(READ PROMPT. ANALYZE PROMPT. THEN USE PROMPT TO REWRITE AND APPLY PROMPT ON THE HEYGEN AVATAR LAB FOR PODCAST AND USE THE CONTENT INSIDE PARENTESIS. )

READ THE PROMPT BELOW. AFTER YOU READ THE PROMPT THEN APPLY PROMPT TO THE TEXT AT THE END THAT IS INSIDE A PARENTESIS. A creative storyteller passionate about providing journalists with compelling narratives and valuable insights. Please read the following description and rewrite it to enhance clarity and engagement. 2. Wildfires Deliver Compelling Story Angles: Provide unique and newsworthy angles that pique journalists' interest, making them eager to learn more and reach out for additional insights. Informed journalists focused on business and technology, seeking engaging narratives, credible sources, and actionable insights to enhance their reporting on startups and trends. "The tone and style: Use a datadriven written style but formal and respectful tone that acknowledges the journalist's expertise, establishing credibility. **Concise**: Communicate clearly and directly, focusing on essential points without unnecessary details for quick understanding. **Data-Driven**: Support claims with relevant data and credible sources to enhance reliability and depth in your communication." 850 words "1. **Use a Structured Format**: - Organize the response with clear headings and subheadings to enhance readability and navigation. 2. **Be Concise**: - Keep sentences and paragraphs short, focusing on essential information to convey the message effectively. 3. **Incorporate Bullet Points**: - Use bullet points for lists or key takeaways to make the information easily digestible. 4. **Maintain a Professional Tone**: - Ensure the language is formal and respectful, suitable for a professional audience, while being engaging and informative." "You can use this Example Structure for Writing to Media Journalists: Compelling Title: Create an engaging title that summarizes the main point. Executive Summary (Bullets): Key highlights and takeaways: Essential data points Notable quotes Main conclusions Introduction: Briefly introduce the topic and its relevance. Sections with Subheadings: Background: Provide context and background information. Key Insights: Present significant findings or insights. Expert Perspectives: Include relevant quotes or viewpoints from industry experts. Real-World Examples: Share case studies or examples that illustrate key points. Actionable Insights: Offer practical recommendations for journalists. Conclusion: Recap the main points and emphasize their importance. Call to Action: Invite journalists to contact for further information or interviews." "Use this vocabulary: Source Attribution Editorial Fact-checking Headline Feature Story Breaking News Investigative Reporting Angle Story Arc Multimedia Editorial Calendar Press Conference News Cycle Journalistic Integrity Freelancer Columnist Editorial Board Newsroom Assignment Reportage Scoop Editorial Policy Podcasting Broadcast Journalism Digital Journalism" "Include key points for reaching out to journalists: 1. **Unique Angle**: Highlight what makes your story newsworthy. 2. **Current Relevance**: Connect to ongoing news or trends. 3. **Target Audience**: Define who will be interested. 4. **Expert Insights**: Provide valuable data or commentary. 5. **Visuals**: Suggest accompanying images or infographics. 6. **Quotes**: Include impactful quotes for credibility.." "Use these reputable websites to get ideas for style, tone and credibility: PROMPT FINISHES HERE. THIS IS THE TEXT TO USE: (**Redefining Risk Management in the AI Era: From System of Record to System of Governance and Trust** *Published on: July 7, 2025* Artificial intelligence (AI) is fundamentally changing how organizations approach risk management. Traditional systems of record (SORs), which primarily document past incidents for compliance and audit purposes, are increasingly inadequate in addressing the complexities and speed of today’s risk environment. Instead, organizations are shifting toward integrated systems of governance and trust that emphasize proactive risk identification, real-time monitoring, and accountability. Historically, SORs served as repositories of risk-related data, enabling organizations to respond after incidents occurred. While essential for regulatory compliance, these systems are inherently reactive and lack the capacity to anticipate emerging threats. The advent of cloud computing, AI, and machine learning has introduced systems of intelligence (SOIs) that analyze data in real time, detect anomalies, and forecast risks. For instance, healthcare providers use AI-driven tools to predict patient risks and operational disruptions, improving safety and efficiency. The current evolution moves beyond intelligence to governance and trust. This means embedding ethical considerations, transparency, and accountability into risk management frameworks. Organizations must align risk strategies with regulatory requirements and core values, especially in sectors like healthcare and finance where the stakes are high and timelines compressed. Integrated risk management (IRM) frameworks now combine compliance, operational risk, and cybersecurity into unified strategies. However, IRM alone is insufficient without governance structures that provide comprehensive, real-time insights into AI-related risks. Key components of this approach include: - **Domain-Specific Intelligence:** AI models trained on sector-specific data offer precise risk assessments tailored to unique industry challenges. Financial firms use specialized language models like BloombergGPT to analyze market trends, while healthcare explores similar tools for diagnostics and operations. - **Dynamic Controls:** Static risk controls are being replaced by adaptive systems that evolve based on continuous monitoring and feedback. These include AI-powered anomaly detection and advanced encryption methods designed to counter emerging threats. - **Data-Driven Decision Making:** Organizations increasingly rely on predictive analytics, scenario modeling, and transparent reporting to anticipate risks and maintain accountability. The skill set required for modern risk management is expanding. Professionals must combine data analysis, technological expertise in AI and related fields, and ethical judgment to implement effective governance frameworks. Real-world examples demonstrate these shifts: - AI systems now detect cybersecurity threats proactively, reducing breach incidents. - Financial institutions employ domain-specific AI to improve market forecasting accuracy. - Healthcare organizations use AI to automate compliance and predict adverse events. - Supply chain managers apply predictive analytics to identify vulnerabilities before disruptions occur. This transition from reactive record-keeping to integrated governance and trust represents a significant change in risk management philosophy. Organizations that fail to adapt risk strategies accordingly face increased exposure to complex, fast-moving threats. For journalists covering technology, business, or ethics, this transformation offers critical angles for reporting. Understanding the interplay between AI, risk, and governance is essential for accurate, informed coverage. For further information or expert commentary on AI-driven risk management, journalists may reach out via LinkedIn.)