AI is changing the enterprise attack surface. Here are five controls your security strategy needs.
Enterprise AI adoption is accelerating. From copilots embedded in business applications to autonomous agents capable of executing code and interacting with critical systems, AI is changing how organizations operate—and fundamentally altering their risk exposure.
For CISOs and CIOs, the challenge extends well beyond protecting sensitive data entered into large language models (LLMs).
AI introduces new execution paths, expands access to enterprise resources, and creates opportunities for unauthorized actions to occur at machine speed. An AI agent operating with excessive permissions can modify production files, invoke privileged processes, or interact with systems far beyond its intended scope.
Traditional security controls weren't necessarily designed for applications that can independently determine and execute a sequence of actions.
And while enterprise AI agreements, governance frameworks, and acceptable-use policies establish important safeguards, they don't automatically enforce what AI applications can execute, which resources agents can access, or where sensitive information can flow.
The next challenge in enterprise AI security is moving from governing AI usage to enforcing AI behavior.
Five critical controls can help organizations close that gap.
1. Eliminate shadow AI and regain visibility into enterprise data flows
The exposure: Unmanaged AI services create blind spots in data governance and increase the risk of sensitive information leaving approved environments.
Shadow AI is an extension of a familiar enterprise security challenge: employees adopting technology faster than IT and security teams can evaluate, approve, and govern it.
But the implications are significant.
Proprietary source code, regulated information, customer records, intellectual property, and confidential business strategies can be submitted to external AI services without appropriate contractual safeguards, retention policies, or administrative oversight.
This creates challenges for data governance, regulatory compliance, and incident response.
Even organizations with approved enterprise AI platforms may have limited visibility into the use of unauthorized alternatives.
The security imperative
Establish a controlled ecosystem of approved AI services and enforce it at the network and application layers.
Enterprise security teams should evaluate AI providers against contractual data protection requirements, retention practices, model-training policies, and organizational risk tolerance.
Web Content Control can then be used to restrict access to unauthorized AI services while enabling approved platforms.
The objective is to translate an AI acceptable-use policy into enforceable technical restrictions.
Security outcome: Reduced exposure to unapproved AI services, stronger data governance, and greater visibility into the organization's AI footprint.
Read more: Shadow AI: The risks it poses to your environment
2. Prevent unauthorized AI execution across enterprise endpoints
The exposure: AI tools and locally deployed models introduce execution capabilities that may operate outside established software governance controls.
AI adoption is no longer limited to browser-based services.
Local LLMs, coding assistants, agent frameworks, and AI-enabled development tools can execute directly on enterprise endpoints.
Depending on their capabilities and configurations, these tools may read local data, execute scripts, spawn processes, install dependencies, or establish external connections.
This introduces an important distinction: Controlling access to an AI website does not control the execution of AI software.
It also expands the challenge of defending against malicious code, including malware developed or modified using AI-assisted techniques.
For security teams relying primarily on detection-based approaches, the growing variety of executable tools and scripts increases the burden of identifying potentially harmful activity.
The security imperative
Enforce deny-by-default application execution policies across enterprise endpoints.
Application Allowlisting enables security teams to define which applications, executables, and scripts are permitted to run, preventing unauthorized AI tools and other unapproved software from executing.
Rather than relying exclusively on identifying malicious behavior after execution begins, organizations can prevent software that lacks authorization from running in the first place.
This establishes a consistent execution policy regardless of whether software is AI-enabled, traditionally developed, or malicious.
Security outcome: Reduced endpoint attack surface, stronger software governance, and prevention of unauthorized execution.
Read more: How to stop AI agents from taking unintended actions
3. Enforce least privilege over AI agents and their downstream actions
The exposure: Authorized AI agents can introduce unauthorized behavior through excessive permissions, delegated execution, and interactions with other applications.
This may be the most consequential security challenge associated with agentic AI.
Traditional access controls typically focus on users, applications, and resources. AI agents introduce another dimension: software that can make decisions about which actions to perform and which tools to invoke.
An approved AI agent may initiate PowerShell commands, modify source code, interact with databases, manipulate spreadsheets, or launch additional applications.
The resulting activity may span multiple processes and execution contexts.
Consider an AI coding agent authorized to modify a specific repository. If that agent invokes Python or PowerShell, do its original restrictions remain in effect? Or can the downstream process access resources outside the agent's authorized scope?
Without appropriate enforcement, delegated execution can undermine the boundaries established around the original application.
Prompt injection, flawed reasoning, or unintended task execution can further amplify the consequences of excessive permissions.
The security imperative
Apply least-privilege controls directly to AI agent behavior, including the applications, files, processes, and network destinations an agent can access.
ThreatLocker Zero Trust AI Control (ZTAIC) enables organizations to define and enforce what approved AI agents can launch, access, connect to, and change.
Critically, these restrictions can persist through downstream processes and applications invoked by the agent, including PowerShell, Python, and Microsoft Office.
This prevents an agent from gaining broader permissions simply by executing another application.
For enterprise security architects, the distinction is fundamental: AI authorization must extend beyond approving the agent itself to controlling its entire chain of execution.
Security outcome: Enforced least privilege for AI agents, reduced risk of unauthorized system changes, and containment of AI-driven activity within approved boundaries.
Read more: The principle of least privilege for AI agents and Excessive agency and least agency: What they mean for your AI tools
4. Eliminate unmanaged-device access to enterprise AI platforms
The exposure: Compromised credentials and unmanaged endpoints can provide access to sensitive AI conversations, connected resources, and organizational intelligence.
Enterprise AI platforms are rapidly becoming repositories of sensitive business information.
Beyond the documents explicitly uploaded, AI conversation histories may contain strategic planning discussions, financial analyses, proprietary code, legal considerations, customer intelligence, and internal operational details.
As organizations integrate AI with additional enterprise systems, the potential impact of account compromise increases.
Traditional authentication mechanisms alone may be insufficient to protect these environments.
An attacker who acquires valid credentials through phishing or other means may attempt to access enterprise AI services from an unmanaged endpoint.
Likewise, employees accessing corporate AI accounts from personal devices can introduce additional risks involving device security, data handling, and organizational visibility.
The security imperative
Extend Zero Trust access principles to enterprise AI platforms.
Require access from authorized, company-managed devices and enforce identity and device trust as conditions of access.
ThreatLocker Zero Trust Cloud Access (ZTCA) enforces device-bound access to supported cloud applications, restricting authentication and access to approved endpoints.
Combined with identity controls and conditional access policies, this provides an additional security boundary beyond credentials alone.
The objective is to prevent valid credentials from automatically translating into authorized access.
Security outcome: Reduced exposure to credential-based attacks, stronger control over AI access, and protection of sensitive enterprise AI environments.
Read more: Device identity vs. user identity: Why security teams need both
5. Enforce corporate AI identities and prevent personal-account data leakage
The exposure: Approved AI platforms can still create unmanaged data channels when employees authenticate using personal accounts.
Enterprise AI procurement often involves significant security due diligence.
Organizations negotiate data protection agreements, evaluate privacy safeguards, configure administrative policies, and establish contractual requirements governing the use of enterprise information.
But those protections may apply only within the organization's managed tenant or account.
If employees can access the same AI service using personal credentials, they may transfer sensitive business information outside the enterprise's approved security framework.
This creates a governance gap that conventional domain-based web filtering may not address.
The destination is approved. The identity and account context are not.
The security imperative
Restrict approved AI services to corporate-managed accounts.
Where supported, organizations can use enterprise authentication restrictions, tenant controls, and HTTP header-based enforcement to prevent personal account authentication.
These measures help ensure that AI activity remains within environments governed by enterprise security policies, contractual protections, and administrative oversight.
For CISOs, the requirement is straightforward: Approval of an AI service must not be confused with approval of every account capable of accessing it.
Security outcome: Reduced risk of sensitive information entering unmanaged AI accounts, stronger identity governance, and improved enforcement of enterprise data protection policies.
Read more: How Zero Trust changes when the “user” is an AI agent
Enterprise AI security requires enforceable boundaries
AI governance frameworks and security policies are necessary, but they are not sufficient on their own.
Organizations must be able to enforce their security decisions across the full AI lifecycle—from which services employees can access to which actions an autonomous agent can execute.
These five controls address distinct layers of enterprise AI risk:
The strategic priority is to establish an architecture in which AI operates within explicitly defined boundaries, regardless of the application, user, device, or execution path involved.
This requires moving beyond visibility and detection toward prevention and policy enforcement.
AI can accelerate business operations. It should not be allowed to expand its own authority. Enterprise security teams must retain control over what runs, what it can access, and what it is permitted to do.
Operationalize Zero Trust across your AI environment
ThreatLocker enables organizations to extend Zero Trust security principles to AI adoption through Application Allowlisting, Web Content Control, Zero Trust AI Control, and Zero Trust Cloud Access.
Together, these capabilities help security teams restrict unauthorized AI usage, control application execution, enforce boundaries around AI agents, and secure access to enterprise AI services.
Book a custom demo to see how ThreatLocker can help enforce Zero Trust across your AI environment.
The future is yours to control.
