AI: Protecting Proprietary Data - Virtual

9/30/26

In 2026, organizations face a rapidly evolving threat landscape in which artificial intelligence can unintentionally expose intellectual property, trade secrets, and sensitive operational knowledge. With unmanaged environments, behaviors can turn proprietary knowledge into a public asset and create opportunity for accidental disclosure, espionage, data leakage, and competitive loss.

This webinar re-frames AI integration through a RISK and Maturity centric lens, showing how NJMEP can help organizations build a defensible “AI perimeter” that protects high value information while still enabling innovation.
Participants will learn how to reduce human driven exposure, strengthen technical safeguards, and ensure that company data remains a strategic advantage rather than a liability.

Learning Objectives

Data Privacy and Security Risks
• Risks related to handling sensitive or personal data.
• Potential for data breaches, leaks, or misuse.
• Ensuring secure data storage, transmission, and access controls.

Governance and Ethics
• Set clear, enforceable rules for acceptable AI use
• Establish clear governance frameworks compliant with industry standards.
• Intellectual property issues related to AI models and data.
• Monitoring auditing and avoiding data drift
• Bias and Fairness Risks

Human Factor Awareness
• Understand when sensitive or regulated data must not be shared and prompts have to be treated like public disclosures.
• Avoiding Automation Bias, complacency and lack of Situational awareness
• Training, communication, and cultural adaptation.

Private AI Deployments
• Use enterprise grade private LLMs to keep proprietary data inside organizational boundaries.