LLMs and AI have increasingly become major contributors to transforming content creation today. Understanding and using prompt skills appropriately can help organisations optimise AI to generate high-quality content efficiently.

While AI offers multiple benefits, it's important to acknowledge the potential risks associated with its implementation. Organisations are advised to carefully consider factors such as data privacy, bias, and the ethical implications of AI-generated content.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Prof. Yash Shreshta, Assistant Professor at the University of Laussane, about prompt engineering and its benefits.

Key Takeaways

• Prompts are commands given to AI to perform tasks.
• Prompt engineering allows users to communicate effectively with AI.
• Iterative processes improve the quality of AI outputs.
• Understanding LLM limitations is crucial for effective use.
• Collaboration can enhance the creative process with AI.
• The role of prompt engineering is rapidly evolving with AI advancements.
• Data privacy is a significant risk when using LLMs.
• Over-reliance on AI can lead to skill degradation.
• Organisations should integrate human creativity with AI.
• Regular training on prompt engineering is essential for maximising LLM benefits.

Chapters

00:00 Introduction to Prompt Engineering and AI

01:30 Understanding Prompt Engineering

04:15 The Importance of Prompt Engineering Skills

06:37 Best Practices for Effective Prompts

08:31 The Evolving Role of Prompt Engineering

11:20 Risks and Challenges of AI in Organizations

13:15 The Future of Creativity with AI

The fraud division has witnessed a dramatic transformation in the age of artificial intelligence (AI). As technology advances, so do the methods employed by fraudsters. Modern criminals use sophisticated techniques, such as deep learning and natural language processing, to deceive individuals and organisations alike. Such techniques allow them to mimic human behaviour, manipulate data, and exploit vulnerabilities in security systems.

That’s why organisations are embracing AI's strengths to combat these evolving threats. AI-driven solutions can provide real-time detection of fraudulent activities, analyse vast amounts of data to identify patterns and anomalies, and automate response processes.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Xavier Sheikrojan, Senior Risk Intelligence Manager at Signifyd, about AI fraud.

Key Takeaways:

• Fraudsters have evolved from individuals to organised criminal enterprises.
• AI enables fraudsters to scale their attacks rapidly and strategically.
• Phishing attacks have become more sophisticated with AI-generated content.
• Synthetic identities can be created easily, complicating fraud prevention.
• Opportunistic fraud is impulsive, while proactive fraud is well-planned.
• Businesses often fail to act post-breach due to resource constraints.
• Inaction after a breach can lead to repeated attacks by fraudsters.
• AI must be used to combat AI-driven fraud effectively.
• Balancing fraud detection with customer experience is crucial for businesses.

Chapters:

00:00 Introduction to AI and Fraud

01:32 The Evolution of Cybercrime

05:43 AI's Role in Modern Fraud Techniques

09:55 Opportunistic vs. Proactive Fraud

12:44 Business Inaction and Its Consequences

15:59 Combating AI-Driven Fraud with AI

As AI technologies become more integrated into business operations, they bring opportunities and challenges. AI’s ability to process vast amounts of data can enhance decision-making but also raise concerns about data privacy, security, and regulatory compliance.

Ensuring that AI-driven systems adhere to data protection laws, such as GDPR and CCPA, is critical to avoid breaches and penalties. Balancing innovation with strict compliance and robust data security measures is essential as organisations explore AI’s potential while protecting sensitive information.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Erin Nicholson, Global Head of Data Protection and AI Compliance at Thoughtworks, about the importance of compliance frameworks, best practices for transparency and accountability, and the need for collaboration among various teams to build trust in AI systems.

Key Takeaways:

• AI systems are powerful but require ethical and compliant design.
• Lack of standardisation in AI regulations poses significant challenges.
• AI models often need help with explainability and transparency.
• Compliance frameworks are essential for implementing AI in critical sectors.
• Documentation and audits are crucial for maintaining AI accountability.
• Baselining pre-AI processes helps build public trust in AI systems.
• Organisations should map regulations to the most stringent standards.
• Cross-functional collaboration is vital for effective AI compliance.

Chapters:

00:00 - Introduction to AI, Data Protection, and Compliance

02:08 - Challenges in AI Implementation and Compliance

05:56 - The Role of Compliance Frameworks in Critical Sectors

10:31 - Best Practices for Transparency and Accountability in AI

14:48 - Navigating Regional Regulations for AI Compliance

17:43 - Collaboration for Trustworthiness in AI Systems