Shadow AI tokens create hidden access paths beyond IAM control

At a glance

What this is: This is a blog post arguing that shadow AI becomes shadow access when tokens and keys give AI systems persistent, untracked access beyond formal identity governance.

Why it matters: It matters because IAM, NHI, and governance teams need a control model for non-human access that can be discovered, scoped, reviewed, and revoked before it silently expands.

By the numbers:

• 28.65 million new hardcoded secrets were detected in public GitHub commits in 2025 alone, a 34% year-over-year increase and the largest single-year jump ever recorded.
• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
• 64% of valid secrets leaked in 2022 are still valid and exploitable today, proving that detection alone is not enough without automated revocation.

👉 Read Token Security's analysis of shadow AI tokens and untracked access

Context

Shadow AI becomes a governance problem when tokens and keys let non-human systems keep using enterprise access without clear ownership or review. In practice, the issue is not whether an AI workflow is useful, but whether the identity behind it is governed as a first-class access path from the moment it is created.

That distinction matters for NHI programmes because tokens do not behave like human accounts. They can live inside scripts, notebooks, CI/CD pipelines, and orchestration layers, which means access often outlives the experiment that created it and keeps working after the business context has changed.

The article's central claim is that shadow access is an identity failure, not a model failure. Once AI systems can authenticate independently, traditional IAM visibility and review processes stop being sufficient unless they are extended to non-human identities and their lifecycle.

Key questions

Q: How should security teams govern AI tokens that are embedded in scripts and pipelines?

A: Treat each token as a managed non-human identity with an owner, scope, expiry, and revocation path. If the token is buried in code or automation, it should still be discoverable and tied to a business purpose. Access that cannot be inventoried cannot be governed, so discovery must come before control hardening.

Q: Why do long-lived API keys create more risk for AI systems than for traditional workloads?

A: AI systems can keep using a key long after the original experiment, approval, or owner has changed. That makes the credential persist beyond its intended context and turns a temporary implementation detail into durable enterprise access. The longer the key lives, the more likely it is to drift beyond least privilege.

Q: What do organisations get wrong about shadow AI access?

A: They often focus on whether the AI output is acceptable instead of whether the underlying access is still appropriate. A workflow can behave normally while holding excessive or stale privileges. The governance question is not only what the AI did, but what it was able to do in the first place.

Q: How can teams tell if secret sprawl is becoming an identity problem?

A: When the same secret appears in multiple environments, has unclear ownership, or lacks a revocation process, it has moved from a configuration issue to an identity issue. That is the point where access paths become invisible and reviewable controls stop being effective. Visibility, ownership, and expiry are the key signals.

Technical breakdown

How tokens turn shadow AI into shadow access

Tokens, API keys, and secrets give AI systems the ability to authenticate to services without a person in the loop. That creates a persistence problem because the access path is often embedded in code or orchestration rather than attached to a managed identity object. Once the token is reused across environments, attribution becomes weak and the organisation loses the link between business purpose and technical privilege. The technical failure is not just exposure, but durable authentication without a lifecycle boundary.

Practical implication: inventory every AI-facing token as a governed identity artifact, not as an implementation detail.

Why static IAM models miss untracked AI behaviour

Most IAM controls were built around users, groups, roles, and approval flows that assume access is requested, assigned, and reviewed through a human cadence. Shadow AI breaks that model because the runtime actor can keep acting after the original approval context has disappeared. Behavioural monitoring may confirm that a system executed normally, but it cannot prove that the underlying access should still exist. That gap is where untracked AI behaviour persists.

Practical implication: pair activity monitoring with access discovery and entitlement ownership for every AI-connected token.

Why long-lived secrets create invisible cross-domain access

When tokens are placed into scripts, CI/CD pipelines, workflow automation, or SaaS integrations, they often cross data and environment boundaries without a visible identity lifecycle. The secret is the credential, but the real problem is the hidden path it creates into multiple systems. If scope is broad and expiry is absent, the access path can survive redeployments, team changes, and workflow changes. That makes the secret itself a long-term control plane.

Practical implication: treat secrets sprawl as an access architecture issue and enforce expiry, ownership, and revocation.

NHI Mgmt Group analysis

Shadow access is the correct name for persistent AI authentication without governance. Shadow AI only becomes operational risk when a token creates durable access that no one can clearly own, review, or revoke. That is not a tooling issue in the narrow sense, because the security boundary has shifted from the model to the identity behind it. Practitioners should treat shadow access as a distinct governance failure mode, not as a subset of generic shadow IT.

Tokens expose a trust assumption that IAM programmes have relied on for years: access is reviewable because it is stable. That assumption fails when AI workflows can embed credentials directly into code and keep using them across environments without a stable human operator. The implication is that access review cadences alone do not solve non-human risk because the identity can persist outside the review window.

Identity-first AI security is now a lifecycle problem, not a policy problem. The article shows that the relevant failure is not whether access was once approved, but whether it is still owned, scoped, and revocable after the AI workflow changes. Lifecycle governance for non-human identities has to cover creation, use, drift, and offboarding as one chain. Practitioners should stop treating AI tokens as temporary exceptions.

Secret sprawl is the hidden distribution layer of shadow access. Tokens do not stay confined to one system. They migrate into notebooks, pipelines, automation jobs, and third-party integrations, which creates overlapping access paths that are hard to enumerate and even harder to retire. The operational conclusion is that visibility into where secrets live matters as much as how they are used.

AI behaviour can look compliant while the underlying access is not. A workflow can execute exactly as designed and still be operating against data or systems it should never have been able to reach. That is why behavioural observability and authorisation governance cannot be substituted for one another. Security teams need both if they want to prevent silent expansion of non-human access.

From our research:

• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, according to Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which shows how limited identity discovery remains in practice.
• For teams addressing secret sprawl directly, the Guide to the Secret Sprawl Challenge covers the operational patterns behind hardcoded credentials and recovery planning.

What this signals

Secret sprawl is now the practical boundary of AI governance. If tokens can live in notebooks, pipelines, and orchestration layers, then identity teams have to assume that access will be distributed long before it is formally approved. That is why the control problem is not just secret storage, but continuous discovery of where non-human credentials actually execute.

With 96% of organisations storing secrets outside secrets managers in vulnerable locations including code, config files, and CI/CD tools, the access surface is already fragmented before AI is added to it. The immediate programme signal is to merge secrets governance, NHI ownership, and lifecycle offboarding into one operating model rather than managing them as separate queues.

Shadow access: when AI is visible but its privileges are not. This is the failure mode practitioners should prepare for as AI adoption grows. Continuous entitlement review, revocation triggers, and workflow-to-secret mapping become the minimum acceptable controls when runtime access can outlive the original use case.

For practitioners

• Inventory AI-issued tokens as identities Build a register of every token, API key, certificate, and secret used by AI tools, agents, notebooks, and orchestration layers. Record owner, scope, issuing system, expiry, and the workflow that consumes it so the access path can be governed as an identity lifecycle.
• Replace static credentials with short-lived identities Use dynamic, time-bound credentials wherever AI systems need to authenticate to cloud, SaaS, or internal services. Short-lived access reduces the chance that a forgotten token becomes a durable shadow access path across environments.
• Map tokens to the exact AI workflow that uses them Tie each secret to a specific workflow, service, or automation job rather than to a broad team or project bucket. That mapping makes it possible to detect when a token outlives the use case that created it.
• Enforce revocation when AI use cases change Create offboarding and review triggers for AI experiments that move into production, switch owners, or stop being used. Without a revocation trigger, stale tokens keep working after the business justification has disappeared.

Key takeaways

• Shadow AI becomes a governance problem when tokens create durable, untracked access paths that no one owns end to end.
• The article's core evidence is that AI systems can authenticate across enterprise environments while traditional IAM visibility and review models still assume human-paced access.
• Practitioners need token inventory, short-lived credentials, and lifecycle offboarding for non-human identities before AI access expands beyond reviewable control.

Key terms

• Shadow AI: AI tools, models, agents, or workflows used without formal security review or identity governance. In practice, the risk is not just undisclosed software, but undisclosed access paths that can persist beyond the original experiment and bypass normal lifecycle controls.
• Shadow Access: Untracked or unmanaged access created when a non-human system can keep authenticating to enterprise services without clear ownership, expiry, or review. It is an identity problem because the credential becomes a durable route into systems even when no one can explain why it still exists.
• Secret Sprawl: The spread of credentials, API keys, tokens, and certificates across code, pipelines, notebooks, and collaboration tools. Secret sprawl matters because each copy increases the chance of unowned, over-scoped, or stale access that is difficult to revoke consistently.
• Non-Human Identity: Any machine or software identity that authenticates to systems, including service accounts, workloads, API keys, tokens, certificates, bots, and AI agents. For autonomous or AI-driven workflows, NHI governance must also track runtime use, ownership drift, and offboarding, not just issuance.

What's in the full article

Token Security's full blog covers the operational detail this post intentionally leaves for the source:

• A closer look at how tokens persist inside scripts, notebooks, CI/CD pipelines, and workflow automation.
• Operational guidance on replacing static tokens with short-lived identities in AI-connected environments.
• The article's framing of shadow access as an identity problem rather than a model or prompt problem.
• Practical detection steps for mapping token usage to specific AI workflows and services.

👉 Token Security's full post covers token persistence, access drift, and practical detection patterns for shadow access.

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.