Tutorials on Security

Explore essential tools for evaluating AI prompts in 2025, enhancing performance, reliability, and cost management.

Explore how Apache Kafka enables real-time event streaming for large language models, enhancing scalability and reliability in AI applications.

Explore the challenges and strategies for fine-tuning large language models for edge devices to enhance real-time processing, security, and efficiency.

Learn how to create a structured Prompt Evaluation Framework to enhance accuracy, consistency, and safety in AI model outputs.

Explore the privacy risks and protections of Retrieval-Augmented Generation systems, focusing on data security in sensitive industries.

Want to make your API integrations seamless and secure? Here's a quick guide to the essentials:

Effective monitoring of large language models is crucial for performance and security, utilizing automated alerts for real-time problem detection.

Learn effective strategies for scaling LLM APIs, focusing on performance, resilience, and security in high-demand environments.

Explore essential strategies for securing AI workflows against data breaches, insider threats, and compliance challenges in a rapidly evolving landscape.

Ensure compliance with LLM regulations in government by following this comprehensive checklist to mitigate risks and uphold ethical standards.

Explore how the Model Context Protocol (MCP) is revolutionizing enterprise AI integration, enhancing security, and improving operational efficiency.

Learn key practices for implementing audit logs in LLM pipelines to enhance security, ensure compliance, and improve operational efficiency.

Learn how to safeguard LLM pipelines from adversarial attacks with effective strategies and solutions to protect sensitive data.

Explore essential strategies for fine-tuning large language models while prioritizing data privacy and security in compliance with regulations.

Protect your LLM pipelines from evolving threats with these 7 essential API security practices, ensuring data integrity and operational reliability.

Explore the advantages and challenges of hybrid cloud versus on-premise deployment for large language models, focusing on scalability, security, and cost.

Explore the complexities and solutions for integrating Large Language Models with legacy systems, focusing on challenges, benefits, and optimized methods.

Learn essential API integration practices to ensure seamless, secure, and efficient workflows in your coding projects.

If you are using PostgreSQL for storing data of multiple users, you might want to apply row-level security, or RLS. It’s good practice even if you are manually writing all the queries you send to your database but it’s especially important if you have any type of LLM or similar generating queries for you! Let’s create a trivial data model. Users and items, whatever that might be. Each item belongs to a user. Now, per default, if you ask the database about any users items, it will just tell you. By introducing RLS, you can limit what the responses will be to add a layer of protection. Even if you should create a buggy query, you will not accidentally get the items belonging to someone else, just like you cannot accidentally change or delete items belonging to someone else. We do that like this: