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Webinar Resources: Secure Innovation: Bridging Digital Trust and Data Security in AI
Webinar Description
Generative AI can accelerate teaching, research, and operations but it also introduces new novel security risks (such as: prompt-injection/jailbreak attacks, data poisoning/leakage, training/data-rights) that can undermine security, privacy, compliance, and trust if not governed uniformly. This workshop shows how to use AI at ASU in a way that protects users, data, and intellectual property while still enabling principled innovation.
Resource Sharing
Name of resource
Description
From OWASP (Open Worldwide Application Security Project) AI Resources
From OWASP (Open Worldwide Application Security Project) AI Resources: Includes AI Cyber Threat Intel, Secure GenAI Adoption, AI Red Teaming & Eval, Risk & Data Gathering, Agentic Application Security
From OWASP (Open Worldwide Application Security Project) AI Resources: Contains 300+ guides from getting-started essentials to deep-dives on specific AI-related sub-systems
From OWASP (Open Worldwide Application Security Project) AI Resources
Media Security/AI sources
Helpful for staying up to date on latest attack & innovations
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Questions from the audience
Question
Response
AI Ethics, Responsibility, and Accountability: How can AI systems be governed and regulated in a way that ensures fairness, accountability, and independence from corporate influence?
Open
Security, Safety, and Risk Management in AI Systems: How can AI systems be designed and managed to ensure security, prevent misuse, and protect data—especially in resource-constrained environments?
Open
Reliability and Limitations of AI Systems (Hallucination & Trust): To what extent can AI systems be trusted to produce accurate and reliable outputs, even when constrained by specific data sources?
Open
AI Detection, Academic Integrity, and Validity of Measurement: How can AI-generated content be accurately detected and fairly evaluated in academic and research contexts?
Open
Access, Equity, and Licensing Challenges in Low-Resource Contexts: How can equitable access to trustworthy, licensed AI tools be ensured in regions with limited infrastructure or payment systems?
Open
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