Topic starter
24/06/2026 8:53 pm
TL;DR: API management and integration are converging under AI pressure, with newer vendors rising and legacy platforms falling behind, according to Kong. The identity lesson is that AI connectivity expands the number of machine and agent touchpoints, so governance now has to follow runtime access paths instead of static integration diagrams.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “# Recapping What the 2025 Gartner Magic Quadrant for API Management Report Says About APIs and AI Success”.
Key questions
Q: How should IAM teams govern AI connectivity across APIs and MCP?
A: IAM teams should govern AI connectivity as a runtime access problem, not a static integration problem.
Q: Why do AI helpdesks and security tools increase identity governance risk?
A: They compress decision time while expanding access scope.
Q: What breaks when access control is based only on integration diagrams?
A: Access control breaks when the documented connection does not match the runtime path.
Practitioner guidance
- Map runtime access paths Inventory how AI applications, coding assistants, agents, and event-driven services actually consume APIs, LLMs, MCP, and streams, then compare those paths with the approved integration diagram.
- Separate governance by interface type Create different control expectations for APIs, tool calls, and event streams so that authentication, authorisation, and observability are not treated as one generic integration control.
- Review delegated access assumptions Check whether downstream resources inherit access context in ways that were acceptable for conventional integrations but become risky when AI runtimes choose tools and data at execution time.
Bottom line: AI connectivity collapses older boundaries between API management and integration, which forces identity governance to follow runtime behaviour instead of design-time architecture.
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25/06/2026 5:44 am
AI connectivity is becoming an identity governance problem, not just an integration pattern. Once AI applications, agents, and assistants consume APIs, LLMs, MCP, and event streams through the same operational fabric, the access question changes. The security issue is no longer whether integration exists, but whether every runtime consumer can be governed, observed, and constrained across the full path. Practitioners should treat this as a shift in control boundary, not a branding change.
A few things that frame the scale:
- 71% of organizations use third-party APIs, according to Gartner’s 2024 data.
A question worth separating out:
Q: What is the difference between API management and IAM in a secure API strategy?
A: API management focuses on exposing, documenting, monitoring, and operating the API itself, while IAM governs who or what can access it and under what conditions. In a strong strategy, the two work together. API management provides the platform discipline, and IAM supplies identity proof, access policy, and trust decisions across applications, partners, and developers.
👉 Read our full editorial: API and AI connectivity convergence reshapes identity governance