Auditing AI LLM Models || Skillweed

Auditing AI LLM Models || Skillweed

🔍 Dive into the world of AI! In this captivating video, we break down the intricate process of auditing AI Language Model (LLM) architectures. Discover the importance of ethical standards and oversight to ensure these powerful models operate transparently and fairly. Our expert panel discusses methodologies, best practices, and red flags to look out for when evaluating LLMs. Want to understand how to maximize efficiency and reliability in AI projects? We've got you covered! Join the conversation, subscribe, and don’t miss out on crucial insights that could redefine AI development! 📈🤖 #AIAudit #LLM #ArtificialIntelligence #TechEthics

What are AI Models, why are the importance of Auditing AI LLM Models and reviewing each of the below:
Logs of access attempts and permissions changes
Anonymization techniques applied to training data
Reports on data leakage assessments
Results of adversarial attack simulations
Reports on bias assessment and mitigation measures
Version control system logs and cryptographic hashes of model versions
Reports on model verification processes and consistency checks
Secure code review reports and documentation
Results of static code analysis and code review
Deployment configuration documentation and security assessment reports
Network penetration testing reports and network security configuration details
Logs of LLM activity, system logs, and access events
Reports on log analysis and incident detection
Documentation on threat intelligence feeds integration and threat analysis
Reports on threat hunting activities and identification of indicators of compromise
Vulnerability scanning reports and remediation plans
Red team exercise reports and post-exercise evaluations
Incident response plan documentation and records of tabletop exercises
Records of incident handling drills and evaluations
Reports on bias identification and mitigation measures


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