Machine Learning Model Risk Monitoring and Vulnerabilities Overview

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Explore the latest advancements in machine learning model risk monitoring, identification, and verification (MEIV). Learn about vulnerabilities such as perturbation, adversarial patches, and security considerations for faster MLOPS deployment processes. Discover Parsons Corporation's role in addressing challenges like data poisoning, AI security, and model inversion attacks within trusty-worthy AI inference systems.

  • Machine Learning
  • Vulnerabilities
  • Security
  • Monitoring
  • Parsons Corporation

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  1. Sensitive MODEL EXAMINATION, IDENTIFICATION AND VERIFICATION (MEIV) Machine Learning Model Risk Monitoring Matt Campbell/Richard Stanfield 19 MAR 2025

  2. Sensitive ML MODEL VULNERABILITIES Perturbation Adversarial Patches Physical 2025 Parsons Corporation 2

  3. Sensitive ML MODEL VULNERABILITIES 2025 Parsons Corporation 3

  4. Sensitive COMPETING INTERESTS Security & Reliability Faster Deployment Tempo MLOPS Processes (Test and Evaluation) 2025 Parsons Corporation 4

  5. Sensitive CONSIDERATIONS Continuous Integration / Continuous Deployment Automated Testing Monitoring and Logging Version Control Scalability Collaboration Security Compliance 2025 Parsons Corporation 5

  6. Sensitive PARSONS MODEL EXAMINATION, IDENTIFICATION, AND VERIFICATION (MEIV) Architecture Verification Initialize Hash Fingerprint Check Publish 2025 Parsons Corporation 6

  7. Sensitive MEIV ROLE WITHIN T&E Ongoing Testing Data Poisoning AI Security T&E Model Inversion Trustworthy/Reliable AI Inference Attacks CI/CD Evasion Attacks 2025 Parsons Corporation 7

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