As organizations adopt artificial intelligence (AI) in cloud security, the emergence of distinct challenges has raised pressing concerns among security teams. The ongoing integration of large language models (LLMs) and AI agents into cloud environments has created uncertainties about adequate protection against new risks.
The white paper titled “5 Steps to Close the AI Security Gap in Your Cloud Security Strategy” highlights the urgency of adapting current security practices to meet these evolving threats. Security professionals are now confronted with crucial questions about the efficacy of existing tools and the necessary policy updates to safeguard their AI assets.
Challenges are intensified by AI's unique characteristics, which undermine traditional security frameworks. Issues such as data access controls can be compromised when LLMs trained on sensitive information can replicate data despite revoked access. Additionally, the rise of nonhuman identities generated by AI complicates data governance and access management.
Furthermore, there is a notable skills gap among security teams, who must develop AI security knowledge while managing ongoing projects. The current tools are fragmented, often addressing isolated AI risks rather than providing a comprehensive security solution, complicating the management of AI-related vulnerabilities.