With great power comes—when it comes to generative AI—significant security and compliance risks. Discover how AI acceptable use policies can safeguard your organization while leveraging this transformative technology. AI has become integral across various industries, driving digital operations and organizational infrastructure. However, its widespread adoption brings substantial risks, particularly concerning cybersecurity. A crucial aspect of
Learning from the CrowdStrike Outage: Enhancing Resilience and Incident Response Overview: In the wake of the CrowdStrike outage, businesses around the globe are focusing on restoring business continuity and bolstering their resilience for future incidents. On Friday, July 19, 2024, a faulty content update triggered crashes across approximately 8.5 million Windows devices, displaying the infamous
Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing
Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI
Bridging the Interoperability Gap: TEFCA’s Role in Payer-Provider Data Exchange The electronic health information exchange (HIE) between healthcare providers has seen significant growth in recent years. However, interoperability between healthcare providers and payers has lagged behind. The Trusted Exchange Framework and Common Agreement (TEFCA) aims to address this gap and enhance data interoperability across the
Understanding AI Transparency AI transparency is about making the inner workings of an AI model clear and understandable, allowing us to see how it arrives at its decisions. It involves a variety of tools and practices that help us comprehend the model, the data it’s trained on, how errors and biases are identified and categorized,
Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The
