Brave Software has introduced Ask Brave, a new interface that merges traditional web search with a generative AI chat in a single workflow. The service is free, accessible from any browser at search.brave.com/ask, and emphasizes privacy by default. The design aims to bridge the gap between classic “ten blue links” and long-form LLM answers, keeping users anchored to verifiable sources.
What Ask Brave Is: Unified Search and Generative AI
Ask Brave complements, rather than replaces, Brave’s existing AI Answers summarizer, which the company reports generates over 15 million answers per day. The new experience ties web results to contextual AI responses to reduce tab switching and manual copy-paste from search results into a separate chat.
How It Works: Triggers, UX, and Deep Mode
Users can initiate Ask Brave in several ways: append a double question mark “??” to a Brave Search query, click the Ask button on search.brave.com, or open the Ask tab from the results page. This flow lowers friction for blended search and conversation, helping users move faster from problem statement to answer and sources.
Two response modes are available: Standard and Deep. Deep mode performs multiple search iterations and aggregates more sources, improving coverage for complex tasks. According to Brave, responses are consistently grounded in the web to reduce hallucinations and irrelevant claims.
Under the Hood: Retrieval‑Augmented Generation (RAG)
Ask Brave follows a retrieval‑augmented generation pattern: the model composes answers based on retrieved documents rather than solely on its internal parameters. For users, this means greater transparency and auditability—key statements can be compared with cited sources directly from the results.
Data Protection: Encryption, Retention, and IP Practices
Brave highlights several safeguards: Ask Brave chats are encrypted, messages are not used to train models, and chats are deleted after 24 hours of inactivity. Brave also states that Brave Search does not log IP addresses, limiting the ability to correlate sessions with individuals. These measures align with data minimization practices and reduce privacy risk exposure.
It is important to note that server-side encryption is not the same as end-to-end encryption (E2EE); the service can still access content while processing it. Privacy outcomes also depend on user behavior, device hygiene, and network security.
Market Context: Generative AI in Search, with Privacy by Default
Embedding conversational AI into search is now a mainstream direction, seen across the industry. Brave’s approach distinguishes itself with privacy-by-default postures and clear role separation: AI Answers for quick summaries and Ask Brave for interactive, multi-step problem solving. This architecture maps well to diverse user scenarios, from fast fact-finding to deeper research.
Security Risks and Operational Limitations
Even with RAG, risks remain. Potential issues include LLM hallucinations, biases introduced by sources, and prompt injection via malicious or manipulative page content. The OWASP Top 10 for LLM Applications lists prompt injection and data leakage among key threats, underscoring the need for defense-in-depth and source scrutiny.
Practical Security Tips for Using Ask Brave
Do this: verify claims against cited sources; reformulate complex queries and use Deep mode for completeness; avoid sharing personal or payment data in chats; clear history and sign out on shared devices; and consider network protections such as DNS filtering or DNS over HTTPS (DoH) to limit metadata exposure.
Ask Brave illustrates how AI chat can be combined with transparent search workflows while respecting privacy. Users who value verifiability and minimal data collection should test the service with their own scenarios and establish internal verification routines. This approach reduces risk, improves decision quality, and helps teams capture the benefits of hybrid AI–search tools without compromising security.
