Shadow AI bypasses traditional gateways through NHI sprawl

At a glance

What this is: NHI Mgmt Group's analysis, drawing on Orca Security's blog post: Shadow AI hides inside approved tools and inherited NHI access, making traditional gateway controls miss the real data path.

Why it matters: IAM, NHI and cloud security teams need to shift from app blocking to identity-led discovery because the risk sits in OAuth apps, service accounts and embedded AI features, not just in overt shadow IT.

By the numbers:

• 80%+ of employees use unapproved AI tools, so shadow AI is already present in most environments.

Context

Shadow AI is not just an unsanctioned app problem. It is a governance problem where approved SaaS platforms, developer tools and automation paths embed AI features that move data and actions through already trusted identity chains.

The primary failure is visibility, not intent. When an AI capability authenticates through OAuth, service accounts or API keys, existing CASB, DLP and gateway controls often see legitimate traffic instead of an unmanaged AI workload.

For identity programmes, this places NHI inventory, privilege scope and data exposure assessment at the centre of AI governance. The article is about detecting the identity layer that makes shadow AI persistent rather than obvious.

Key questions

Q: What breaks when hidden AI appears inside approved SaaS applications?

A: Static app categories stop reflecting real risk once vendors embed GenAI or model connectivity into software that was already approved. The inventory still looks clean, but governance decisions, access reviews, and acceptable use policy are now based on stale assumptions about what the application can do.

Q: Why do service accounts and OAuth tokens increase breach impact in cloud environments?

A: Because they often carry standing access to more than one system and are trusted by the platforms they connect. That makes them ideal for lateral movement after compromise. In cloud and SaaS environments, the issue is not only that these identities exist, but that their scope is usually broader and their monitoring weaker than human access.

Q: What do security teams get wrong about shadow AI governance?

A: They often treat shadow AI as a banned-app problem when it is usually an identity and accountability problem. Employees can use approved tools, personal accounts, or embedded AI features in ways that bypass policy even when the app itself is not explicitly blocked. Governance has to follow the interaction, not just the endpoint.

Q: How should organisations govern shadow AI without blocking legitimate use?

A: Start with approved-use policy, tool inventory, and data classification. Then require that any AI system handling internal information has named owners, logged access, and defined credential paths. The goal is not prohibition, but visibility and control. If a tool cannot be inventoried or monitored, it should not process sensitive data.

Technical breakdown

Why traditional gateways miss embedded AI

Traditional web gateways are built to observe distinct applications and user sessions. Shadow AI often appears instead as a feature inside SaaS, an IDE extension, or an MCP server calling external APIs from within an already trusted platform. That means the security control point shifts from network perimeter inspection to identity, API and workload context. The hard part is that the traffic looks legitimate because the platform itself is approved, even when the AI behaviour is not. Practical detection depends on identifying where AI features are operating inside trust boundaries rather than outside them.

Practical implication: Map embedded AI features to the identity and API paths they use, not just to the application names they live inside.

How NHI sprawl turns AI into persistent access

The article shows AI systems inheriting access through OAuth tokens, API keys and service accounts. That is classic NHI sprawl: credentials are issued to a service or application, then reused by an AI assistant, agent or extension with standing privileges that outlive any single human task. Because these identities are not governed like employee accounts, they can accumulate broad permissions and remain unreviewed for long periods. The risk is not merely that AI can read data, but that it can continue reading and acting through durable machine credentials. Practical detection starts with ownership, scope and lifecycle, not with the model itself.

Practical implication: Inventory every credential an AI feature can inherit and validate who owns it, what it can reach and when it was last reviewed.

Why data exposure must be judged with privilege depth

Shadow AI risk is not uniform. A summarisation tool that only sees public content is a very different control problem from an agent that can read source code, secrets, customer records or production configurations. The article’s useful distinction is between read exposure and action authority. Read access creates confidentiality and compliance risk, while write access creates operational and integrity risk because the tool can change repositories, deployments or communications. Security teams need to score both dimensions together, because a low-friction AI feature with high authority is often the highest-risk case.

Practical implication: Classify shadow AI by what it can read and what it can change before deciding whether to monitor, restrict or revoke it.

Threat narrative

Attacker objective: To move sensitive data and action authority through trusted identity paths that evade traditional gateway controls.

1. Entry occurs when AI capability is introduced through a trusted SaaS feature, IDE extension, MCP server or CI/CD integration rather than a separate external application.
2. Credential access happens when the AI function inherits OAuth tokens, API keys or service account permissions that were originally granted to the surrounding platform.
3. Escalation follows when those inherited privileges expose repositories, secrets, deployment settings or production data beyond the intended human task.
4. Impact is continuous data exposure, policy bypass and potential modification of systems or communications through an identity that security teams did not explicitly govern.

Breaches seen in the wild

• CI/CD pipeline exploitation case study: Credentials in an exposed .git/config let a researcher edit a Bitbucket pipeline so it planted their SSH key on the server. No victim was named.

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

Shadow AI is now an identity governance problem disguised as an application problem: the real control failure sits in OAuth apps, service accounts and embedded features that inherit trust inside approved platforms. Traditional perimeter logic assumes the risky object is the app, but in shadow AI the risky object is often the identity behind the feature. The practitioner conclusion is simple: if the identity layer is unmanaged, the AI layer cannot be governed.

NHI sprawl is the mechanism that makes shadow AI durable: AI assistants and embedded automations are not dangerous only because they are AI, but because they inherit standing privileges that were never intended for machine-scale, continuous use. That is a direct OWASP-NHI issue, especially NHI-04, NHI-05 and NHI-07, because authentication, privilege and lifecycle were designed for a different usage pattern. Practitioners should treat every AI-connected credential as part of the NHI estate.

Shadow AI breaks the assumption that approved platforms imply approved behaviour: once AI features run inside sanctioned SaaS or developer tooling, visibility controls that key off application allow-lists lose their edge. The new governance problem is not blocking a tool at the border, but detecting when a trusted platform starts processing, retaining or acting on data in ways the programme never reviewed. The conclusion is that trust must be reassigned from platform name to identity, data path and privilege scope.

Trusted path illusion: the article shows that approved software can conceal unapproved AI processing without changing the outward traffic pattern. That means discovery has to move from app-centric monitoring to identity-centric inventory and data-flow correlation. Practitioners should re-baseline what counts as sanctioned use before the hidden path becomes the default path.

Golden paths need governance, not just convenience: the piece correctly points to approved tools and JIT elevation, but the deeper issue is that developer productivity incentives will always route around friction. Unless access requests, ownership and review cadences are built into the identity model, shadow AI simply migrates from one sanctioned surface to another. The lesson is to govern the path, not just the tool.

From our research library:

• NHIs outnumber human identities by 25x to 50x in modern enterprises, according to the Ultimate Guide to NHIs.
• Read next: Ultimate Guide to NHIs — Key Challenges and Risks

What this signals

Trusted path illusion: the most important change for programme owners is that sanctioned software can now conceal unsanctioned AI processing. That shifts the detection model from application allow-lists to identity, API and data-flow correlation, because the platform name no longer tells you whether the behaviour is governed.

Shadow AI should be added to the NHI inventory and recertification conversation, not left in a separate AI or SaaS workstream. When agents, extensions and embedded features inherit service account permissions, the security boundary is no longer the product category but the credential lifecycle behind it.

96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, according to the Ultimate Guide to NHIs. That matters here because shadow AI commonly reaches those same locations first, so the exposure problem and the AI governance problem are now the same operational queue.

For practitioners

• Map AI-connected identities first Build a living inventory of OAuth applications, service accounts, API keys and browser extensions that can touch AI services or embedded AI features. Treat ownership, scope and last review date as mandatory metadata, because unmanaged credentials are the real entry point.
• Audit SaaS and IDE surfaces for embedded AI Review collaboration tools, developer environments and CI/CD pipelines for hidden AI functions, extension packs, model calls and newly enabled summarisation or code assistance features. Focus on where AI appears inside already trusted software, not only on standalone AI products.
• Score read access and write authority separately Rank every shadow AI use case by what data it can read and what systems it can modify. Escalate any tool that can reach secrets, production data or deployment systems, because write capability changes the control response from monitoring to restriction.
• Replace standing privileges with time-bound access Use just-in-time elevation for AI features that need sensitive repositories, environments or data stores. The goal is to make privileged access explicit, short-lived and reviewable instead of leaving inherited permissions in place indefinitely.
• Publish an approved AI access path Give developers a sanctioned route with pre-reviewed tools, clear data handling rules and fast-track security review. If the governed route is slower than the shadow route, adoption will keep flowing around controls.

Key takeaways

• Shadow AI is most dangerous when it runs inside trusted software and inherits the permissions already attached to that environment.
• The article ties the risk to identity sprawl, continuous API use and hidden data flow, not to a single rogue application.
• Programme owners should move from perimeter blocking to identity-led discovery, privilege scoping and time-bound access for AI-connected workflows.

Key terms

• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• 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.
• Standing Privilege: Standing privilege is access that remains active even when no immediate task requires it. For NHI programmes, it is a common failure mode because long-lived credentials and persistent roles create unnecessary exposure. Reducing standing privilege usually means tighter expiry, on-demand access, and clearer review of who or what still needs access.
• Identity-Centric Data Discovery: Identity-centric data discovery is the process of finding and organising personal information by linking records to a person rather than only matching keywords or patterns. It supports DSARs by correlating data across environments, resolving ambiguous identities, and avoiding the need to centralise sensitive information.

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 building or maturing an IAM programme, it is worth exploring.