How to secure AI

HOW TO SECURE AI is your go-to resource for mastering the intersection of cybersecurity and artificial intelligence. We deliver high-signal, low-noise content designed for professionals building, deploying, or securing AI/LLM-powered applications. Through bite-sized, actionable posts, we tackle both offensive and defensive security techniques specific to GenAI systems, LLMs, and AI infrastructure. Expect insights on: • Prompt injection mitigation, context hijacking, and token leakage • Secure LLM architecture design, input validation, and access control strategies • Jailbreak detection, canary tokens, and sandboxing LLM execution • SOC2, data privacy, and compliance best practices for GenAI applications • Red teaming against LLMs, adversarial robustness, and securing RAG pipelines • Safe use of tools like LangChain, Guardrails AI, and OpenAI Functions We also cover niche topics like fine-tuning vs. prompt engineering, differential privacy in training data, and using post-processing filters to block unwanted or harmful LLM output. Whether you’re a builder, red teamer, risk manager, or technical decision-maker, this page equips you with knowledge to secure AI systems from design to deployment—while staying ahead of the evolving threat landscape. Follow for weekly breakdowns, code examples, threat models, and real-world security exercises tailored for AI systems.