Security checklist for AI startup CTOs
The article provides a security checklist for startup CTOs deploying AI, covering data privacy, model governance, access controls, and compliance risks. It offers practical steps to secure AI systems from development through production, addressing threats like prompt injection and data leakage.
Background
- This page is a security checklist written for CTOs (Chief Technology Officers) at AI startups — early-stage companies building products powered by large language models (LLMs), vector databases, or agentic AI systems.
- AI startups face unique security risks beyond traditional software: prompt injection (users tricking the model into ignoring its instructions), insecure output handling, and data leakage through model interactions.
- The checklist covers key areas like supply chain security (your AI vendor's security posture), model access controls, monitoring for misuse, and securing the data pipeline from training through inference.
- Unlike standard web app security, AI systems have an 'LLM stack' that includes third-party model APIs (e.g. OpenAI API), self-hosted models, vector stores, embedding pipelines, and retrieval-augmented generation (RAG) components — each with its own threat surface.
- Industry context: after the 2023 OpenAI / ChatGPT boom, many AI startups moved fast without mature security practices. Regulatory pressure is rising (e.g. EU AI Act, US Executive Order on AI) and investors now expect AI-specific security diligence.