Data Security Challenges with Gen AI Applications 

‍Overview 
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Security and ethical risks associated with generative AI applications are now widely recognized. Organizations are implementing safeguards to prevent threats like prompt injections, sensitive information leaks, and hallucinations. While deploying these measures is important for real-time threat prevention, the data that powers Gen AI applications can still expose them to various threats. For example, an indirect injection attack hidden within the data causes the chatbot to launch a phishing attack. See an example of this attack below.

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Video 1: Healthcare chatbot launching phishing attacks due to an indirect injection attack in the data.

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Security Issues with Data

While data is essential for powering advanced Gen AI applications, it can also introduce new security challenges. Malicious users may introduce attacks into the data, causing systems that rely on this data to generate harmful outputs. Data Integrity problems like contradictory or incomplete information can cause Gen AI applications to hallucinate. And data that is not compliant with Industry regulations or Company guidelines could potentially lead to legal and compliance problems.

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Example #1: Sensitive Information Data Leakage

Data might contain sensitive information that, if not properly managed, could be inadvertently disclosed. Regulations such as GDPR and HIPPA mandate that companies closely monitor data privacy. This includes safeguarding Personally Identifiable Information (PII) such as names, addresses, or social security numbers, and Protected Health Information (PHI) like medical records or health-related information. Additionally, the presence of banned keywords—terms or phrases that are prohibited due to legal, ethical, or policy reasons—poses a significant risk.

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Example #2: Data Integrity Issues 

Data integrity directly impacts the reliability of a Gen AI system. Hallucinations often stem from wrong information, which can confuse the AI Model and lead to overemphasis on certain data points. Contradictory information can cause the system to produce inconsistent responses, while potentially biased information can result in skewed outputs that may perpetuate stereotypes or inaccuracies. Gaps in data prevent the system from accessing all necessary information, leading to incorrect conclusions. 

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Example #3: Data Moderation and Compliance Issues

Adhering to policy guidelines and regulatory standards is crucial for building safe and compliant generative ai solutions. Content that violates company policies or legal regulations can lead to serious repercussions and financial loss. The presence of offensive or inappropriate language within the data offends users and leads to brand damage. Implementing filters and monitoring systems helps in identifying and removing such content.

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Improve Data Security with Enkrypt AI

The figure below illustrates a simple data security solution using our AI security platform. By scanning folders containing all data, Enkrypt AI helps organizations:

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• Identify and Remove Vulnerabilities: Detects potential security risks like indirect injection points or sensitive data exposure.
• Ensure Data Integrity: Flags repeated, contradictory, or incomplete information for review.
• Maintain Compliance: Checks data against policy guidelines and regulatory standards to ensure adherence.
• Enhance Overall Security: Provides actionable insights to strengthen the knowledge base and, by extension, the application's reliability.
  
  

By integrating Enkrypt AI into the data preparation process, organizations can significantly reduce risks and improve the performance of their Gen AI applications. See video demo below.

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Video 2: Enkrypt AI Data Security Demo

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Conclusion

Data is transforming the Generative AI landscape by providing more accurate and context-aware interactions. However, the effectiveness of these systems hinges on the quality and security of their underlying data. By prioritizing data preparation, addressing security vulnerabilities, ensuring data integrity, and maintaining compliance, organizations can fully leverage the potential of RAG while safeguarding against risks.

Enkrypt AI offers comprehensive solutions to understand and remove risks from data powering Generative AI applications.