AI browsing security and prompt‑injection protection covers the methods, platforms, and controls used to keep browsing‑enabled assistants from being manipulated by malicious web content or inadvertent data leakage. This topic examines Perplexity BrowseSafe alongside alternative approaches from model and platform providers to harden web‑grounded LLMs, secure retrieval‑augmented workflows, and ensure provenance and governance for real‑time answers. It is timely in late 2025 because assistants increasingly combine live web access, multimodal models, and tool execution—expanding the attack surface for prompt injection, credential exfiltration, and hallucination from untrusted sources. Enterprise adoption and regulatory scrutiny have pushed vendors to build defenses into both model stacks and orchestration layers. Key tools and categories: Perplexity AI (web‑grounded answers and developer APIs; BrowseSafe represents per‑query browsing protections and citation emphasis), Anthropic’s Claude family (safety‑focused conversational models), Google Gemini (multimodal models and APIs with managed deployment), IBM watsonx Assistant and Kore.ai (enterprise agent platforms that prioritize governance, observability, and policy controls), Cohere and Mistral (private/customizable or open models for controlled deployments), Vertex AI (unified deployment, monitoring, and policy enforcement), and content tools like ChatPDF (document ingestion with citation that affects RAG security). Common mitigations include input/output sanitization, retrieval filtering and source scoring, provenance and citation plumbing, sandboxed tool execution, policy enforcement layers, role‑based access and secret scanning, and observability for runtime detection of anomalous prompts. When evaluating solutions, prioritize transparent provenance, integration with enterprise governance, configurable retrieval and tool access, and measurable false‑positive/negative behavior—tradeoffs that shape practical prompt‑injection protection in production.