Filling out security/vendor questionnaires

The AI agent accepts an incoming security questionnaire in any format (Excel, Word, web form, PDF), extracts the questions, searches for answers in the corporate knowledge base, and returns a ready draft with source citation for each row. The company receives the first version of the completed questionnaire in minutes instead of days, and Legal & Compliance focuses on non-standard items instead of repeatedly copying standard answers.

What automation does

1. Accepts the questionnaire in Excel, Word, PDF, or via CSV export from vendor review web portals.
2. Parses the structure — extracts numbers, question text, answer types (yes/no, free text, multi-choice).
3. Classifies questions by topic: encryption, access controls, SDLC, incident response, sub-processors, data residency.
4. Searches for relevant context in the knowledge base — previously completed questionnaires, policies, SOC 2 / ISO 27001 reports, DPA templates.
5. Generates a draft response with source citation: "See Security Policy §4.2" or "From the SIG 2025 Q1 response".
6. Flags uncertainties — questions where the model found no exact answer or a legal decision is required are flagged as "requires manual review".
7. Produces the final file in the original format (Excel with the original structure, Word with forms) — ready for review.
8. Saves responses to the knowledge base after approval, so the next questionnaire is completed faster.

What automation does NOT do

• Does not sign commitments on behalf of the company. Responses remain a draft until explicitly approved by an authorized employee.
• Does not replace legal expertise for non-standard questions. Contractual terms, regional compliance requirements, and new regulations require a human.
• Does not guarantee passing vendor review. Answer quality depends on the completeness and currency of the knowledge base — outdated policies produce outdated drafts.

The technical architecture relies on the RAG Q&A pattern: a vector knowledge base with embeddings of corporate documents, a retrieval layer for finding relevant chunks, and an LLM for generating a response based on the retrieved context. Integration works via file storage — an incoming questionnaire lands in a shared folder, the AI agent picks up the file, processes it, and returns a draft to the same folder.

Data flow

1. Corporate base indexing. All relevant documents — past completed questionnaires, security policies, audit reports, DPA, architecture diagrams, presales materials — are converted into chunks and loaded into the vector store with metadata (document type, date, section).
2. Parsing the incoming questionnaire. The agent recognizes the file structure: Excel tables, numbered Word questions, PDF fields. It extracts «question_id → question_text» pairs.
3. Classification and routing. Each question receives a category tag (access-control, encryption, incident-response, data-handling, etc.) and is routed to the corresponding knowledge base subsection to narrow the search.
4. Retrieval. Based on the question text and category tag, a semantic search is performed — returning the top-N relevant chunks with source and confidence score.
5. Answer generation. The LLM takes the question plus the retrieved fragments and generates a response in the required format (yes/no + justification, free text, document reference).
6. Flagging uncertain items. If retrieval found no relevant context or the confidence is low, the response is flagged «REVIEW REQUIRED» with an explanation of what exactly is unclear.
7. Final file assembly. Answers are inserted back into the original template, preserving the formatting and question numbers.
8. Review loop. A lawyer or security engineer reviews the draft, corrects the flagged questions, and approved answers are returned to the knowledge base to train subsequent iterations.

Key components

Implementation steps

1. Collect the document corpus — 10-30 of the most recent completed questionnaires, current policies, audit reports, DPA. This is the foundation of retrieval quality.
2. Configure the file storage trigger — the folder where a new questionnaire lands initiates processing.
3. Define the question taxonomy — 15-25 categories covering typical SIG/CAIQ sections.
4. Connect an LLM with compliance in mind — for sensitive data, a self-hosted model or a provider with a signed DPA/BAA is chosen.
5. Run a pilot on 2-3 recent questionnaires — compare with manual completion, measure the share of auto-responses and errors.
6. Configure the review interface — at minimum a table with a confidence column and an approval button.
7. Go live — connect to the inbox where questionnaires arrive, and establish a review SLA.

To launch automation, you need access to documentation, a basic agreement on the review format, and a sample of past questionnaires — the more complete the corpus, the fewer responses will require manual review.

Data and access

• Corpus of past questionnaires — at least 5-10 completed questionnaires from the past year (SIG, CAIQ, or custom).
• Security policies — information security, incident response, access control, data handling, SDLC.
• Audit reports — current SOC 2 Type II, ISO 27001, PCI DSS (if applicable).
• DPA and sub-processors — DPA template, current list of sub-processors, data processing regions.
• File storage — a shared folder where incoming questionnaires are placed and drafts are returned.
• LLM provider with compliance in mind — for sensitive data, a self-hosted model or a cloud provider with a signed DPA and BAA is selected.

Team readiness

• Process owner — a lawyer or security engineer who approves the final responses.
• Technical support — 1 engineer or external contractor for pipeline setup and review interface configuration.
• Knowledge base update rules — an agreement on who adds new policies and approved responses after each review.

Timeline

The base version (file storage + RAG + review table) is deployed in 1-2 weeks. The first pilot on a real questionnaire — within the first week. Taxonomy refinement, integration with a specific vendor portal, and prompt calibration — another 2-4 weeks after the pilot.