BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ChamberMaster//Event Calendar 2.0//EN
METHOD:PUBLISH
X-PUBLISHED-TTL:P3D
REFRESH-INTERVAL:P3D
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTART:20260930T180000Z
DTEND:20260930T190000Z
X-MICROSOFT-CDO-ALLDAYEVENT:FALSE
SUMMARY:AI: Protecting Proprietary Data - Virtual
DESCRIPTION:9/30/26\n\nIn 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.\n\nThis 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.\n\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.\n\nLearning Objectives\n\nData Privacy and Security Risks\n\n  Risks related to handling sensitive or personal data.\n\n  Potential for data breaches\, leaks\, or misuse.\n\n  Ensuring secure data storage\, transmission\, and access controls.\n\nGovernance and Ethics\n\n  Set clear\, enforceable rules for acceptable AI use\n\n  Establish clear governance frameworks compliant with industry standards.\n\n  Intellectual property issues related to AI models and data.\n\n  Monitoring auditing and avoiding data drift\n\n  Bias and Fairness Risks\n\nHuman Factor Awareness\n\n  Understand when sensitive or regulated data must not be shared and prompts have to be treated like public disclosures.\n\n  Avoiding Automation Bias\, complacency and lack of Situational awareness\n\n  Training\, communication\, and cultural adaptation.\n\nPrivate AI Deployments\n\n  Use enterprise grade private LLMs to keep proprietary data inside organizational boundaries.
X-ALT-DESC;FMTTYPE=text/html:<h2 style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 0.75em\; font-weight: 700\; font-size: 2rem\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">9/30/26</h2>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">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.</p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">This 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.<br style="box-sizing: border-box\;" />\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.</p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;"><span style="box-sizing: border-box\; font-weight: bolder\;">Learning Objectives</span></p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">Data Privacy and Security Risks<br style="box-sizing: border-box\;" />\n&bull\; Risks related to handling sensitive or personal data.<br style="box-sizing: border-box\;" />\n&bull\; Potential for data breaches\, leaks\, or misuse.<br style="box-sizing: border-box\;" />\n&bull\; Ensuring secure data storage\, transmission\, and access controls.</p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">Governance and Ethics<br style="box-sizing: border-box\;" />\n&bull\; Set clear\, enforceable rules for acceptable AI use<br style="box-sizing: border-box\;" />\n&bull\; Establish clear governance frameworks compliant with industry standards.<br style="box-sizing: border-box\;" />\n&bull\; Intellectual property issues related to AI models and data.<br style="box-sizing: border-box\;" />\n&bull\; Monitoring auditing and avoiding data drift<br style="box-sizing: border-box\;" />\n&bull\; Bias and Fairness Risks</p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 16px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">Human Factor Awareness<br style="box-sizing: border-box\;" />\n&bull\; Understand when sensitive or regulated data must not be shared and prompts have to be treated like public disclosures.<br style="box-sizing: border-box\;" />\n&bull\; Avoiding Automation Bias\, complacency and lack of Situational awareness<br style="box-sizing: border-box\;" />\n&bull\; Training\, communication\, and cultural adaptation.</p>\n\n<p style="box-sizing: border-box\; margin-top: 0px\; margin-bottom: 28px\; font-size: 18px\; line-height: 1.5\; color: rgb(20\, 24\, 39)\; font-family: &quot\;Helvetica Neue&quot\;\, Helvetica\, -apple-system\, BlinkMacSystemFont\, Roboto\, Arial\, sans-serif\;">Private AI Deployments<br style="box-sizing: border-box\;" />\n&bull\; Use enterprise grade private LLMs to keep proprietary data inside organizational boundaries.</p>\n
LOCATION:Venue Online
UID:e.2857.2806
SEQUENCE:3
DTSTAMP:20260917T225104Z
URL:https://business.shccnj.org/events/details/ai-protecting-proprietary-data-virtual-2806
END:VEVENT

END:VCALENDAR