Webinar Highlights Expanding Attack Surface in Enterprise AI Infrastructure
What Happened — Cisco hosted a 60‑minute, credit‑eligible webinar titled “Navigating the New Threat Surface in AI Infrastructure.” The session explained how rapid AI adoption widens the attack surface beyond traditional network perimeters, exposing data pipelines, model‑training environments, API endpoints, and runtime inference layers. Speakers described concrete threat vectors such as data‑poisoning, model‑inversion, and unauthorized API calls, and they outlined a set‑of architectural pillars (governance, data integrity, runtime monitoring) needed to protect AI workloads before they scale enterprise‑wide.
Why It Matters for Trust & Control Assurance — The briefing underscores the need for a dedicated AI‑governance control objective that maps to many frameworks (e.g., NIST AI RMF) and provides continuous evidence of model‑risk management.
Who Is Affected — Enterprises deploying or planning large‑scale AI/ML models, especially in regulated sectors such as finance, healthcare, and critical infrastructure.
Recommended Actions
- Conduct an AI‑risk assessment that inventories training data sources, model repositories, and inference APIs.
- Map identified AI‑governance controls to the Verisq Common Framework (VCF) to generate audit‑ready evidence.
- Deploy continuous monitoring of model integrity and data‑pipeline security.
Technical Notes — Threats discussed include data‑poisoning of training sets, model‑extraction attacks on inference APIs, supply‑chain compromises of third‑party model components, and misconfigurations in containerized AI runtimes. No specific CVEs were cited.