AI security is challenged by the gap between declared intent and actual runtime execution, particularly with self-hosted models. Traditional security measures like dependency scanning and network inspection fail to capture the dynamic nature of AI systems, where agents and their capabilities can change at runtime. Aviv Mussinger of Kodema highlights that many self-hosted inference instances, including those using Ollama and vLLM, are vulnerable and not visible to network-based controls. True AI security requires runtime visibility to confirm that guardrails and model identities are actively enforced, not just declared. AI
IMPACT Highlights critical gaps in current AI security practices, emphasizing the need for runtime visibility to manage risks associated with self-hosted models.
RANK_REASON Article discusses AI security challenges and proposes solutions without announcing a new product, research, or policy.
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