As organizations race to adopt AI, they are creating a new class of security risk rooted not in vulnerabilities, but in visibility.
According to a survey conducted for
Delinea's 2026 Identity Security Report, nearly all organizations admit gaps in their abilities to track non-human identities (NHIs) such as service accounts, APIs, and
AI agents.
These identity blind spots make it difficult to answer a basic security question: Who, or what, is accessing your critical systems and data? As the widespread implementation of AI accelerates
identity sprawl, identity visibility is not just lagging behind but may soon be the primary weakness in modern security architectures.
How identity-visibility gaps harm organizations
The scale of the identity-visibility problem is staggering. The Delinea report shows that 90% of organizations have some form of identity-visibility gap, with the most persistent gaps existing in AI-related environments.
Many companies say they're confident in their abilities to monitor
non-human identities, but that confidence often masks a lack of real-time validation. For example, while 82% of responding companies say they can identify NHIs, fewer than one-third verify how those identities behave in real time.
This creates what the report calls an "AI security confidence paradox." Organizations think they are secure, but they can't prove it. Without continuous identity monitoring, security teams can't detect anomalous behavior or enforce
governance policies effectively. Their NHI identity risk becomes both invisible and unquantifiable.
"The business is accepting AI risk to stay competitive, but because AI is such a new paradigm, they're accepting it without actually understanding qualitatively or quantitatively what the risk is," says Dr. Gerald Auger of Simply Cyber, one of the experts quoted in the Delinea report.
This problem is only made worse in AI-driven environments. AI agents operate autonomously, are often empowered to make context-based decisions, and may request new permissions dynamically. Unlike traditional NHIs, the behavior of AI agents is unpredictable, their abilities may be unconstrained, their decision-making tends to be opaque, and their actions may be difficult to audit for policy compliance. In the Delinea survey, 80% of organizations say they cannot fully understand why an AI agent or an NHI took privileged action.
How shadow AI forges unauthorized, hidden access paths
If authorized AI creates complexity, then
shadow AI — unsanctioned AI tools and agents deployed without IT oversight or approval, often by employees seeking productivity gains — introduces chaos.
The Delinea report finds that 53% of organizations regularly encounter unauthorized AI tools accessing their systems, yet only 28% say they can detect such activity in real time.
What makes shadow AI especially dangerous is its ability to blend in, as these tools often operate using legitimate credentials and mimic normal user behavior.
"Agents behave exactly like compromised credentials: They're trusted, they're persistent, they're pretty much invisible once they're inside," says Kayla Williams of Williams Rose AI Cyber Advisory, another expert quoted in the Delinea report.
The rapid adoption of AI at all organizational levels exacerbates the problem. Employees across departments are deploying AI tools on their own, creating decentralized innovation but centralized risk. Many of these AI tools are given elevated permissions and connect to enterprise systems and sensitive data without proper governance.
Because they look and behave like legitimate users, these shadow AI tools fail to raise alerts, yet their privileges enable them to create hidden access paths that security teams may never see.
Just three years ago, shadow AI most likely would have consisted of an employee typing proprietary data into public ChatGPT through a web browser. That was bad enough, but today, free open-source AI agents like
OpenClaw can be secretly installed on company endpoints and given wide latitude to access sensitive resources and make data-altering decisions.
"What ChatGPT did by putting a text field in front of the LLM and unlocking it for normal people, OpenClaw is doing for agents," says Auger.
How identity sprawl and limited governance expand the attack surface
At the heart of the visibility crisis is identity sprawl. Non-human identities already outnumber human users by a wide margin, and AI is accelerating that growth. Each new AI agent, API, or automated process introduces another identity, often with persistent, high-level privileges.
The Delinea report reveals that many organizations rely on static, long-lived credentials to manage these identities, despite the known risks of not rotating credentials for NHIs. These credentials may be easy to exploit and difficult to track, especially if combined with limited governance frameworks.
Worse, organizations often grant standing access to NHIs to avoid disrupting operations while acknowledging that this increases risk.
"If I, as an employee, had this level of standing access with this little oversight, it would trigger an incident. Someone somewhere would say, 'Oh no, no, no, this can't happen,'" says Williams. "The problem with NHIs is there's no one who owns the identity or who even knows it exists in some cases."
This sprawl creates an expanding attack surface that traditional
identity and access management (IAM) models are not designed to handle. These systems were designed to manage human users, not autonomous AI agents operating at machine speed.
As AI systems clone workflows, share permissions, and replicate across environments, trust relationships multiply faster than governance mechanisms can keep track of.
Ultimately, the issue comes down to a simple truth: Organizations can't secure what they can't see. Until organizations close the identity visibility gap through better discovery, monitoring, and governance, they will remain exposed to risks that are already inside their environments, operating quietly at scale.
