Risks of Generative AI Overview
As Generative AI (GenAI) becomes more widespread and impacts critical processes, understanding and managing its risks is crucial for successful operations. This risks of Generative AI training teaches attendees how to spot the dangers, risks, and vulnerabilities of GenAI. Participants learn to methodically examine GenAI systems for potential hazards and identify mitigations for those risks. In addition, attendees gain industry-leading resources to stay updated in this rapidly evolving area.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Evaluate the ethical implications of AI systems, considering their potential impact on individuals, society, and the environment
- Understand the AI system lifecycle and the roles of different actors involved in its development, deployment, and use
- Apply the principles of AI ethics (safety, security, explainability, privacy, fairness, accountability, validity, and reliability) to the design and implementation of AI systems
- Identify and mitigate specific risks associated with generative AI, such as the creation of harmful content, privacy breaches, and bias
- Implement cybersecurity measures to protect AI systems from vulnerabilities and attacks, ensuring their security and resilience
- Recognize and address the potential negative consequences of AI failures, such as data breaches, misinformation, and discrimination
- Develop strategies to enhance the trustworthiness of AI systems, including explainability, transparency, and accountability
- Understand the emerging threats and vulnerabilities in AI cybersecurity and implement appropriate defenses
- Advocate for ethical considerations in AI research, development, and deployment to ensure a positive impact on society
Prerequisites
This course is designed for personnel responsible for identifying, assessing, and managing the risks of Generative AI in their organization. It assumes they understand how Generative AI functions at a workflow level, including core steps in the training and prediction process.
Outline
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AI Ethics and Responsibility
- What is an AI system?
- The AI System Lifecycle
- Common AI Actors
- Principles of AI Ethics
- Safe
- Secure & Resilient
- Explainable & Interpretable
- Privacy-Enhanced
- Fair
- Accountable & Transparent
- Valid & Reliable
- Cybersecurity Triad & The Fallout of Failure
GenAI Risks & Mitigations
- Nefarious Information (e.g., CBRN)
- Hallucinations
- Dangerous or Violent Recommendations
- Data Privacy
- Environmental Impacts
- Human-AI Configuration (e.g., workforce impact)
- Information Integrity (e.g., misinformation)
- Information Security
- Intellectual Property
- Obscene, Degrading, and Abusive Conduct
- Toxicity
- Bias
- Homogenization
- Supply Chain Integration
GenAI Cybersecurity – Top 10 Vulnerabilities & Defenses
- OWASP LLM Top 10
- Prompt Injection
- Insecure Output Handling
- Training Data Poisoning
- Denial of Service
- Supply Chain Vulnerabilities
- Sensitive Information Disclosure
- Excessive Agency
- Overreliance
- Model Theft
GenAI Cybersecurity – Tactics, Techniques, and Mitigations
- MITRE ATLAS
- Reconnaissance
- Resource Development
- Gaining Access
- Execution
- Persistence
- Privilege Escalation
- Defense Evasion
- Credential Access
- Discovery
- Collection
- ML Attack Staging
- Exfiltration
- Impact
Conclusion
- Frontier Threats
- Additional Resources
- Responsibility Matters
Training Materials
All Generative AI Risks training students receive comprehensive courseware.
Software Requirements
Students should have Zoom installed as the conference platform.
