Hidden API costs show why API security is now identity security

At a glance

What this is: Probely argues that API neglect creates hidden financial, operational and trust costs as APIs and AI workflows expand the attack surface.

Why it matters: This matters to IAM and security teams because API inventory, authentication and lifecycle governance now sit at the boundary between application security and identity control.

Context

APIs are the connective tissue of modern applications, and they increasingly sit underneath AI-enabled workflows as well as conventional software integrations. The security gap is not just exposed endpoints, but the lack of complete inventory, consistent authentication and lifecycle governance across public, internal, shadow and third-party APIs.

When organisations treat APIs as an AppSec problem only, they miss the identity layer that governs who or what can call them, how access is issued and how long that access remains valid. That gap becomes more visible as LLM-backed features, service-to-service calls and third-party integrations multiply.

The article’s starting point is typical: most teams discover API risk only after the operational, financial or reputational damage is already in motion.

Key questions

Q: Where does API security fail when teams do not know all of their APIs?

A: It fails at the point where access paths become invisible. Undocumented, shadow and zombie APIs cannot be tested, reviewed or revoked with confidence, so they become unmanaged entry points for data access and business actions. The result is a control gap, not just a technical oversight, because governance depends on knowing what exists before deciding who or what may call it.

Q: Why do APIs create a larger identity risk surface in AI-enabled environments?

A: APIs become the execution boundary for software agents, workflow automation, and backend services, so any overbroad token can reach multiple systems. The risk is less about the endpoint itself and more about delegated capability spreading across connected services. Teams should measure blast radius, not just access count.

Q: What are the signs that API governance is failing at runtime?

A: Common signs include undocumented endpoints, duplicated ownership, inconsistent responses, rising retry loops, and frequent discovery of APIs that are not in the catalogue. Those signals show that the live estate and the formal control model no longer match. Once that happens, security and reliability teams are managing assumptions instead of actual behaviour.

Q: Should teams treat API security as part of IAM or AppSec?

A: Treat it as both, but with identity governance at the centre. AppSec covers testing and vulnerability management, while IAM controls who or what can call an API, under what scope and for how long. In modern environments, especially AI-enabled ones, separating those responsibilities leaves an accountability gap that attackers can exploit.

Technical breakdown

Why API inventory is an identity control

API inventory is not just asset discovery. It is the prerequisite for knowing which services, applications, bots and AI-driven workflows can reach data or trigger business actions. Shadow APIs, zombie endpoints and undocumented third-party integrations create unauthorised access paths that conventional perimeter thinking misses. In practice, the identity question is not only who logs in, but which machine, token or delegated workflow is allowed to act. Without inventory, authentication policy and lifecycle controls drift apart, and teams cannot prove what is in scope for review, testing or revocation.

Practical implication: maintain a complete API inventory that ties each endpoint to an owning identity, access method and review cadence.

How AI workflows widen API exposure

Generative AI adds API calls that are often indirect, dynamic and difficult to govern using static application controls. LLM-backed features may query backend APIs, fetch context, or trigger transactions through chained services, which means a prompt injection or abuse of a connected workflow can become an authorisation problem, not just a model problem. That is why the identity of the calling system matters. The key control boundary shifts from the user interface to the machine identity, token scope and delegated authority behind the AI workflow.

Practical implication: bind AI workflows to tightly scoped machine identities and review every downstream API they can invoke.

Why hidden API costs keep compounding

The costs described in the article are cumulative because one weak API can create several downstream effects at once: breach response, outage, legal exposure, fines, customer churn and partner distrust. Once the endpoint is embedded in revenue or data flows, the issue is no longer a single vulnerability but a governance failure over exposed business processes. That is why API security and identity governance converge. The same inventory, authentication, entitlement and monitoring decisions determine whether an API can be abused and whether the abuse is contained quickly.

Practical implication: treat high-value APIs as governed business access paths, not isolated technical interfaces.

Threat narrative

Attacker objective: The attacker wants to use ungoverned API access to steal data, disrupt services or trigger unauthorised business actions at scale.

1. Entry occurs through exposed, shadow or poorly inventoried APIs that attackers can discover and reach without reliable ownership or monitoring.
2. Credential abuse follows when authentication is weak, overly broad or reusable, allowing credential stuffing, delegated misuse or unauthorised API calls.
3. Impact comes from data scraping, denial of service, prompt-injected AI actions or service disruption that produces financial loss, regulatory exposure and trust damage.

Breaches seen in the wild

• Moltbook AI agent keys breach: Moltbook breach exposed 1.5M AI agent keys.
• LiteLLM PyPI package breach: LiteLLM PyPI supply chain attack, credentials stolen from users.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

API security is now a governance problem, not a narrow AppSec task. The article correctly frames hidden API costs as operational, financial and reputational, but the deeper issue is control ownership. When APIs mediate access to data and actions, they become part of the identity plane, because every call is an access decision. Practitioners should manage APIs as governed access paths with explicit ownership.

Shadow APIs create an identity blind spot that most control programmes still underweight. Undocumented endpoints, zombie services and third-party integrations are not just extra attack surface. They are unmanaged access routes whose identities, scopes and review obligations are unknown, which makes conventional review and testing incomplete. The named concept here is API identity sprawl: the gap between what exists and what is governed. Practitioners should close that gap before expanding AI-linked workflows further.

AI does not invent a new problem here, it accelerates an old one. LLMs and agent-like workflows simply multiply the number of API calls, service identities and delegated paths that must be controlled. That means the same weaknesses that affected conventional APIs now propagate faster through AI-enabled systems. Practitioners should assume every AI-connected API path is a governance path, not a convenience layer.

Business impact makes API governance inseparable from resilience and trust. The article’s financial framing is persuasive because a compromised API can trigger outage, compliance exposure and customer loss in the same event. In identity terms, that is a blast-radius problem: how far one abused access path can reach before containment. Practitioners should design for containment and revocation as core governance outcomes, not afterthoughts.

From our research library:

• AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
• Secrets management is a top five cybersecurity priority for only 33% of organisations, behind cloud security (45%), API security (42%), and endpoint security (36%), according to the 2024 State of Secrets Management Survey.
• Read next: AI Agent Identity Security Buyer's Guide

What this signals

API identity sprawl: the real governance problem is not merely the number of endpoints, but the number of access paths that lack clear ownership, scope and revocation discipline. As AI-enabled workflows proliferate, every undiscovered API becomes a potential identity boundary failure, not just a code quality issue.

Security teams should expect API governance to move closer to identity lifecycle management, because the practical question is increasingly who or what can invoke a function, not just whether the function is patched. That makes entitlement review, machine credential scope and shadow API discovery part of the same operating model.

The article’s risk framing aligns with broader API and secrets trends: AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026. That pace makes inventory and revocation speed a programme design issue, not a tooling detail.

For practitioners

• Build a complete API identity inventory Map every public, internal, shadow and third-party API to an owner, an authentication method and an access review cycle. Include AI-connected endpoints and service-to-service paths that are not visible in user-facing workflows.
• Scope machine credentials to each API path Issue credentials and tokens with the smallest feasible scope for the specific API action, then revoke unused or overly broad credentials that can reach sensitive data or business functions.
• Test AI-connected APIs as privileged workflows Treat LLM-backed features and automated agents as callers that can trigger downstream API actions, and validate the full chain from prompt or event to data access, update or transaction.
• Put shadow API discovery into continuous monitoring Run recurring discovery against code, gateways and runtime traffic so undocumented endpoints and zombie APIs are detected before they become invisible access paths.

Key takeaways

• Hidden APIs, shadow integrations and AI-connected workflows turn API exposure into a governance problem as much as a technical one.
• The strongest control point is not only vulnerability testing, but knowing which identities, tokens and delegated paths can invoke each API.
• As AI usage expands, unmanaged API access can trigger data loss, outage and trust damage through the same weak control plane.

Key terms

• API Inventory Management: API Inventory Management is the practice of discovering, cataloging, and maintaining all application programming interfaces used across an organization. It records ownership, purpose, authentication method, data exposure, dependencies, and lifecycle status, so security and engineering teams can reduce shadow APIs, control access, and retire unused interfaces before they become risk.
• Shadow API: An API endpoint that exists in production but is not fully known, reviewed, or governed by the security programme. Shadow APIs often emerge through fast delivery, copy-paste development, or overlooked internal routes, and they create untracked exposure because they sit outside inventory, policy, and ownership processes.
• Machine Credential: A machine credential is a secret or identity artifact used by software rather than a person. It includes service account credentials, API keys, tokens, and certificates. In practice, the main risk is not just exposure, but unmanaged lifecycle, unclear ownership, and overbroad access.
• AI-Connected Workflow: An AI-connected workflow is any process in which an AI system, model, or agent can read, transform, or route sensitive data as part of business operations. The governance challenge is that these workflows often inherit access from humans or services without a clear business justification for every permission.

Deepen your knowledge

NHI Mgmt Group covers identity security, NHI governance, and agentic AI through independent research, practitioner guides, and the NHI Foundation Level course. It is designed for teams that need to govern machine identities, access scope, and lifecycle control across modern environments.