AI model provenance is quickly becoming a top security question for enterprise buyers. Amy Chang, Head of AI Threat and Security Research at Cisco, joins Alan Shimel on Techstrong TV. Furthermore, she explains why country of origin is a poor proxy for real model risk.

About Amy Chang

Amy has spent about two decades in cybersecurity with a recent focus on AI security. In addition, she now leads the Cisco team studying how models fail across the full development life cycle. Consequently, her group covers red teaming, mitigations and industry best practices.

Why AI model provenance matters

Amy shares that Cisco publishes an open LLM security leaderboard for frontier and open weight models. Furthermore, common jailbreaks and prompt injections hit up to 80 or 90 percent success in five turns. Therefore, guardrails alone are not enough for enterprise deployments.

She also treats agents as untrusted entities inside any deployment environment. Meanwhile, prompts that are too narrow or too broad both create exploitable gray areas. As a result, security teams need layered controls and clear scope for every agent.

Beyond country of origin

Amy details new research where two independent methods traced technical lineage across US and Chinese models. In addition, some US built models shared clear lineage with Chinese ones. Consequently, country of origin becomes a weak signal without deeper AI model provenance data.

She points enterprises toward the Cisco AI Defense GitHub for a model provenance kit and fingerprints. Meanwhile, Cisco has donated CodeGuard to the Coalition for Secure AI. Therefore, more open transparency and shared tools can help defenders make safer choices.

Amy also flags the AI Supply Chain Provenance Explorer as a free public resource. Furthermore, it reports license restrictions, countries of origin and security notes for many models. Consequently, buyers can align AI model provenance data with their own risk tolerance before deployment.

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