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AI Cybersecurity

In: AI and the Boardroom

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  • Rohan Sharma

Abstract

Imagine your business relying heavily on AI for day-to-day decisions, only to realize that a security breach could compromise sensitive data and erode customer trust. As AI becomes deeply integrated into enterprise operations, safeguarding these systems from vulnerabilities is not just important—it’s critical. This chapter explores the key security challenges facing AI systems, particularly for Large Language Models (LLMs), such as data leaks, malicious actors, and data poisoning. It contrasts these threats with proactive strategies like strict data handling protocols, stringent access controls, and effective data masking. By focusing on continuous monitoring and collaborating with security experts, businesses can strengthen their AI security frameworks. Actionable insight: Develop a solid AI security strategy today—implement regular system audits, enforce access restrictions, and keep your teams informed about AI security best practices. Is your AI infrastructure ready to face modern security challenges?

Suggested Citation

  • Rohan Sharma, 2024. "AI Cybersecurity," Springer Books, in: AI and the Boardroom, chapter 0, pages 225-236, Springer.
    • Handle: RePEc:spr:sprchp:979-8-8688-0796-1_18
    DOI: 10.1007/979-8-8688-0796-1_18
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