Workshop Summary 

The adoption of artificial intelligence (AI) products and services for the healthcare industry—such as predictive analytics, medical imaging analysis, and automated administrative processes—is projected to experience significant growth in the coming years.  

• While this expansion promises substantial benefits, it also introduces several critical challenges. These include ensuring regulatory compliance, maintaining AI model transparency, preventing AI manipulation, and safeguarding data privacy and security to name a few.  

Addressing these issues is essential to ensure that AI technologies are deployed ethically and responsibly, mitigating risks while maximizing their potential positive impact to the industry. 

• This workshop outlines the components required to establish an AI Governance program, including the people, process and technology that drives the programs operation. 
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• It will also touch on the new amount of increased output and visibility that you will be presented with and two secrets to help automate lower and mid-level risk or issues with automation and risk calculations so your teams can focus on the high risks/issues. 

Attendees Can Expect to Learn How To:

1. Run proactive AI/IT/OT asset management deployments, VS reactive break/fix legacy programs... by integrating data between asset visibility, deployment context, cyber vulnerabilities/incidents, controls and operational risks by aggregating risk factors. 
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3. Enable your team to focus on high risks, by automating low/medium level issues, risks and incidents