What is shadow AI, and why is it a real risk for LLM apps
Blog post from Portkey
Shadow AI, a growing concern within enterprises, involves the use of AI models, APIs, or applications without the oversight of central IT or security teams, leading to significant risks such as security vulnerabilities, compliance violations, and financial costs. This phenomenon is driven by the rapid adoption of low-friction APIs and GenAI tools, which allow teams to bypass formal processes in their haste to leverage AI, often resulting in fragmented AI landscapes due to a lack of governance. Shadow AI can introduce reliability and debugging challenges, as applications may lack observability, making it difficult to trace and resolve issues. Additionally, the absence of centralized control can result in brand and reputational risks if unauthorized AI applications generate incorrect or offensive content. Detecting and mitigating shadow AI requires organizations to implement visibility and control mechanisms, such as routing all AI traffic through a centralized gateway, enforcing access controls, and establishing usage quotas to prevent experiments from escalating into costly risks.
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