Artificial intelligence tools have moved from novelty to necessity at a speed that has outpaced most organizations' governance frameworks. Employees are using ChatGPT, Copilot, Claude, Gemini, and dozens of AI-powered productivity tools — often without organizational awareness, approval, or security review.
This is the new shadow IT problem. And it is significantly more consequential than the shadow cloud storage or unapproved SaaS tools of the previous decade. AI systems process, retain, and act on data in ways that are often opaque — and the risks to your organization are real, growing, and largely unaddressed.
This guide covers the key AI security risks organizations face today, with a particular focus on unauthorized and agentic AI usage — AI systems that can take autonomous actions on behalf of users.
What Is Agentic AI — and Why Does It Matter?
Traditional AI tools respond to prompts: you ask a question, you get an answer. Agentic AI goes further — these systems can execute multi-step tasks autonomously, using tools like web browsers, email clients, file systems, code interpreters, and APIs to take actions in the real world.
Examples of agentic AI capabilities in enterprise contexts include:
- Autonomously drafting and sending emails on behalf of a user
- Reading, summarizing, and responding to documents and data stored in cloud drives
- Executing code, running queries, or interacting with business systems via API
- Browsing the web, gathering intelligence, and compiling reports without human intervention
- Orchestrating multi-step workflows across multiple tools and data sources
When employees deploy these capabilities using personal accounts, unapproved tools, or unauthorized integrations, your organization's sensitive data — client records, financial information, proprietary processes, legal communications — may be processed by systems you have no visibility into, under terms of service your legal and compliance teams have never reviewed.
The OWASP LLM Top 10: Key Risks for Enterprise Organizations
The Open Worldwide Application Security Project (OWASP) publishes a Top 10 list of critical security risks for Large Language Model (LLM) applications. The following are most directly relevant to enterprise organizations using or deploying AI:
LLM01 — Prompt Injection
Malicious content embedded in data processed by an AI system can override its instructions and cause it to take unintended actions. In agentic contexts — where AI reads emails, documents, or web content before taking action — prompt injection can be used to exfiltrate data, bypass controls, or cause unauthorized system actions. This is the AI equivalent of SQL injection.
LLM02 — Sensitive Information Disclosure
AI models may inadvertently reveal sensitive information from their training data, context window, or prior conversations. Enterprise AI tools processing confidential data can leak that data to other users or external parties under certain conditions.
LLM06 — Excessive Agency
Agentic AI systems granted broad permissions — access to email, files, APIs, or execution environments — can cause significant unintended harm if manipulated or misdirected. Limiting the permissions and scope of AI agents is a critical security control.
LLM08 — Vector and Embedding Weaknesses
Organizations using AI with retrieval-augmented generation (RAG) — where the AI searches an internal knowledge base before responding — may expose sensitive documents to unauthorized users through the AI interface, bypassing traditional access controls.
LLM09 — Misinformation
AI systems can confidently produce incorrect information, which — when used for security decisions, compliance documentation, or operational guidance — can introduce significant organizational risk.
Shadow AI: The Unauthorized Use Problem
Shadow AI refers to the use of AI tools and services by employees without organizational knowledge, approval, or governance. Research from multiple industry sources indicates that a significant majority of employees in knowledge-worker environments are using AI tools — and that a substantial portion of that usage occurs without IT or security awareness.
The risks of shadow AI include:
- Data exfiltration: Employees pasting sensitive client data, financial records, source code, or legal documents into external AI tools whose data retention policies are unknown or unfavorable
- Regulatory exposure: In regulated industries (healthcare, financial services, legal), processing personal or protected data in unapproved AI tools may violate HIPAA, GLBA, or attorney-client privilege
- Credential exposure: Employees using AI coding assistants may inadvertently expose API keys, passwords, or configuration secrets
- Intellectual property risk: Proprietary processes, product designs, or competitive intelligence submitted to AI tools may be used to train future models
- Uncontrolled agentic actions: AI tools granted access to email or calendar accounts may take actions — send messages, create meetings, modify files — without user review
Building an AI Governance Framework: Where to Start
Organizations do not need to prohibit AI to manage these risks. The goal is governed, secure AI usage — not abstinence. The following steps provide a practical starting point:
- Conduct an AI inventory: Identify what AI tools are currently in use across your organization — both approved and unapproved
- Assess data sensitivity: Map which data types are most likely to be processed by AI tools and evaluate the risk of unauthorized disclosure
- Establish an Acceptable Use Policy for AI: Define what AI tools are approved, what data may be processed, and what employee responsibilities are
- Implement technical controls: Use web proxy or DLP tools to detect and restrict uploads of sensitive data to unauthorized AI platforms
- Evaluate agentic permissions: For approved AI tools with agentic capabilities, apply least-privilege — limit the tools, data, and systems the AI can access
- Review vendor terms of service: Understand how your approved AI vendors handle data retention, training, and breach notification
- Train employees: Awareness training on AI-specific risks is now a critical component of your security awareness program
- Monitor and audit: Establish ongoing monitoring for AI-related data movement and anomalous behavior
How TrilogySecurity Can Help
TrilogySecurity's AI Security Assessment evaluates your organization's exposure across the OWASP LLM Top 10 and MITRE ATLAS framework. Our assessment includes:
- Shadow AI discovery — identifying unapproved AI tool usage across your environment
- AI Governance gap analysis — comparing your current policies against emerging best practices
- Agentic AI risk review — evaluating the permissions, scope, and controls around any AI agents or integrations in your environment
- Technical testing — where AI systems are deployed internally, we test for prompt injection, data exposure, and control bypass vulnerabilities
- Remediation roadmap — prioritized recommendations your team can act on immediately
As AI adoption accelerates across every industry, organizations that get ahead of governance now will be significantly better positioned than those who react after an incident.