Shadow AI is creating invisible access paths across enterprise identity

At a glance

What this is: Shadow AI is the unsanctioned use of AI tools and integrations that creates hidden, proxy-like access to corporate data and systems.

Why it matters: It matters because IAM, IGA, and PAM programmes can miss AI-driven access paths that look like ordinary user activity but behave like unmanaged non-human identities.

👉 Read Token Security's analysis of Shadow AI and invisible access paths

Context

Shadow AI is what happens when employees connect unapproved AI tools to corporate systems and data through valid credentials, often without security review. The problem is not just data entering a chatbot, but the creation of invisible access paths that let external models act inside the enterprise.

For IAM teams, this shifts the issue from tool approval to identity and access control. A user-granted AI integration can inherit broad permissions, persist after the browser is closed, and bypass the governance processes designed for service accounts, API keys, and reviewed machine identities.

Key questions

Q: How should security teams govern AI tools that connect to corporate systems?

A: Security teams should govern AI tools as non-human identities with scoped access, named ownership, and revocation criteria. That means inventorying OAuth grants and API keys, limiting privileges to the smallest workable scope, and reviewing access on a lifecycle schedule. If the tool can read, write, or trigger actions, it needs the same control discipline as any other machine identity.

Q: Why do AI integrations create hidden access risk even when SSO is used?

A: SSO does not solve the risk because the AI tool can inherit a user’s permissions after authentication and then act independently through APIs or tokens. The logon looks legitimate, but the downstream access is a separate non-human path. Security teams need to govern the resulting token scope, not just the user sign-in event.

Q: What breaks when AI tools are allowed broad write access to internal systems?

A: Broad write access turns an AI tool from a helper into an unreviewed operator. It can modify code, create tickets, change records, or move data in ways that expand the attack surface and complicate incident response. The failure is not only overprivilege, but also the loss of clear accountability for actions taken through the AI intermediary.

Q: How can organisations reduce shadow AI risk without banning AI use entirely?

A: Organisations should allow approved AI use while blocking unsanctioned high-risk integrations. Provide sanctioned tools, keep sensitive systems off external write paths, and use continuous detection to spot new AI-linked credentials. That approach reduces covert adoption while preserving productivity, which is more realistic than trying to suppress AI use altogether.

Technical breakdown

How OAuth grants and API keys create invisible AI access paths

Most shadow AI risk begins when a user authorises an external AI tool to connect to internal systems through OAuth or an API key. That authorisation creates a durable credentialed path that can read, modify, or move data without the visibility usually associated with a new account. Because the access is attached to a human identity or a token issued under that identity, it often escapes separate machine identity review. The security problem is not the AI interface itself, but the persistent integration behind it.

Practical implication: inventory AI-linked OAuth grants and API keys as governed identities, not as casual app integrations.

Why shadow AI breaks identity governance and audit trails

Shadow AI breaks the usual 1:1 relationship between a person and an action. If an AI tool uses a user’s token to query data, create tickets, or change code, the logs may show the human name even though the AI performed the action. That collapses non-repudiation and weakens forensic analysis, because the access path is hidden inside legitimate-looking activity. In governance terms, the actor is still non-human, but the accountability chain is obscured by the user credential it borrowed.

Practical implication: separate human authentication from non-human action logging so AI-mediated actions are traceable on their own.

Why static security controls miss AI-mediated access

Traditional controls are tuned for infrastructure state and network movement, not for AI intent or scope. Firewalls may allow traffic to an approved AI domain, DSPM may miss data that is processed in motion, and IGA may never see a token created ad hoc for an AI tool. That creates a gap between configuration and behaviour. An environment can look compliant while an external model is reading sensitive repositories, rewriting code, or sending data elsewhere through a valid but ungoverned access path.

Practical implication: add identity and API telemetry to your control stack instead of relying on perimeter or data-at-rest tools alone.

NHI Mgmt Group analysis

Shadow AI is an identity problem before it is a data-loss problem. The article correctly frames unapproved AI use as a proxy identity issue, because the real risk comes from the credentials and scopes attached to the tool. Once an AI integration can act with a user’s privileges, the enterprise has created an unmanaged non-human identity whether it named one or not. The practitioner conclusion is simple: identity governance must extend to every AI-mediated access path, not just named accounts.

Invisible access paths create privilege duplication without lifecycle control. A user who authorises an AI tool to access GitHub, Salesforce, or cloud infrastructure is effectively cloning part of their access into a new actor that is not on the recertification schedule. That is a lifecycle failure, not a visibility nuisance. NHI governance now has to track who granted the path, what scope was granted, and whether the path still matches the original business need. The practitioner conclusion is that access review must cover delegated AI access, not only direct user entitlements.

Shadow AI exposes an identity blast radius that traditional IAM reporting does not measure. The same token can let an external model read data, modify code, create tickets, or trigger downstream workflows, which makes the blast radius larger than the visible login event suggests. This is why least privilege is no longer just about account scope at issuance; it is about the actions an AI intermediary can chain from that scope. The practitioner conclusion is that governance must measure what the AI can do, not just what the human intended.

Unmanaged AI integrations are now part of the wider NHI surface, not an edge case. The article’s strongest contribution is its reminder that every plugin, token, and connected app becomes an identity object even if no one created it through formal IAM workflows. That aligns with the OWASP Non-Human Identity Top 10 and zero trust assumptions about continuous verification. The practitioner conclusion is to treat AI-connected access as a first-class NHI population with ownership, scope, and revocation requirements.

Identity does not end at authentication when the actor is an AI intermediary. Authentication may still succeed through SSO, but the security question shifts to what the connected AI can do once it is inside the trust boundary. That means the governance assumption behind many access reviews was designed for human-paced, visible action, not for hidden execution through external models. The practitioner conclusion is to redesign governance for delegated machine action across the full session, not just for login approval.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
• That gap is why practitioners should also read the Ultimate Guide to NHIs for the lifecycle controls that shadow AI commonly bypasses.

What this signals

Shadow AI is forcing IAM teams to broaden their identity inventory. If an AI tool can inherit user permissions and act through APIs, then the organisation no longer has a clean boundary between human access and machine action. With 70% of organisations already granting AI systems more access than they would give a human employee performing the same job, according to the 2026 Infrastructure Identity Survey, the governance problem is now structural, not exceptional.

Invisible access paths should be treated as a distinct control class. They sit between SSO, API governance, and NHI lifecycle management, which means no single team usually owns them end to end. That is the operational signal to build a combined inventory of AI-linked tokens, connected apps, and delegated write scopes before they become the default way work is done.

The programme implication is that review cadences, logging, and revocation workflows must be redesigned for proxy action. A login-centric operating model will miss the moment when a human grants an AI tool durable authority over internal systems, and by then the access path is already live.

For practitioners

• Inventory AI-linked credentials and grants Build a register of OAuth grants, API keys, personal access tokens, and browser extensions that connect AI tools to internal systems, then classify each by scope, owner, and data reach.
• Separate human login from AI action logging Record when an AI tool uses a user token, what system it touched, and what action it executed, so investigators can distinguish human activity from proxy actions.
• Restrict write access for AI integrations Default AI-connected tools to read-only access and require explicit approval for write or admin scopes, especially for repositories, production data stores, and workflow systems.
• Apply lifecycle review to delegated AI access Tie AI-enabled grants to a named owner and recertify them on the same cadence as other non-human identities, with revocation when the use case changes or ends.

Key takeaways

• Shadow AI is not just unsanctioned software use, it is the creation of unmanaged non-human access paths inside the enterprise.
• The main risk is not the chatbot interface but the token, scope, and proxy action that let an external model operate with internal privileges.
• IAM teams should inventory, restrict, and recertify AI-linked grants as first-class identities before they become the hidden default for enterprise access.

Key terms

• Shadow AI: Shadow AI is the unsanctioned use of AI tools, models, or agents inside an organisation without formal security visibility or approval. It becomes an identity problem when those tools connect to internal systems through tokens, OAuth grants, or browser extensions that act on behalf of a user.
• Invisible Access Path: An invisible access path is a credentialed connection between an external AI service and an internal system that is not properly represented in governance tooling. The path may look like ordinary user activity while actually enabling a non-human actor to read, write, or trigger downstream actions.
• Proxy Action: Proxy action is work performed by one actor on behalf of another, where the visible identity in logs is not the entity making the decision or executing the task. In shadow AI scenarios, the user appears in the record while the AI tool performs the actual operation through borrowed access.
• Delegated AI Access: Delegated AI access is the permission a user gives an AI tool to operate within enterprise systems using that user’s entitlements or a scoped token. It must be treated as a governed non-human identity because the delegated actor can persist, change scope, and outlive the original approval intent.

What's in the full article

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

• Examples of how employees create AI-linked access paths through OAuth, API keys, and browser extensions
• A side-by-side comparison of Shadow AI, Shadow IT, and traditional NHI risk patterns
• Operational detection ideas for spotting AI tools that inherit write scopes or touch sensitive systems
• Practical guidance on governing AI usage without blocking every sanctioned productivity tool

👉 Token Security's full post covers the access patterns, logging gaps, and control failures behind Shadow AI.

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 governance in your organisation, it is worth exploring.