AI Security
AI Data Leakage Risk Checker
Assess the risk of sensitive organizational data leaking through AI and LLM platforms.
This tool evaluates your data protection posture when using AI systems. By assessing data sensitivity levels, platform hosting models, masking controls, DLP deployment, and user training, it generates a comprehensive leakage risk score. Built for security teams and data officers who need to quantify AI-related data exposure before approving enterprise AI adoption.
Assessment Results
Complete the AI tool assessment to calculate leakage risk, confidence, evidence status, and approval recommendation.
Overall AI Data Leakage Risk
Not calculated
Risk Trend Over Time
Calculated planning view from this assessment, not historical monitoring data.AI Leakage Risk Distribution
Risk Heatmap (Likelihood vs Impact)
AI Leakage Risk by Domain
Top Leakage Drivers
Compliance Framework Coverage
Recent Critical Findings
Evidence Checklist
Remediation Summary
AI Tools Risk Register
Approval Workflow
Global Standards Alignment
AI Risk Summary
This summary is generated from the entered assessment data and should be reviewed by the responsible AI risk owner before final decision.
Management Reports
AI Security Disclaimer
This assessment is aligned with OWASP Top 10 for LLM and NIST AI RMF concepts but is simplified for rapid evaluation.
- A comprehensive AI security assessment requires deep analysis of data flows, model architecture, and access controls.
- Use this score to guide AI governance discussions and budget prioritization.
- Coordinate AI security architecture changes with your CISO or AI governance team.
Search topics covered
- AI data leakage risk assessment
- LLM sensitive data exposure
- AI platform security evaluation
- data masking for AI systems
- DLP controls for generative AI
- prompt data protection
- AI governance risk scoring
- enterprise AI security posture
- OWASP LLM data leakage
- NIST AI risk management framework
- AI data loss prevention
- sensitive data in AI prompts
- generative AI security controls
- AI privacy risk calculator
- LLM deployment security checklist
How to use this calculator
Classify Data Sensitivity
Start by selecting the highest sensitivity level of data that flows through your AI systems. If any restricted or regulated data (PII, PHI, PCI) passes through prompts, select "Restricted" even if most usage involves lower-sensitivity data. This establishes the baseline exposure and drives the risk weighting across all other controls.
Select Platform Type
Identify where your AI processing occurs. Public APIs send data to third-party servers, creating the highest exposure. Private hosted solutions (like Azure OpenAI) offer more control. On-premises or air-gapped deployments provide the lowest platform risk since data never leaves your environment.
Evaluate Technical Controls
Assess whether data masking, DLP, and prompt logging are actively deployed. Each control reduces leakage risk independently. Data masking removes sensitive content before it reaches AI. DLP monitors and blocks unauthorized data flows. Prompt logging creates audit trails for investigation and compliance.
Assess Human Factors
User training is a critical control layer. Untrained users are more likely to paste sensitive data into prompts, share confidential documents with AI tools, or bypass security policies. Mark training as completed only if AI-specific security awareness training has been delivered to all users with AI access.
Review Risk Score
The leakage risk score combines data sensitivity, platform exposure, control coverage, and human factors into a weighted index. Higher scores indicate greater leakage risk. Use the control breakdown table to identify which specific areas need remediation. Focus budget on the highest-impact gaps first.
Export And Share
Export the assessment to PDF, CSV, or Excel to share with your AI governance committee or CISO. The report includes the risk breakdown, control status table, and disclaimer. Reassess whenever you change AI platforms, expand usage, or modify security controls to keep risk data current.
Advantages of this calculator
Rapid AI Risk Visibility
Most organizations adopt AI tools faster than security teams can assess them. This calculator provides an instant risk snapshot that helps security leaders understand exposure before formal audits are completed. Quick visibility enables faster decisions about which AI deployments need immediate attention.
Multi-Factor Risk Model
Unlike simple checklists, this tool combines data sensitivity, platform architecture, technical controls, and human factors into a single weighted score. This multi-dimensional approach reflects how real-world data leakage occurs through the interaction of multiple risk vectors simultaneously.
Control Gap Identification
The breakdown table clearly shows which controls are missing or weak. Security teams can prioritize remediation by addressing the highest-scoring gaps first. This targeted approach is more efficient than applying generic security measures across the board.
Governance Communication
The risk score and tier system translate complex AI security concepts into business language. This makes it easier to communicate AI risks to executives, board members, and compliance officers who may not understand the technical details of data masking or DLP deployment.
Standards Alignment
The assessment framework aligns with OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework, and ISO 42001 concepts. This ensures your internal risk language matches the frameworks used by auditors, regulators, and cyber insurance carriers.
Repeatable Assessment
The structured input format makes it easy to reassess risk regularly as AI usage expands or controls improve. Weekly or monthly reassessments create a trend line showing whether your AI security posture is improving or declining over time.
Governing bodies & standards
- OWASP: Top 10 for LLM Applications
- NIST: AI Risk Management Framework
- ISO/IEC 42001: AI Management System
- MITRE ATLAS: Adversarial Threat Landscape for AI
Related Security Calculators
Q&A
What is AI data leakage?
AI data leakage occurs when sensitive information (PII, trade secrets, financial data) is unintentionally exposed through AI prompts, training data, or model outputs to unauthorized parties or third-party platforms.
Is using public AI APIs always risky?
Not always, but public APIs process data on external servers. If sensitive data is included in prompts, it may be stored, logged, or used for model training by the provider unless contractual safeguards exist.
What is data masking for AI?
Data masking automatically redacts or replaces sensitive information (names, SSNs, account numbers) in prompts before they reach AI systems, reducing exposure without blocking AI usage entirely.
How does DLP help with AI security?
Data Loss Prevention tools monitor data flows to AI platforms and can block or alert when sensitive data patterns are detected in prompts, file uploads, or API calls to AI services.
Why is prompt logging important?
Prompt logging creates audit trails showing what data was sent to AI systems, by whom, and when. This is essential for incident investigation, compliance reporting, and identifying policy violations.
Can user training reduce AI data leakage?
Yes. Trained users are less likely to paste sensitive data into prompts or bypass security controls. Training should cover acceptable use policies, data classification, and safe prompt practices.
What is the OWASP Top 10 for LLM?
A security framework identifying the ten most critical vulnerabilities in Large Language Model applications, including prompt injection, data leakage, and insecure output handling.
How often should I reassess AI data leakage risk?
Reassess whenever you adopt new AI tools, change platforms, expand user access, or modify security controls. Quarterly reviews are recommended as a minimum baseline.
Does this tool replace a formal DPIA?
No. This is a rapid assessment tool. A formal Data Protection Impact Assessment (DPIA) involves deeper analysis of data flows, legal bases, and stakeholder consultation as required by GDPR and similar regulations.
What is a safe AI data leakage score?
Scores below 30 indicate low leakage risk with strong controls. Scores above 60 suggest significant gaps that need immediate remediation before expanding AI usage.