their own tools. Connect AI Gateway records the timestamp, event type, user, origin such as MCP, and target resource, and it can forward events to your security information and event management (SIEM) in real time. CData maintains SOC 2 Type II and ISO/IEC 27001:2022. A SOC 2 Type II report assesses design and operating effectiveness over an observation period, commonly three to 12 months. OpenAI’s Compliance Logs Platform exports audit logs as files you can ingest into your SIEM
Audit need | Question to ask the vendor | Acceptable answer |
SIEM export | Can logs stream to Splunk/Sentinel? | Native export or documented API |
Control evidence | Is there a current SOC 2 Type II report? | Report available, often under NDA |
Logging scope | Are data access actions logged, not only model calls? | Yes, with user and source identified |
Vendor neutrality across ChatGPT, Claude, Copilot, and Gemini
Vendor neutrality means governed data is connected once and served to any approved AI model or assistant. Connect AI Gateway exposes the same governed connections to MCP clients such as Claude, ChatGPT, Microsoft Copilot, and Gemini. Palo Alto describes a gateway as a single endpoint across model providers, and MCP gives the data side the same property. Connect one source once, then confirm two different AI front ends can use it under the same access settings.
Cross-system federation and semantic context
Cross-system federation is the ability to fetch and join live data across ERP, CRM, and warehouse systems in one governed request. In a CData Labs benchmark, Connect AI answered 98.5% of real prompts correctly, which is CData’s own test and has not been independently verified. In one multi-source example, a request used 4,427 tokens instead of 183,541, a 97.6% reduction that will vary by environment. For systems inside a private network, the Private Cloud Connector is software installed in an on-premises network that bridges on-premises data and Connect AI. To test this, run one request across two or more systems and confirm access controls still apply.
Comparing your enterprise AI gateway options
Four categories solve different problems here, so the right choice depends on whether your bottleneck is model traffic, orchestration, integration, or governed data access. Connect AI Gateway is a purpose-built data gateway that also includes a Model Gateway for routing, budgets, and failover (in early access as of October 2026).
Option type | Primary job | MCP governance | OAuth/SAML passthrough | SIEM export | Vendor neutrality | Cross-system federation | Model routing & cost control |
Data-layer AI gateway (managed MCP) | Governed, live access to enterprise data | Central control over which tools and data an agent can use | User identity carried through to the source, where sources support it | Audit events forwarded to your SIEM | Works with any MCP client and any major model provider | Live fetch across sources in one request | Included via Model Gateway (early access) |
Large language model (LLM) router / orchestration layer | Model routing, token control, and runtime security | Varies by product | Varies by product | Varies by product | Spans multiple model providers | Not its main job | Core strength |
Integration platform as a service (iPaaS) / integration platform | Scheduled integration and workflow automation | Not its main job | Varies by product | Varies by product | Varies by product | Moves data between systems on a schedule | Not its main job |
Native AI platform connector | Data access inside a single AI platform | Handled within that platform | Handled within that platform | Through the platform's own tooling | Limited to that platform | Within that platform only | Within that platform only |
Choose the enterprise AI gateway that turns AI into ROI with CData
A gateway gives your organization one place to route, secure, and audit AI activity, and the decision that most affects ROI is whether that AI can reach governed, live data. CData Connect AI Gateway gives your agents and AI tools governed access to hundreds of enterprise sources through managed MCP access, and its Model Gateway adds routing, budgets, and failover across model providers. Start for free with the MCP Gateway.
Request early access to the Model Gateway and test it against your own security criteria.
Frequently asked questions
What is an enterprise AI gateway, and how does it work?
A central layer that routes, secures, governs, and audits AI traffic.
How is it different from a standard API gateway or iPaaS?
An AI gateway adds model routing and MCP tool governance.
Why do so many enterprise AI pilots fail, and how does a gateway fix that?
A data-layer gateway gives AI governed access to live data.
How does a gateway handle security, permissions, and compliance?
Through MCP governance, identity passthrough, SIEM-exportable audit events, and SOC 2 Type II evidence.
Can it work across OpenAI, Anthropic, and Microsoft AI?
Yes, because MCP is an open standard, and the Model Gateway is OpenAI- and Anthropic-compatible.
What does a gateway do for the business beyond IT infrastructure?
It gives AI governed access to current data and security teams a path to approval.
