How to prevent your new chatbot from giving away company secrets

    enJune 04, 2024

    Podcast Summary

    • AI and software developmentStay curious and adaptable in tech, embrace new trends like AI, and utilize resources like Intel.com/edgeai for development and deployment assistance

      Kamakshi Narayan, also known as CAMS, shares her journey from a fascination with finance and business management to a successful career in software development and product management. Along the way, she discovered her aptitude for application development and eventually transitioned into product management, a role that allowed her to interface with stakeholders, plan roadmaps, and manage engineering teams. As technology evolved, she stayed curious and adaptable, embracing new trends like generative AI and its impact on API management, data governance, and the modernization of legacy tech. For those interested in the world of software and technology, especially as it relates to AI and its applications, Intel.com/edgeai offers valuable resources, including open-source code snippets and helpful guides, to speed up development and ensure seamless deployment.

    • API management with AIAPIs are increasingly important and will have AI capabilities, necessitating advancements in security and connectivity models for effective API management, ensuring responsible use and addressing concerns around security and data privacy.

      APIs, fueled by the increasing use of AI, are becoming an essential part of technology products and services, and their importance will continue to grow. The speaker, currently a director of product managing API management at SnapLogic, emphasized the ubiquity of APIs and their role in enabling the democratization of application development. Every API will likely have an interface with AI capabilities, necessitating advancements in security and connectivity models. API management will be crucial in providing a structured way to expose and access AI models and services, while ensuring responsible use and addressing concerns around security and data privacy. The speaker highlighted the example of OpenAI starting out as an API platform and the need for effective API management to handle scalability and performance issues. With the increasing integration of AI into various industries, including finance, API management will play a vital role in addressing security and privacy concerns and enabling the adoption of AI technologies.

    • API management for AI modelsAPI management is essential for securing access to data used in AI models and preventing unintended data leaks. Encryption, security algorithms, and access profiling are key features of API management platforms.

      Effective API management plays a crucial role in securing and controlling access to data used in AI models, especially when dealing with sensitive information. The data and the AI model should be kept separate to prevent unintended data leaks. API management platforms provide encryption and security algorithms to ensure secure data transmission. Access to systems and data can be profiled and different layers of access can be set based on user roles. For instance, a CEO may have access to financial records, while an employee in the customer service department may not. AI-specific complexities, such as screening prompts and enforcing checks on shared information, are areas for future advancements in API management systems.

    • AI in citizen developer modelAI simplifies the development process for citizen developers by providing drag-and-drop components, suggest capabilities, templates, NLP capabilities, and analyzing code for vulnerabilities and optimizations.

      The citizen developer model, which allows non-technical individuals to build applications using low-code and no-code platforms, has gained significant momentum with the addition of AI capabilities. Guardrails are essential for ensuring security in these systems, but there's also an opportunity for API management platforms to enhance security and other mechanisms. AI has simplified the development process with features like drag-and-drop components, suggest capabilities, templates, and NLP capabilities, making it easier for citizen developers to build applications. Furthermore, AI can help developers by analyzing code, detecting vulnerabilities, identifying bugs, and suggesting optimizations, reducing the time involved in testing and debugging. The recent launch of Overflow AI by Stack Overflow is a testament to the vast potential of these tools in improving code quality and troubleshooting. While the citizen developer model isn't new, AI has added significant value to it, making it more accessible and powerful for individuals and organizations alike.

    • AI-assisted developmentGenerative AI models can enhance development process, but require robust guardrails for secure and reliable code, and offer opportunities for upskilling and reskilling

      Generative AI models can significantly improve the development process by automating repetitive tasks, enhancing code quality, and assisting with documentation. However, the unpredictable nature of these models requires robust guardrails, such as API management and testing, to ensure secure and reliable code. Additionally, the use of generative AI provides an opportunity for upskilling and reskilling by adapting to the model's output and learning from its unique approach to coding. The challenge lies in implementing effective guardrails to maintain control and consistency while harnessing the benefits of AI-assisted development.

    • Low-code/no-code tools and human involvementThough low-code/no-code tools can generate code, human involvement is crucial for ensuring quality and suitability. Upskilling, adapting to new technologies, and focusing on data governance and security are essential in the evolving tech workforce.

      While the use of low-code and no-code tools is on the rise, the human element in software development remains crucial. These tools can generate code, but it's important to ensure the quality and suitability of the output for specific use cases. The interaction between humans and these tools is ongoing, and there's a need for upskilling and adaptation to new technologies and interview processes in the evolving tech workforce. Additionally, data governance and security are significant considerations when using these tools, especially with regards to data privacy and compliance. API management systems can help with data handling procedures and access controls, but data governance is a complex issue that requires dedicated attention. Ultimately, data is a valuable asset, and protecting it while leveraging it for Gen AI applications involves implementing robust security measures.

    • Data governanceOrganizations must manage and govern data responsibly, implementing procedures for access and usage, risk management, and compliance with regulations like GDPR.

      Data management and governance are crucial aspects of any organization, especially in the tech industry. Data owners must consider responsibilities around data access and usage, implementing procedures and risk management strategies for potential misuse or breaches. Compliance with regulations like GDPR is essential for handling customer sensitive data and PII. A well-built data governance system is necessary to ensure future readiness as new technologies are adopted. A great example of this is Ethan Hileman's question on Stack Overflow about dealing with Java garbage collection log messages, which helped thousands of developers. We appreciate Ethan's contribution to the community and encourage everyone to engage in the Stack Overflow podcast or blog with their thoughts and questions.

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