AI Browsers Explained: What They Do and the Settings to Check
AI browsers explained: sidebar assistants vs agent modes that click and fill forms, what they can see, and the privacy settings worth checking.
New AI tools explained from official sources, and how AI systems get attacked and defended.
AI browsers explained: sidebar assistants vs agent modes that click and fill forms, what they can see, and the privacy settings worth checking.
How to ship LLM features safely: version prompts and model IDs, gate releases on evals and red-team tests, secure the model supply chain, and roll back.
What the Model Context Protocol is, how MCP servers and tools get abused, real CVEs, and the fixes: allowlists, pinned versions, narrow scopes, approval.
How OpenAI secures frontier models and business data: Preparedness Framework, SOC 2 and ISO scope, retention, ZDR, and what isn't verified.
A layer-by-layer map of an AI agent's attack surface, from prompt to credentials, with the controls, OWASP and ATLAS IDs and logs each layer needs.
What Claude Code, Continue, Cursor and GitHub Copilot send out, the admin controls that limit them, and the repo rules that still need a human.
An AI model router puts one API in front of many models. What Bedrock, Azure, Vertex AI, OpenRouter and LiteLLM see, keep, and where your data goes.
Anthropic security from its own pages: ASL-3 weight protection, ISO and SOC 2 claims, data retention, zero data retention, and what's unverified.
What an AI agent is, why letting AI act brings new security risks, and the guardrails, vendor questions and 10-point checklist a manager needs.
What on-device AI means, why it helps privacy, and when Apple, Microsoft, Google and local AI apps still send your data to the cloud.
Prompt injection in plain language: how hidden instructions trick AI tools, why AI agents raise the stakes, and five defences any manager can use.
Shadow AI at work is AI your team uses without approval. How to find it in consent, network and expense records, and what to offer instead.