AI Sprawl

Expanded Definition

AI sprawl describes the point where AI tools, copilots, agent frameworks, and embedded model features proliferate faster than governance can track them. In NHI and IAM terms, the concern is not just software count. It is the creation of unmanaged service identities, duplicated API keys, inconsistent approval workflows, and hidden data paths that bypass normal access review. Guidance varies across vendors on whether shadow AI, citizen-built agents, and embedded AI features belong in the same category, but the security problem is the same: control boundaries become unclear. That is why AI sprawl must be treated as an access governance issue, not only a procurement issue, and why it maps closely to the control discipline described in the NIST Cybersecurity Framework 2.0. NHIMG’s analysis of the Ultimate Guide to NHIs — Key Challenges and Risks shows how fast unmanaged identities can accumulate when ownership is diffuse. The most common misapplication is treating AI sprawl as a cataloging problem, which occurs when teams record tools but fail to govern the identities, secrets, and data permissions behind them.

Examples and Use Cases

Implementing governance for AI sprawl rigorously often introduces approval friction, requiring organisations to weigh speed of experimentation against visibility, segregation, and revocation readiness.

• A department adopts multiple chat assistants, each connected to different file stores, creating separate permission paths that IAM cannot reconcile quickly.
• A product team deploys an internal AI agent that uses service tokens from a shared vault, then expands its scope without a formal review cycle.
• A business unit experiments with model plugins and workflow automations, but no owner can answer which datasets or secrets each integration can reach.
• Security teams discover that a new AI feature in an existing SaaS platform is processing regulated data, even though the original application approval never covered that data flow.
• After reviewing incidents similar to the DeepSeek breach, teams realise that model exposure, backend credentials, and chat histories can all become part of the same sprawl problem.

These patterns are also reflected in the operational guidance of the NIST Cybersecurity Framework 2.0, which emphasises asset visibility, control, and recovery across the environment.

Why It Matters in NHI Security

AI sprawl matters because every unmanaged AI integration can introduce a new non-human identity, a new secret, or a new trust relationship that falls outside normal joiner-mover-leaver processes. NHIMG research in The State of Secrets in AppSec highlights how fragmented control is already common, with organisations maintaining an average of 6 distinct secrets manager instances and spending 32.4% of security budgets on secrets management and code security. That fragmentation becomes more dangerous when AI tools can learn from code, reuse prompts, or move sensitive data into services that security teams do not formally own. The result is weaker auditability, slower revocation, and higher exposure when a token or connector is compromised. AI sprawl also undermines Zero Trust because trust decisions cannot be consistently enforced when the inventory is incomplete. In practice, the issue often surfaces only after a token leak, a data handling complaint, or an access review exposes unknown AI-connected systems, at which point AI sprawl becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• Why does Agentic AI dramatically increase identity sprawl?
• Why do AI agents increase the risk of NHI sprawl?
• Why do AI copilots make permission sprawl more dangerous?
• How should security teams stop AI coding tools from creating secrets sprawl?