AI and GenAI Security: A Complete Guide for 2026
A complete guide to AI and GenAI security in 2026, covering the threat landscape, the OWASP Top 10 for LLMs and defense-in-depth controls.
AI Gateway
LLM API Rate Limiting with Virtual Keys and Budgets
TL;DR
* LLM API rate limiting caps how many requests and tokens a consumer can send in a time window, while budgets cap how much money that consumer can spend; you need both to prevent 429 errors and runaway cost.
* In Bifrost, the open-source AI gateway by Maxim AI,
LLM Monitoring: Metrics, Audit Logs, and Controls
TL;DR
* LLM monitoring is the continuous collection of cost, latency, token, and error-rate signals for every model call, so engineering and compliance teams can see what happened and who did it.
* The core metrics to track are cost per request, latency (including time to first token), token consumption,
Run an Open-Weight Model in Production Without Code Changes
TL;DR
* An open-weight model is a model whose trained parameters are published for download, which is not the same as open source: training data, training code, and commercial terms can all stay restricted.
* Alibaba published the Qwen3.8-Max weights as Qwen3.8-2.4T-A95B in August
Top 5 AI Gateways for AI Cost Optimization in 2026
Five AI gateways compared on budgets that block requests, token-aware rate limits and the cost metrics teams can build spend alerts on.
Top 5 Tools for LLM Cost and Usage Monitoring
Bifrost, LiteLLM, Langfuse, Datadog LLM Observability and Weights & Biases Weave compared on cost attribution, budget enforcement and deployment.
Enterprise AI Security: The Controls That Apply to LLM Traffic
TL;DR
* Enterprise AI security is the set of controls that govern how an organization's applications, users, and agents send data to and receive data from language models.
* LLM traffic is a distinct security surface: prompts can carry sensitive data out, responses can carry it back, and agents