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.
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\nThis webinar re-frames AI integration through a RISK and Maturity centric lens\, showing how NJMEP can help organizations build a defensible &ldquo\;AI perimeter&rdquo\; that protects high value information while still enabling innovation.
\nParticipants will learn how to reduce human driven exposure\, strengthen technical safeguards\, and ensure that company data remains a strategic advantage rather than a liability.
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\nLearning Objectives
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\nData Privacy and Security Risks
\n&bull\; Risks related to handling sensitive or personal data.
\n&bull\; Potential for data breaches\, leaks\, or misuse.
\n&bull\; Ensuring secure data storage\, transmission\, and access controls.
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\nGovernance and Ethics
\n&bull\; Set clear\, enforceable rules for acceptable AI use
\n&bull\; Establish clear governance frameworks compliant with industry standards.
\n&bull\; Intellectual property issues related to AI models and data.
\n&bull\; Monitoring auditing and avoiding data drift
\n&bull\; Bias and Fairness Risks
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\nHuman Factor Awareness
\n&bull\; Understand when sensitive or regulated data must not be shared and prompts have to be treated like public disclosures.
\n&bull\; Avoiding Automation Bias\, complacency and lack of Situational awareness
\n&bull\; Training\, communication\, and cultural adaptation.
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\nPrivate AI Deployments
\n&bull\; Use enterprise grade private LLMs to keep proprietary data inside organizational boundaries.