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Data Loss Prevention for AI

Artificial intelligence (AI), specifically generative AI (GenAI), presents a multitude of opportunities and challenges to industries worldwide. However, the rapid advancement and adoption of this technology has created significant data governance and legal obstacles like data loss and copyright infringement.

Polymer’s whitepaper covers three topics that can help you leverage GenAI while safeguarding your sensitive data:

  • The risks of using AI in the workplace
  • The challenges of data loss prevention (DLP) for cloud workflows & AI
  • How to protect sensitive data while using AI

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The risks of using AI in the workplace

Learn about the common ways data loss can occur when you use AI tools and how they can affect your HIPAA, CMMC 2.0, ISO 27001-2, GLBA, and GDPR compliance.

  • Unauthorized access
  • Data exfiltration via conversational AI bots
  • Intellectual property theft
  • Unauthorized sharing of customer or patient data
  • Copyright infringement
  • Misconfiguration of GenAI tools
  • Insider threats
  • Accidental data leaks
  • Unauthorized integration with external services
  • Lack of monitoring and oversight

Any AI tool will require monitoring of data flows to ensure compliance with regulatory and privacy standards.

The challenges of DLP for cloud workflows & AI

The reason many DLP and cloud access security broker (CASB) solutions fail to deliver value to organizations is low signal-to-noise ratio. The challenges stem from cloud platforms’ inherently unstructured and highly-collaborative environments.

Text and data generated by AI models lack a predefined structure making it difficult to identify sensitive data such as personally identifiable information (PII), protected health information (PHI), or intellectual property (IP).

Explore the obstacles facing DLP for AI solutions, the similarities between DLP for cloud workflows and DLP for AI, and why context matters.

How to protect sensitive data while using AI

Dive into the ways you can protect PII, PHI, IP, and more while still enabling employee access to GenAI tools.

  • Copyright education and awareness
  • Review and clearance processes
  • Licensing compliance
  • Copyright monitoring tools
  • Legal consultation

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DLP for AI

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