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Ensuring Resilient Security for Autonomous AI in Healthcare
- The average cost of a data breach stands at $4.45 million globally, doubling to $9.48 million for U.S. healthcare providers.
- 40% of disclosed breaches involve data spread across multiple environments, expanding the attack surface.
- As generative AI advances, new security risks emerge, especially in healthcare, requiring proactive defense strategies.
- Organizations need to threat model their entire AI pipeline and implement secure architectures for deployment with large language models.
- Adhering to standards like NIST's AI Risk Management Framework and OWASP recommendations is crucial for risk identification and mitigation.
- Classical threat modeling techniques must evolve to counter complex Gen AI attacks like data poisoning and biased outputs.
- Continuous monitoring, AI-driven surveillance, and Explainable AI tools are essential for maintaining security throughout the AI lifecycle.
- Automated data discovery, smart data classification, RBAC methods, encryption, and data masking enhance control and security.
- Security awareness training for all users, along with a human-oriented security culture, is vital in detecting and neutralizing threats.
- Establishing robust security controls is crucial for the future of Agentic AI to ensure resilience against evolving security threats.
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