The Crux of AI
Artificial intelligence is entering a new phase. What began as a productivity tool for answering questions, generating content, and summarizing information is rapidly evolving into agentic AI—systems capable of planning tasks, making decisions, calling tools, writing code, and executing multi-step workflows with limited human intervention.
That evolution creates a fundamentally different cybersecurity challenge.
The question is no longer simply, “What information are employees entering into AI?” It is increasingly, “What can AI access, change or execute on an organization’s behalf?”
At the same time, employees are adopting AI tools, browser-based applications, and low-code/no-code platforms outside formal IT, and security controls. The combination of autonomous AI + Shadow AI + citizen-developed applications could create a growing layer of invisible access, and privilege across enterprise environments.
When AI Starts Acting on Its Own
Traditional generative AI generally waits for a user to prompt. Agentic AI can take a goal and determine the steps required to accomplish it. An agent may search for repositories, interact with APIs, create or modify files, execute code, access applications, or initiate workflows. This creates a new security problem: AI becomes an actor with access privileges.
OWASP identifies excessive agency as a major AI security risk, particularly where agents have excessive functionality, excessive permissions or excessive autonomy. An agent connected to a database, development environment, or enterprise application could potentially perform actions far beyond what its original task requires.
The risk becomes particularly serious when an agent operates at machine speed.
A compromised human account may require several steps to cause damage. An autonomous agent can potentially chain actions together rapidly across interconnected systems. Recent 2026 incidents involving AI agents escaping controlled environments have further demonstrated why containment, monitoring, and privilege separation deserve greater attention as autonomous capabilities advance.
The Shadow AI Problem is Getting Bigger
While organizations are trying to understand enterprise AI, employees are often already using it. Employees may turn to public AI assistants, coding copilots, AI-powered browser extensions or specialized tools to accelerate everyday tasks. The problem is not necessarily malicious intent. Often, employees simply choose the fastest tool available to complete their work.
That creates Shadow AI—AI usage that exists outside the organization’s approved technology, governance, and security framework.
Sensitive information can be entered into external AI services, copied into prompts or exposed through integrations without security teams having visibility into the activity. The result can be data leakage, compliance exposure, and an expanding inventory of unknown third-party applications.
NIST’s Generative AI Risk Management Profile highlights the need for organizational governance, tracking, documentation, and appropriate oversight as organizations adopt generative AI across different use cases.
The visibility challenge becomes even harder when AI is embedded inside everyday applications rather than deployed as a clearly identifiable enterprise system.
Low-Code/No-Code: The Hidden Application Layer
AI is also making it easier for non-developers to build applications and automate workflows. Low-code/no-code platforms can deliver genuine business value. But when employees create applications without security or IT oversight, organizations can unintentionally create Shadow IT at application speed.
These applications may connect to databases, APIs, cloud services, and business systems. Poorly configured credentials, excessive permissions, exposed interfaces or inappropriate data handling can turn a seemingly harmless workflow into an unauthorized entry point.
OWASP specifically warns that AI-assisted development within low-code/no-code environments can introduce security misconfigurations, exposed secrets, unprotected endpoints, and excessive data access. The concern is therefore not the technology itself. It is the absence of visibility and control over who created it, what it can access, and what privileges it possesses.
The Privilege Problem Behind AI
This is where the AI conversation intersects directly with identity and endpoint security. An AI agent, automation workflow or employee-created application ultimately needs permission to do something. If those permissions are broader than necessary, the potential impact of compromise or misuse increases dramatically.
The principle should therefore be simple:
AI should have only the privileges required to perform its specific task—and only for as long as those privileges are required.
For high-impact actions, organizations should consider human approval, granular authorization, activity monitoring, and strong audit trails. OWASP similarly recommends minimizing agent permissions and enforcing authorization in downstream systems rather than relying on the AI itself to determine whether an action is permissible.
This becomes particularly important at endpoints, where employees interact with AI tools, download applications, execute generated code, and access business-critical resources.
What Security Leaders Should Watch
Organizations should begin treating AI adoption as an access-control and privilege-management issue, not merely an AI governance issue. Security leaders should ask:
- Which AI tools are employees using?
- What corporate or sensitive information is being entered into them?
- Which AI agents can access enterprise applications or data?
- What identities and credentials are those agents using?
- Can an agent execute privileged actions without human approval?
- Which low-code/no-code applications have been created outside IT?
- What databases, APIs, and endpoints can those applications access?
- Are privileges limited to the minimum required?
- Can security teams monitor and audit these activities?
The objective should not be to stop employees from using AI. Restrictive policies alone may simply encourage more Shadow AI. The objective is to make AI usage visible, governed, and least privileged.
The Bottom-line
AI is moving from answering to acting. At the same time, employees are moving from using applications to building their own AI-enabled workflows.
That creates a convergence of risks: autonomous agents with excessive permissions, employees using unmanaged AI services, AI-generated code deployed without adequate scrutiny, and low-code/no-code applications connecting directly to sensitive enterprise resources. The organizations most prepared for this shift will be those that recognize a fundamental reality:
Every AI agent is becoming an identity. Every AI action can become a privilege. And every unmanaged AI workflow can become an attack path.
As AI becomes increasingly autonomous, cybersecurity cannot depend solely on what the AI is designed to do. It must also control what AI is allowed to access and execute. That makes least privilege, continuous monitoring, and centralized control increasingly important foundations for securing the AI-enabled enterprise.
