Generative AI and Data Leakage Risks

Other → Technological Risk
| 2025-11-05 19:28:05

Introduction Slide – Generative AI and Data Leakage Risks

Understanding the New Frontier of Data Security

Overview

Generative AI tools are transforming business operations but introduce new data leakage risks due to their reliance on vast datasets and user inputs.
Understanding these risks is critical for organizations to safeguard sensitive information, comply with regulations, and maintain trust.
This presentation will cover key risk drivers, real-world trends, analytical frameworks, and mitigation strategies for generative AI data leakage.
Key insights include the rise of shadow AI, increased breach costs, and the importance of AI-specific DLP and governance controls.

Key Discussion Points – Generative AI and Data Leakage Risks

Drivers and Implications of Generative AI Data Leakage

Main Points

Generative AI tools often process sensitive data, increasing exposure risks through unintended sharing, insecure model training, and adversarial attacks.
Shadow AI use—employees leveraging unapproved tools—creates blind spots and exposes organizations to data breaches and compliance violations.
Recent studies show that 67% of employees share internal data with generative AI without authorization, and 40% of uploaded files contain PII or PCI data.
Implications include higher breach costs (up to 28% more than conventional breaches), regulatory penalties, and reputational damage.

Graphical Analysis – Generative AI Data Flow and Exposure

Visualizing Data Exposure Pathways in Generative AI

Context and Interpretation

This sequence diagram illustrates how data moves from users to generative AI platforms, highlighting potential exposure points.
Trends show that copy/paste actions and unmanaged accounts are the primary vectors for data leakage.
Risk considerations include lack of visibility, unsecured data handling, and the challenge of monitoring personal versus corporate accounts.
Key insights: Most leaks occur via unmanaged endpoints, and traditional security controls often miss these threats.

Figure: Data Flow and Exposure in Generative AI

sequenceDiagram
participant User as "Employee"
participant AI as "GenAI Tool"
participant ThirdParty as "Third-Party Vendor"
User->>AI: Paste Sensitive Data
AI->>ThirdParty: Process Data
alt Data Shared
ThirdParty->>User: Return Output
else Data Leaked
ThirdParty->>External: Expose Data
end
Note over AI: Exposure Risk

Graphical Analysis – Generative AI Data Leakage Trends

Context and Interpretation

This bar chart visualizes the rise in GenAI-related data security incidents from 2023 to 2025, showing a sharp increase in DLP incidents.
Trends indicate that GenAI-related incidents now account for 14% of all data security incidents in SaaS environments.
Risk considerations include the proliferation of unsanctioned tools and the lack of clear AI usage policies.
Key insights: Organizations face a growing challenge in monitoring and controlling GenAI usage, with incident rates more than doubling in early 2025.

Figure: GenAI-Related DLP Incidents (2023–2025)

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Analytical Summary & Table – Generative AI Risk Factors

Breakdown of Key Generative AI Data Leakage Risk Factors

Key Discussion Points

Major risk factors include shadow AI use, unsecured model training, unintended data sharing, and adversarial threats.
Contextual interpretation: Each factor contributes to increased breach likelihood and regulatory exposure.
Significance: Organizations must prioritize AI-specific DLP, private AI deployments, and granular usage policies.
Assumptions: Data reflects current enterprise environments; limitations include evolving regulatory landscapes.

Illustrative Data Table

Key risk factors and their impact on data leakage.

Risk Factor	Impact Level	Prevalence	Mitigation Strategy
Shadow AI Use	High	66 apps/org	Policy enforcement
Unsecured Model Training	High	40% files leak PII	Data anonymization
Unintended Data Sharing	Medium	67% employees share data	AI-specific DLP
Adversarial Threats	Medium	Emerging	Regular audits

Analytical Explanation & Formula – Generative AI Risk Modeling

Quantitative Framework for Generative AI Data Leakage Risk

Concept Overview

The risk of data leakage in generative AI can be modeled as a function of exposure, vulnerability, and threat likelihood.
This formula helps organizations quantify and prioritize risks for mitigation planning.
Key parameters include data sensitivity, model security, and user behavior.
Practical implications: The model can guide resource allocation and policy development for AI risk management.

General Formula Representation

The general relationship for this analysis can be expressed as:

$$ R = E \times V \times T $$

Where:

$ R $ = Risk of data leakage.
$ E $ = Exposure (data sensitivity).
$ V $ = Vulnerability (model security).
$ T $ = Threat likelihood (user behavior).

This form can represent statistical models, optimization functions, or analytical relationships across different domains such as risk modeling, forecasting, or simulation.

Code Example: Generative AI and Data Leakage Risks

Code Description

This Python code demonstrates a basic approach to detecting sensitive data in user inputs before sending them to a generative AI model, helping to prevent data leakage.

import re

def detect_sensitive_data(text):
    # Define patterns for PII
    patterns = [
        r'\b\d{3}-\d{2}-\d{4}\b',  # SSN
        r'\b\d{16}\b',              # Credit card
        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'  # Email
    ]
    for pattern in patterns:
        if re.search(pattern, text):
            return True
    return False

# Example usage
user_input = "My SSN is 123-45-6789"
if detect_sensitive_data(user_input):
    print("Sensitive data detected!")
else:
    print("No sensitive data found.")

Conclusion

Summarize and conclude.

Generative AI introduces significant data leakage risks, including shadow AI use, unsecured model training, and unintended data sharing.
Organizations must adopt AI-specific DLP, private AI deployments, and clear usage policies to mitigate these risks.
Key notes: Regular audits, employee training, and regulatory compliance are essential for safe AI adoption.
Recommendations: Stay informed about emerging threats and leverage quantitative risk models to guide mitigation strategies.

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Explore the details behind: Generative AI and Data Leakage Risks

Generative AI and Data Leakage Risks

Introduction Slide – Generative AI and Data Leakage Risks

Overview

Key Discussion Points – Generative AI and Data Leakage Risks

Main Points

Graphical Analysis – Generative AI Data Flow and Exposure

Context and Interpretation

Graphical Analysis – Generative AI Data Leakage Trends

Context and Interpretation

Analytical Summary & Table – Generative AI Risk Factors

Key Discussion Points

Illustrative Data Table

Analytical Explanation & Formula – Generative AI Risk Modeling

Concept Overview

General Formula Representation

Code Example: Generative AI and Data Leakage Risks

Code Description

Conclusion