Artificial Intelligence (AI) or LLMs are here to stay. They have revolutionized the speed of execution of many business teams. With the help of AI coding assistants, developers can now ship new features, bug fixes, and entire products at record speeds. Pitch decks, presentations, and proposals can be generated within minutes. While AI exceeds efficiency expectations, uncontrolled adoption and tool sprawl can dramatically increase the risk of data loss and cyber-attacks. 

You have probably read about incidents involving users pasting sensitive information and source code into AI chatbots and tried implementing a data loss prevention (DLP) program to fix it. Similarly, organizations using cloud-based AI tools implement cloud access security brokers (CASB) for access control. While DLP and CASB are important tools in securing the organization, organizations often overlook the AI tools that are installed directly on the endpoints. This article explores how this overlooked share of shadow AI can expose the organization to cyber threats and how you can mitigate the risks. 

What is Shadow AI? 

Shadow AI is the younger, meaner sister of Shadow IT. They are the artificial intelligence tools installed outside the approval of IT teams. They operate outside the scope of your organization’s governance and lifecycle management policies. Adding to the risks involved with shadow IT tools, shadow AI tools bring its own set of IT risks. 

The risk caused by unauthorized use of AI tools is far more potent than those risks caused by apps and tools that do not have AI capabilities. The risk is higher because Shadow AI tools don’t just access and store sensitive information. They ingest proprietary data, generate unverified outputs, and use them for unsanctioned activities. 

The Real Risk of Shadow AI 

The real risk of allowing unauthorized AI tools to gain excessive permissions is the speed with which it operates. A human moves one step at a time; AI agents can launch multiple operations through child processes and coordinate with unparalleled efficiency. If one malicious agent is accidentally deployed by the users in a bid to improve their output, the entire network can be compromised. 

Most users interact with artificial intelligence tools through web browsers and mobile applications. While this is true when accounting for domestic and personal use of AI chatbots for generating Ghibli images and demand generated by social media trends, significant use of AI tools in a corporate setup is through AI tools and agents that are directly installed and run on employee endpoints.  

These tools have direct access to your network through the endpoint. What security measures do you have against these tools that reside within the network walls? 

AI Agents and desktop clients of AI tools have access to local data, index files, memory, and code repositories directly. Unmanaged AI agents and MCP servers can run scripts, access databases, and take actions autonomously - without any human prompting specific actions explicitly. 

If you don’t have security frameworks and systems in place to prevent users from installing unapproved AI tools on their machines, you must learn about the risks of Shadow AI and the security measures available to prevent users from installing unauthorized tools. 

Data Exposure & Leakage 

• Sensitive data ingestion: Employees may feed proprietary code, customer PII, financial data, or trade secrets into unsanctioned AI tools that retain or train themselves using the user’s inputs. 
• Uncontrolled data residency: Data may be processed or stored in jurisdictions or by vendors that violate compliance requirements (GDPR, HIPAA, etc.). 
• Third-party data retention: Unknown or unclear data retention/deletion policies of the AI vendor. 

Expanded Attack Surface 

• Unmanaged API keys and credentials: Shadow agents often need credentials to connect to systems (email, calendars, databases), creating unmonitored access points. 
• Unvetted integrations: Agents connecting to internal tools (via plugins, MCP-style connectors, browser automation) that bypass normal access review. 
• Persistent background processes: Agents that run continuously or on schedules can become long-lived footholds if compromised. 
• Permission sprawl and inherited privileges: Locally run AI tools and agents can inherit the user’s privileges, pass them to the child processes they spawn and rapidly expand the attack surface. 

Prompt Injection & Manipulation 

• Indirect prompt injection: Agents that browse the web or read documents/emails can be manipulated by malicious content embedded in those sources, leading to data exfiltration or unauthorized actions. 
• Tool/function abuse: If an agent has write-access tools (sending emails, modifying files, executing code), a successful injection can cascade into real-world harm. 

Identity & Access Management Gaps 

• Excessive permissions: Agents often get broad access for convenience, violating least-privilege principles. 
• No audit trail: Actions taken by an agent may not be logged or attributed to a specific human, complicating incident response and accountability. 
• Orphaned agents: Agents set up by an employee who later leaves the company, continuing to run with valid credentials. 

Supply Chain Risks

• Unvetted vendors: Shadow AI tools may not undergo the security review applied to sanctioned software (SOC 2, penetration testing, etc.). 
• Malicious or low-quality models/plugins: Downloaded or connected models/agents could contain backdoors or vulnerabilities. 
• AI assisted software development: For companies developing software solutions, unvetted AI coding assistants can generate insecure code blocks, recommend dependencies with vulnerabilities, and include nonexistent runtimes and obscure packages. 

Operational & Reliability Risks 

• Unpredictable agent behavior: Autonomous agents making decisions or taking actions (e.g., sending communications, modifying records) without human review can cause errors at scale. 
• Model drift or hallucination: Decisions based on inaccurate AI output can stay undetected if no one is formally monitoring the tool. 
• Duplication and inconsistency: Multiple unofficial agents solving the same problem differently, creating inconsistent or conflicting outputs across the organization. 

Detection & Visibility Challenges 

• Lack of inventory: Security teams can't control without adequate visibility into the inventory of shadow agents. 
• Evasion of DLP/monitoring tools: Traffic to AI APIs may not be flagged by existing data-loss-prevention or network monitoring systems tuned for known SaaS apps. 

Compliance & Legal Exposure 

• Regulatory violations: Unsanctioned processing of regulated data (health records, financial data) without proper control. 
• IP and licensing issues: Feeding proprietary or third-party licensed content into external models without authorization. 
• Contractual breaches: Violating client or vendor data-handling agreements by routing their data through unapproved tools. 

Most of these risks are caused by unsanctioned and uncontrolled use of AI tools. Shadow AI can be curbed using a mixed approach of limiting what each employee can do on their endpoints and providing a pre-approved stack of AI tools for users, allowing them to improve their efficiency while ensuring data security and compliance.  

To control what employees can do at their endpoints, you must scope their permissions to the specific tasks their job requires them to do. Even today, many organizations grant local admin rights to employees or have systems that grant and revoke broad permissions by directly sharing local admin account credentials.  

Eliminating local administrator privileges from user accounts and granting permissions to run specific applications with elevated privileges allows them to complete their tasks seamlessly and securely. This also prevents users from installing unapproved AI tools and agents that require admin rights. 

To control what AI tools are sanctioned for different teams in an organization, an allowlist-based application control system can be implemented. An allowlist will prevent users from running any other app or tool apart from the pre-approved list. By combining these two solutions into a single control plane, Securden Endpoint Privilege Manager helps mitigate the risks of shadow AI at the endpoint level.

Continuous AI Application Discovery 

Gain instant visibility into every AI tool being run by users on their Windows and macOS endpoints. Discover installed AI apps, portable executables, scripts, and unknown binaries, along with metadata like file hash, publisher, and file path. 

Just-in-Time Privilege Elevation 

AI agents and tools do not need local admin rights to function. Eliminate local admin rights across endpoints and servers. Elevate individual applications on a just-in-time basis through granular policies and approval workflows. 

Allow Trusted AI Tools 

Control what AI agents and tools users can run on their endpoint with granular application control. Allowlist approved AI tools and applications. Everything else is automatically blocked. 

Complete Activity Tracking 

Monitor which applications and AI tools are run on endpoints and continuously feed data to SIEM tools. Prove compliance with regulatory compliance using drilled down reports for GDPR, HIPAA, PCI-DSS, NERC-CIP, and Essential Eight.

These controls matter because they help you control the shadow AI tools that run on endpoints connected to your network. Local admin rights can amplify the blast radius of minor security incidents; AI agents and tools can amplify the intensity of the blast. The safest course is to prevent the agents from gaining any administrative access unless it is absolutely required. Even then, you must time-box the administrative access and enforce strict auditing during elevated access. 

Book a demo and get a free Proof of Concept for Endpoint Privilege Manager.