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AI Usage Risk Assessment

Quantify the security, privacy, and compliance risks of deploying Generative AI and LLMs within your corporate environment.

As organizations rush to adopt AI (like ChatGPT, Copilot, or custom LLMs), they expose themselves to new threat vectors including data leakage, intellectual property loss, and shadow IT. This assessment tool evaluates your planned AI use case against four critical domains: Data Sensitivity, Model Type, Access Control, and Governance. It generates a standardized risk score to help IT and Legal teams approve, modify, or block AI deployments safely.

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For Internal Planning Only

This tool assesses policy risks. Conduct technical prompt injection testing, data privacy audits, and legal compliance reviews with AI security specialists before deploying LLMs in production.

Enterprise AI Usage Risk Assessment
AI Use Case Registration
AI Usage Questionnaire
Evidence Checklist

All scores are calculated from entered answers. Unknown and missing evidence increase risk and reduce confidence.

AI Usage Risk Scorecard

Overall AI Usage Risk Score-

Complete the assessment to calculate AI usage risk, evidence confidence, and approval recommendation.

Overall AI Usage Risk Score

-/100

Not calculated

Risk Trend Over Time

Planning view derived from this assessment, not live monitoring data.

AI Risk by Severity

Risk Heatmap (Likelihood vs Impact)

Risk Scores by Domain

Top Risk Drivers

Compliance Framework Coverage

Recent Critical Findings

Risk Distribution

Remediation Summary

AI Use Case Risk Register

Approval Workflow

Global Standards Alignment

AI-Generated Executive Summary

This summary is generated from the entered assessment data and should be reviewed by the responsible AI governance owner before final decision.

Reports Section

Compliance Disclaimer

The AI Usage Risk Assessment is for internal scoping and policy development.

  • Does not replace a formal Data Protection Impact Assessment (DPIA).
  • Does not evaluate the accuracy or 'hallucination' risk of the model output.
  • Consult legal counsel regarding copyright and IP ownership of AI-generated content.

Search topics covered

  • AI security risk assessment template
  • Generative AI corporate policy checklist
  • risks of using ChatGPT in business
  • enterprise LLM deployment security
  • data privacy and artificial intelligence
  • EU AI act compliance checker
  • shadow AI detection and mitigation
  • protecting intellectual property from LLMs
  • AI governance framework for IT
  • DLP rules for AI chatbots
  • public vs enterprise AI SaaS security
  • algorithmic impact assessment (AIA)
  • machine learning data poisoning risks
  • AI acceptable use policy (AUP)
  • role-based access control for GenAI tools

How to use this calculator

Assess Data Sensitivity

The biggest risk in AI usage is data leakage. Determine what your employees intend to feed into the prompt. If they are writing marketing copy using public info, the risk is Low. If developers are pasting proprietary source code, or HR is analyzing employee performance reviews (PII), the risk is High. Never allow High-risk data into unmanaged public models.

Determine Model Deployment

Where is the brain of the AI located? "Public Consumer SaaS" (like the free version of ChatGPT) often uses user inputs to train future versions of the model, meaning your data could be exposed to competitors. "Enterprise SaaS" (like Copilot for M365 or ChatGPT Enterprise) contractually guarantees your data is isolated and not used for training, significantly lowering the risk.

Evaluate Access Controls

"Shadow AI" occurs when employees create their own accounts using personal emails to bypass IT. This is High risk. The standard approach is to provision access via Single Sign-On (SSO) so IT can revoke access if an employee leaves. The most mature approach integrates Data Loss Prevention (DLP) tools to actively block sensitive data from being pasted into the prompt.

Review Governance Maturity

Technology alone cannot secure AI. Do you have an updated Acceptable Use Policy (AUP) that specifically mentions AI? Have employees been trained on the dangers of "hallucinations" (AI making up facts)? A mature organization has an AI Council (Legal, IT, HR) that reviews all AI requests before deployment.

Analyze the Risk Tier

The calculator combines these factors to assign a Risk Tier. A "Critical" score usually means a mix of highly sensitive data going into a public model with no governance. This requires immediate intervention. A "Moderate" score means you are using enterprise tools correctly but still need to maintain vigilance.

Export for the AI Council

Before approving a new AI tool for a specific department, have the project lead fill out this calculator. Export the PDF and present it to the IT/Security team. It forces the requester to think about data sensitivity and gives security a standardized framework for saying "Yes, but only in the Enterprise tenant."

Advantages of this calculator

Bridges the IT/Business Gap

Business units just want the productivity gains of AI; IT sees the massive data leakage risks. This tool provides a common, objective language. It helps the business understand *why* they must use the paid Enterprise version instead of the free public version.

Highlights Shadow AI Risks

By explicitly calling out "Unmanaged accounts," the tool highlights the danger of Shadow AI. It gives CISOs the justification they need to block consumer AI sites at the firewall and force traffic through approved, monitored corporate channels.

Actionable Mitigation

It doesn't just say "No" to AI. It provides a roadmap. If a use case scores "Critical," the tool shows that upgrading to "Enterprise SaaS" and updating the "Governance Policy" can bring the score down to an acceptable "Moderate" level, enabling innovation safely.

Simplifies Complex Frameworks

Frameworks like the NIST AI RMF are incredibly complex and hard for average managers to use. This calculator distills the core concepts of those frameworks (Data, Architecture, Access, Governance) into a quick, 4-question triage tool.

Prepares for Regulation

With regulations like the EU AI Act coming into force, organizations will be required to document their AI risk assessments. Using this tool establishes a baseline "paper trail" showing that the company took a measured, risk-based approach to AI adoption.

Governing bodies & standards

Related Governance Tools

Q&A

What is the biggest risk of using ChatGPT at work?

Data leakage. If you paste proprietary code or customer data into consumer AI models, that data may be used to train future versions, potentially exposing your secrets to competitors.

What is the difference between Public and Enterprise AI?

Enterprise AI (like Copilot for M365) includes contractual guarantees that your input data is not used for model training and remains within your secure, private tenant.

What is 'Shadow AI'?

Shadow AI is when employees use unsanctioned, unmanaged AI tools (like personal ChatGPT accounts) to do corporate work, bypassing IT security controls and logging.

What is an AI 'Hallucination'?

A hallucination is when an AI model confidently generates false or fabricated information. This is a massive risk if employees rely on AI output for legal or financial decisions without verifying it.

Can we use AI to write code?

Yes, but it should only be done using Enterprise tools. Pasting company source code into public models is a severe security violation that exposes intellectual property.

What is an Acceptable Use Policy (AUP)?

An AUP is a document employees sign that outlines what they can and cannot do with company technology. It must be updated to explicitly cover the rules for Generative AI usage.

How does DLP work with AI?

Data Loss Prevention (DLP) tools can monitor web traffic and block users from pasting specific types of sensitive data (like credit card numbers or keywords) into AI chat windows.

What is the EU AI Act?

It is a comprehensive legal framework that categorizes AI systems by risk. High-risk systems require strict compliance, transparency, and human oversight before they can be used in the EU.

Who owns the copyright to AI-generated content?

Currently, the US Copyright Office states that purely AI-generated content without significant human modification cannot be copyrighted. This poses a risk for marketing and legal teams.

What is 'Data Poisoning'?

Data poisoning is a cyber attack where malicious actors intentionally manipulate the training data of an AI model to cause it to make errors or create backdoors.

Do we need an 'AI Council'?

Yes, mature organizations form a cross-functional group (IT, Legal, Privacy, HR) to review and approve new AI tools to ensure they meet security and ethical standards.

Can AI be used for hiring?

Using AI for resume screening is highly regulated due to 'Algorithmic Bias' risks. AI can inadvertently discriminate against certain groups based on flawed training data, leading to lawsuits.

What is a self-hosted LLM?

A self-hosted Large Language Model (like Llama 3) runs entirely on your own servers or cloud infrastructure. It offers the highest level of privacy because no data ever leaves your control.

How does GDPR apply to AI?

If an AI model processes the personal data of EU citizens, it must comply with GDPR, including the 'Right to be Forgotten' (which is technically very difficult to do once a model is trained).