AI Readiness for Life Sciences: Navigating Cybersecurity Risks and Compliance
Executive Summary
Artificial intelligence is transforming life sciences — accelerating drug discovery, optimizing clinical trials, and enabling precision medicine. But AI adoption introduces new cybersecurity risks and compliance challenges that many organizations are not prepared to address.
From the security of training data and model integrity to regulatory requirements for AI-driven decisions in clinical settings, life sciences organizations need a comprehensive framework for AI readiness that addresses both the opportunities and the risks.
This white paper examines the cybersecurity and compliance dimensions of AI adoption in life sciences — providing a practical roadmap for organizations seeking to harness AI while maintaining security and regulatory compliance.
Download the full PDF for the complete analysis, frameworks, and implementation guidance.
Key Takeaways
- AI training data security requirements for sensitive research and clinical data
- Model integrity and adversarial AI risks in life sciences applications
- Regulatory landscape for AI in life sciences including FDA guidance on AI/ML
- Data governance frameworks for AI that satisfy HIPAA, GxP, and international requirements
- Shadow AI risks and discovery strategies for life sciences organizations
- Vendor evaluation criteria for AI platforms and tools in regulated environments
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