TL;DR: Tokens and API keys let shadow AI authenticate across cloud, SaaS, and internal systems without ownership, expiry, or review, turning experimentation into persistent non-human access, according to Token Security. The core governance failure is that identity controls still assume access is human-owned, reviewable, and static.
At a glance
What this is: This analysis argues that shadow AI turns into shadow access when tokens and secrets let AI systems keep using enterprise services without formal identity governance.
Why it matters: It matters because IAM, IGA, and PAM programmes built around human ownership and review miss non-human access that is persistent, unattributed, and difficult to revoke.
Context
Shadow access is the state where an AI system can authenticate to enterprise services without the access being owned, reviewed, or clearly bounded. In this article's model, the governance gap is not AI output quality but identity control over the token itself.
The problem matters for NHI governance because tokens, API keys, and other secrets are behaving like standing non-human identities inside AI workflows. Once those credentials are embedded in scripts, orchestration layers, or automation paths, traditional IAM records can show an account exists without showing who is actually responsible for its use.
Key questions
Q: What breaks when AI tokens are not owned or reviewed?
A: The access path becomes durable even when the AI experiment was supposed to be temporary. Without ownership and review, teams lose the ability to say who approved the credential, why it still exists, or whether it should still be active. That is a governance failure, not just a visibility problem.
Q: Why do shadow AI tokens create more risk than visible automation?
A: Because visibility into output does not equal control over access. An AI workflow can behave normally while reaching cloud, SaaS, or internal systems through a secret that was never intended to be permanent or broadly scoped. The risk is the hidden entitlement, not the task automation itself.
Q: How can teams detect shadow access before it spreads?
A: Start by discovering active tokens across scripts, notebooks, pipelines, and orchestration layers, then map each token to a specific workflow and owner. Look for secrets with no expiry, excessive scope, or access that crosses data boundaries unexpectedly. Those are the signs that access has outgrown governance.
Q: Should AI credentials be governed like privileged access?
A: Yes, when the credential can reach sensitive data or production services. Privileged access controls are the right model because the issue is not just authentication, but the potential blast radius of a secret that can operate without a human session. Treat AI access as high-risk when review and revocation are unclear.
Technical breakdown
How tokens turn shadow AI into hidden access
Tokens, API keys, and secrets give an AI system a way to authenticate without a human session or a normal user lifecycle. Because the credential is often copied into a workflow, the AI can call APIs, databases, cloud services, or SaaS platforms repeatedly until the token is revoked. The control problem is not authentication alone. It is that the access object exists outside the normal ownership, expiry, and review model, so it behaves like a durable non-human identity even when the originating project was meant to be temporary.
Practical implication: treat every AI-issued token as an identity object with an owner, scope, expiry, and revocation path.
Why IAM visibility breaks in AI-driven workflows
Traditional IAM was built around human accounts, role assignment, and periodic review. Shadow AI breaks that model because the credential may sit inside a script, notebook, CI/CD pipeline, or orchestration framework with no obvious business owner. The article's central point is that logs can show API calls but not the intent behind them, so teams can observe activity without knowing whether the access should still exist. That creates a governance blind spot between authentication evidence and accountability.
Practical implication: inventory where AI workflows store and use secrets, then map each secret to a responsible owner and business purpose.
Why behavioural monitoring does not equal access governance
A system can behave as designed and still be using data or services it should not touch. Behavioural controls answer whether the workflow executed normally, while identity governance answers whether the access was authorised, owned, and proportionate. In shadow access situations, those are different questions. The article is right to separate them because an AI workflow can produce valid outputs while quietly expanding its effective privilege across systems and data boundaries.
Practical implication: monitor not only what the AI does, but also what services and data its credentials can reach.
Breaches seen in the wild
- OmniGPT breach claim 2025: A hacker claims to have leaked 34 million OmniGPT AI chat messages holding users' API keys and credentials; OmniGPT has not confirmed it.
- Salesloft OAuth token breach: hackers stole OAuth tokens to access Salesforce data via Salesloft.
Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Shadow access is an NHI governance failure, not an AI novelty. The article shows that the security issue begins when tokens and keys are allowed to behave like permanent access paths instead of governed identities. That shifts the problem out of the AI tooling layer and into NHI lifecycle control, where ownership, review, and offboarding should already exist. The practitioner conclusion is that AI access must be managed as identity, not as an exception.
Identity controls designed for human accounts do not survive tokenised AI workflows. Human-centric IAM assumes a session can be attributed to a person, reviewed against a role, and retired through normal lifecycle processes. Shadow AI breaks that assumption because the credential can persist independently of the person who created it and continue operating long after the experiment has ended. The implication is that current review cadences may be structurally unable to detect or certify this access.
Long-lived tokens create identity blast radius across cloud, SaaS, and internal systems. Once a token is embedded into multiple workflows, access spreads faster than ownership can be tracked. That creates a compounded control problem where one unmanaged secret can extend non-human access across several environments without a clean boundary. The practitioner implication is to think in terms of blast radius, not isolated credentials.
Shadow AI forces IAM, IGA, and PAM to converge on the same non-human lifecycle. The article is describing one access pattern that touches all three disciplines at once: provisioning, review, and privileged use. That matters because teams often treat AI access as a tooling issue while the underlying failure is governance fragmentation. The practitioner conclusion is that ownership of AI credentials must be assigned, certified, and revoked with the same seriousness as any other privileged NHI.
Shadow access should be named as a distinct control gap. Shadow access is the state where an AI system can continue to act through a token that no one can confidently attribute, review, or retire. Naming the failure mode matters because it clarifies that the threat is not merely undiscovered AI usage. It is undiscovered access that has outgrown the control model. The practitioner implication is to govern the access path first, then the AI behaviour.
From our research library:
- Organisations with high levels of shadow AI faced an average of $670,000 in additional breach costs compared with those with little or none, according to IBM's 2025 Cost of a Data Breach Report.
What this signals
Shadow access is the practical name for hidden non-human entitlement. Once a token is embedded in an AI workflow, the governance question changes from what the system said or did to what it can still reach. That is why identity-first security has to begin at issuance, not after the workflow is already running.
AI programmes that rely on static secrets inherit a control model that assumes access is stable, attributable, and reviewable. Those assumptions do not hold when credentials are copied into scripts, notebooks, and orchestration layers, so the security boundary moves from the model to the token lifecycle.
For practitioners
- Inventory AI-issued tokens and secrets Find every token, API key, and secret used by AI tools, agents, notebooks, scripts, and orchestration layers. Record where each credential lives, what it reaches, and who owns it.
- Assign explicit ownership to non-human access Require a named business and technical owner for each AI access path, including service scope, expiry, and revocation responsibility. If no owner can be identified, the credential should not remain active.
- Replace static secrets with short-lived identities Move AI systems away from persistent tokens toward dynamic credentials with narrow scope and automatic expiration. Where a workflow cannot support that, treat it as a governance gap rather than an exception.
- Review AI access like privileged access Fold high-risk AI credentials into access review and privileged access workflows so hidden permissions are recertified and removed on a governed cycle, not left to informal cleanup.
Key takeaways
- Shadow AI becomes shadow access when tokens create non-human access that outlives the experiment that introduced it.
- The article shows a governance gap across ownership, review, and revocation, which is why hidden AI access is hard to attribute.
- The most effective response is to manage AI credentials as governed identities with explicit scope, expiry, and accountability.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | Tokens and API keys embedded in AI workflows create leaked, persistent access paths. |
| NHI-07 — Long-Lived Secrets | The article focuses on tokens that persist without expiry or review. | |
| NHI-05 — Overprivileged NHI | The article highlights excessive scope and access that expands beyond intended use. | |
| Recommendation — Scan AI workflows for exposed secrets and revoke credentials that are no longer governed. Replace long-lived AI tokens with short-lived credentials and enforce expiry by default. Limit AI credentials to the smallest service scope that the workflow actually requires. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Credential lifecycle management is central to preventing unmanaged AI token persistence. |
| Recommendation — Apply authenticator lifecycle controls to issue, rotate, and revoke AI credentials on a governed schedule. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is about unreviewed entitlements hidden inside AI access paths. |
| Recommendation — Govern AI entitlements as access permissions and recertify them before they become invisible. | ||
Key terms
- Shadow Access: Shadow access is unauthorised or unmanaged access that continues to exist because a credential, role, or account was forgotten, reused, or never properly revoked. In NHI programmes, shadow access is especially dangerous because it can remain active across cloud, SaaS, and automation layers without obvious human ownership.
- Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
- Token Lifecycle: Token lifecycle is the full sequence of issuing, refreshing, expiring, revoking, and reauthorizing a credential. For delegated access, lifecycle state is the real indicator of whether a connection is still legitimate, because a valid-looking token can still be stale, disconnected, or out of scope.
- Identity Blast Radius: The amount of damage a compromised identity can cause across systems, data, and infrastructure. In NHI environments, it is shaped by permissions, network reach, and administrative capability rather than by the credential alone. Reducing blast radius is a containment strategy that limits lateral movement and data exposure.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle 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.
Published by the NHIMG editorial team on July 6, 2026.
Updated on October 6, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org