SUMM

How to Build Trustworthy AI — Allie Howe

Introduction to Trustworthy AI 00:01

  • Ally Howe introduces herself as VCSO for Growth Cyber, focusing on building trustworthy AI at the intersection of AI security and compliance.
  • The video explores the definition, importance, and components of trustworthy AI.

Importance of Trustworthy AI 00:33

  • Recent incidents highlight the risks of untrustworthy AI, including a chatbot misoffering a vehicle and Slack leaking data due to prompt injection.
  • The emergence of AI characters in gaming raises concerns about inappropriate behavior, echoing past AI missteps.

Responsibility for Trustworthy AI 02:06

  • The onus of ensuring trustworthy AI falls on users and organizations, as illustrated by a recent lawsuit against Open AI for misleading statements generated by ChatGPT.
  • Companies must be aware of their accountability for any negative consequences stemming from AI outputs.

Building Trustworthy AI: Key Focus Areas 03:00

  • Trustworthy AI involves collaboration between product engineering and security teams to ensure accurate, relevant, and safe outputs.
  • The recipe for trustworthy AI combines AI security (external threats) and AI safety (internal risks).

New Paradigms in AI Engineering 04:06

  • The shift from traditional dev sec ops to AI engineering necessitates new models for integrating security within AI development workflows.
  • Emphasis is placed on runtime security due to the non-deterministic nature of AI applications.

AI Security Practices: ML SecOps 05:59

  • ML SecOps focuses on machine learning security operations, addressing vulnerabilities that traditional methods may overlook.
  • Important considerations include model provenance and the risks of model serialization attacks.

AI Red Teaming 09:27

  • AI red teaming simulates threats to test for vulnerabilities and ensure models do not output harmful or biased information.
  • Continuous testing is crucial due to evolving user interactions and potential biases in AI responses.

Importance of Runtime Security 11:29

  • Runtime security is emphasized as a critical area for protecting AI applications from prompt injections and unsafe outputs.
  • Implementing runtime security can help filter inappropriate prompts and validate AI outputs before they reach users.

Case Study: AI Implementation in Fortnite 13:30

  • The architecture of AI interactions in Fortnite is discussed, showcasing how various components work together.
  • AI runtime security can be integrated to monitor and validate inputs and outputs in real-time.

Custom Guardrails and Compliance 18:01

  • AI runtime security solutions allow for the implementation of custom guardrails to restrict inappropriate queries or actions.
  • Validating AI outputs and demonstrating compliance can enhance customer trust and streamline sales processes.

The Business Case for Trustworthy AI 20:22

  • Aligning cybersecurity and business risks underscores the necessity of building secure AI applications from the outset to protect revenue.
  • Increasing regulatory scrutiny necessitates proactive compliance measures to avoid penalties.

Future Innovations with Trustworthy AI 22:30

  • Trustworthy AI is essential for leveraging innovations in fields like healthcare, paving the way for groundbreaking advancements.
  • Without trust in AI systems, the potential for revolutionary applications remains untapped.

Conclusion: Your Responsibility for Trustworthy AI 23:20

  • Organizations are reminded of their responsibility in building trustworthy AI to avoid legal repercussions.
  • Trustworthy AI integrates security and safety measures, emphasizing the need for comprehensive strategies in AI deployment.
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