API and AI connectivity convergence reshapes identity governance

At a glance

What this is: This is Kong’s interpretation of Gartner’s API management market shift, with the central finding that API management and integration are converging around AI-driven connectivity.

Why it matters: It matters because IAM, NHI, and platform teams will increasingly govern APIs, tokens, and agent access as one connected control surface rather than separate programmes.

By the numbers:

• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.

👉 Read Kong's recap of Gartner's 2025 API management report and AI connectivity view

Context

API management is no longer just about routing traffic or enforcing developer access. As AI systems begin to consume APIs, event streams, MCP-style interfaces, and other machine-readable resources, the governance problem shifts toward how identities, tokens, and runtime permissions are connected across those channels.

Kong’s argument is that integration and API management are converging because AI workloads need continuous access to multiple back-end resources. For IAM and NHI teams, that means the control plane is becoming more identity-heavy, with machine access patterns increasingly shaped by the same lifecycle, privilege, and audit questions that already apply to service accounts and workloads.

Key questions

Q: How should security teams govern AI systems that connect to multiple APIs and data sources?

A: Treat each AI connection as a governed identity path, not just an application integration. Teams should assign ownership, constrain downstream permissions, and log the full execution chain from the first token to the final resource access. If the system can traverse multiple tools, governance has to follow that delegation path.

Q: Why do AI-connected integrations increase machine identity risk?

A: They increase risk because one workflow can accumulate access across many services, often through tokens or secrets that were created for a narrower purpose. That widens blast radius, makes offboarding harder, and creates hidden dependency chains that are difficult to audit after the fact.

Q: What breaks when machine credentials are reused across AI workflows?

A: Reuse breaks accountability and containment. A credential that survives beyond one workflow can be used in places its original owner did not intend, which makes privilege review and incident scoping much harder. The result is not just exposure, but unclear ownership of the access path.

Q: How do teams know if AI connectivity governance is actually working?

A: Look for complete identity-to-resource traceability, consistent ownership of every credential, and timely revocation when a workflow ends. If you cannot answer which identity touched which system and why, governance is still fragmented and the AI path remains under-controlled.

Technical breakdown

AI connectivity as an identity control surface

AI connectivity describes the layer that links AI applications to the APIs, data sources, and event streams they need at runtime. The important identity change is that the caller is not always a person or a fixed service account. It may be an AI workflow, an agent, or a tool chain consuming resources dynamically across multiple environments. That expands the number of identities, secrets, and authorisation paths that must be governed together, rather than separately by platform team. Practical implication: treat connectivity decisions as identity decisions, with entitlement, audit, and ownership mapped to each runtime path.

Practical implication: Map every AI-facing integration to an accountable identity, not just an endpoint or gateway rule.

MCP, APIs, and resource delegation in agentic systems

Model Context Protocol, APIs, and event streams can all serve as delegation layers for AI systems, but they do not remove the need for identity governance. The risk is that access may be granted to the wrong execution boundary, especially when agents can chain requests across tools and back-end services. In that case, least privilege becomes a question of how much the agent can do after the first token is issued, not just what the initial integration allows. Practical implication: define separate controls for the agent, the tool, and the downstream resource.

Practical implication: Separate agent identity from downstream service permissions so one integration cannot silently expand into many.

Why integration convergence changes NHI governance

When integration and API management converge, secrets sprawl and workload identity sprawl tend to converge with them. Each new AI-enabled workflow can introduce tokens, certificates, or machine credentials that are harder to inventory than traditional application access. This is where lifecycle discipline matters: provisioning, rotation, offboarding, and audit need to be tied to the business purpose of the connection, not the existence of the application alone. Practical implication: build governance around runtime purpose and ownership, not just platform placement.

Practical implication: Tie secret rotation and offboarding to the business purpose of each AI integration.

Threat narrative

Attacker objective: The attacker wants to turn a trusted AI connectivity path into a broader machine-access route that reaches sensitive systems, data, or credentials.

1. Entry occurs when an AI application, integration layer, or agent is granted access to APIs or event resources needed for its task.
2. Escalation happens when that access is reused across additional tools, back-end systems, or protocols without tight boundary checks.
3. Impact follows when the connected identity can move beyond its intended scope and reach data, actions, or credentials that were never meant for the original workflow.

Breaches seen in the wild

• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.
• Internet Archive breach — unsecured GitLab authentication tokens exposed 31M Internet Archive accounts.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI connectivity is becoming an identity governance problem, not just an architecture trend. Once APIs, MCP-style interfaces, and event streams become the fuel for AI systems, the question shifts from connectivity design to who owns each machine access path. That moves the problem squarely into IAM, NHI, and lifecycle governance. The practical conclusion is that integration inventories are now identity inventories in disguise.

API management and integration convergence increases the blast radius of every machine credential. When a single workflow can touch multiple runtimes, clouds, and tools, the failure is rarely one token in isolation. It is the absence of a governance model that follows the credential across its usable path. Practitioners should treat every connected AI workflow as a potential multi-system access corridor, not a single application.

Runtime delegation is the real control point for agentic systems. The article’s logic is consistent with a broader shift in the market: the security boundary is moving from static APIs to dynamic execution. That means entitlement review, secret ownership, and auditability have to be evaluated at the moment of use, not at design time. Teams that still govern only the endpoint will miss the actual path of privilege.

AI connectivity will expose weak machine-identity lifecycle discipline faster than traditional integration ever did. AI-driven systems create more short-lived connections, more hidden dependencies, and more opportunities for stale access to survive past its purpose. That makes rotation, offboarding, and accountability central to the architecture, not administrative cleanup. Practitioners should expect identity lifecycle failures to become visible first in AI-connected environments.

Identity blast radius is the right named concept for this shift. The meaningful unit of risk is no longer the individual API or token, but the span of systems reachable through one connected AI workflow. That blast radius grows whenever ownership is unclear or permissions are inherited across layers. The practitioner implication is to govern the whole chain of delegated access, not the first hop alone.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• For the broader governance context, OWASP Agentic AI Top 10 frames the control gaps that emerge when agents can misuse tools or expand scope at runtime.

What this signals

Identity blast radius will become the practical metric that matters most for AI-enabled integration work. The more an AI workflow can traverse APIs, event streams, and downstream services, the more security teams need to measure how far a single credential can reach. That is why the governance conversation is shifting from integration coverage to permission span and revocation speed. For practitioners, the next step is to identify where delegated access still survives after its original purpose has ended.

AI connectivity will force IAM and platform teams to share a control vocabulary. When machine identities, application gateways, and AI orchestration layers all participate in the same access path, isolated governance models fail. Teams should align their operational controls with external guidance such as the NIST AI Risk Management Framework and use the NHI Lifecycle Management Guide to keep lifecycle ownership visible. The programme risk is not just more access, but more unowned access.

AI governance programmes need traceability before scale. The security value of AI connectivity falls sharply when no one can reconstruct which identity touched which resource and why. That makes audit evidence, ownership, and revocation workflow the first controls to harden, not the last. Practitioners should expect the most urgent failures to appear where machine identity lifecycle and API governance still live in separate operating models.

For practitioners

• Map AI connectivity to accountable identities Inventory each AI-facing integration, the identities it uses, and the downstream systems it can reach. Assign an owner to every token, certificate, or workload credential so no machine path exists without a named governance contact.
• Separate agent permissions from downstream resource permissions Give AI workflows the narrowest possible initial access and enforce explicit boundaries before they can reuse that access against other tools or services. Review whether one integration is effectively acting as a proxy for many unrelated systems.
• Tie lifecycle controls to runtime purpose Rotate and revoke machine credentials based on the purpose and duration of the AI connection, not on broad platform refresh cycles. Use the NHI Lifecycle Management Guide to align provisioning, rotation, and offboarding with actual usage.
• Track auditability across the full execution path Require logs that show which identity initiated a request, which tool was called, and which downstream resource was reached. If you cannot reconstruct the path, you do not have defensible control over the AI integration.

Key takeaways

• AI connectivity is pushing API management into the identity governance domain, where machine access paths matter as much as endpoints.
• When AI systems can chain across APIs and services, the blast radius of a single credential expands unless ownership and lifecycle controls follow the full path.
• Practitioners should govern AI integrations as delegated identity routes, with traceability, revocation, and accountability built into the operating model.

Key terms

• AI Connectivity: The set of APIs, event streams, tools, and identity controls that allow AI systems to reach enterprise resources at runtime. In practice, it is a governance layer as much as an integration layer because every connection also creates an access path that must be owned, audited, and revoked when no longer needed.
• Identity Blast Radius: The total span of systems, data, and actions reachable through one identity or delegated workflow. For AI-connected environments, blast radius is defined by how far a token or agent can move across services before control is reasserted, not by the first system it touches.
• Machine Identity Lifecycle: The end-to-end governance of non-human credentials from issuance through rotation, monitoring, and offboarding. In AI-connected environments, lifecycle management must follow the actual runtime purpose of the credential, because stale access often persists after the original workflow has changed.
• Delegated Access Path: A chain of permissions in which one identity can act through tools or downstream services on behalf of a workflow or application. This matters for AI because the first approval does not describe the full power of the session, and later hops may widen the effective privilege set.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Kong: Recapping What the 2025 Gartner Magic Quadrant for API Management Report Says About APIs and AI Success. Read the original.