Building your own GRC platform is easier than ever, thanks to Claude and other tools like it. So…should you?
Your team already pays for a general-purpose AI solution. Your engineers are sharp, the models are capable, and the workflows that eat your GRC team alive—security questionnaires, vendor assessments—look like exactly the kind of document-in, answer-out task these tools excel at. So when someone asks why you'd buy a purpose-built solution the instinct is fair: we can build this ourselves.
You can build a prototype—maybe even between now and the end of the day tomorrow—and have fun doing it. It’s exactly the type of project that puts the “vibe” in “vibe coding”. But there’s nothing vibes-based about a program you'd defend in an audit. Trust work has consequences a general-purpose model was never designed to solve—and requires proof that your go-to chat buddy can’t produce.
What a General-Purpose AI Tool Gives You + What It Doesn't
An LLM is a reasoning tool. Point it at a document and it will summarize and draft fluently. That handles the easy part of both use cases covered in this post. More advanced AI tools like Claude Cowork and Claude Code can go even further, drafting multi-step workflows, identifying new ways to tackle a problem, and turning over finished artifacts that look polished and complete.
What these tools can’t do—yet:
- Ground every answer in your documented posture rather than patterns learned from the open internet.
- Build institutional memory, so approved answers compound instead of every questionnaire starting from zero, while avoiding decay or drift.
- Keep your data inside your perimeter, so customer intake forms and vendor evidence never get pasted into a third-party model.
- Produce an audit trail to prove what was said, to whom, on what date, sourced from which document.
Challenge One: Answering Security Questionnaires
The pitch for spinning up your own questionnaire-answering machine on your general-purpose AI solution is compelling. Build a knowledge base from previous answers and your existing documentation. Paste the questionnaire in. Get answers back.
The problem? Data decay starts on day one. The moment you upload your security documentation into a chat tool, it becomes a stale copy that drifts from your Trust Center and your knowledge base. To avoid this problem, you’ll need to connect your AI tool via MCP or API to a knowledge base that’s kept up-to-date, like we recommend to our customers.
Then there's the confidence problem. Generative AI is built to generate text that sounds right. A well-formatted answer that misstates your data retention policy or claims a certification you don't have reads exactly like a correct answer—until it surfaces in a customer's due diligence review six months later.
Answering pipelines that achieve 90% accuracy or greater take months to build and constant maintenance—so your GRC team goes from spending hours every week answering questionnaires, to spending even more time fiddling with your answer automation.
A purpose-built system closes those gaps by design. Drata's AI Questionnaire Assistance drafts answers from your uploaded documents, Trust Center content, and past approved responses, then tracks every response with each answer traceable to its source document. Review becomes a quick approve-or-edit on a sourced draft.
Plus, our engineers devote serious time to maintaining the AIQA answer pipeline and improving it so you get more accurate answers faster. GitLab cut security questionnaires by half year over year working this way; Instacart's team recovered hundreds of hours it used to spend answering them by hand.
Challenge Two: Assessing Third-Party Risk
Vendor risk exposes the deeper limitation. Summarizing a vendor's SOC 2 report is the part an LLM does well. Running a third-party risk program is a different job.
Ask an LLM to evaluate a vendor and the output depends entirely on how the prompt was phrased and how the reviewer reads the response. A TPRM program needs four things an LLM can’t supply:
- A persistent criteria baseline, so each vendor gets judged the same way
- Structured outcomes you can compare across a portfolio
- A chain from evidence to requirement to decision, which is exactly what auditors ask for
- Inherent and residual risk plus an approval workflow, so governance stops living in spreadsheets
That’s what Drata's Third-Party Risk Management is designed to provide. Its Agentic TPRM Assessment evaluates vendor evidence against predefined criteria baselines and returns structured outcomes—Met, Partially Met, Not Met, or Inconclusive—with each finding mapped to the specific evidence behind it. That gives you full traceability from evidence to requirement to outcome to decision. The agent handles collection and analysis while generating a paper trail at each step. People still make the call on residual risk and approvals, which keeps accountability where auditors expect it. Because the assessment runs on Drata's Trust Graph, a network of enterprise Trust Centers, your team pulls real vendor-provided documentation for high-value vendors and subprocessors instead of chasing evidence over email.
The Real Question Isn't "Can We?" It’s “Should We?”
Your team can vibe-code something for either workflow. The question you actually face is whether maintaining a vibe-coded GRC platform is the best use of your engineers’ and your GRC team's time.
It’s relatively quick to produce a prototype with AI. On the other hand, a program is a standing commitment: keeping a knowledge base synced and conflict-free, tuning retrieval so the hallucination rate stays low enough to put in front of a customer, building review and routing interfaces, wiring an audit trail, and defending all of it when a regulator or a large prospect asks how a decision was made.
There's a real case for building. A large engineering org with a genuinely unique workflow and the appetite to run this like a funded, roadmapped product can own the toolchain long-term. Most teams aren't signing up for that. They need the security review off the critical path this quarter, and they need their engineers on the roadmap for the product their company actually sells.
General purpose enterprise AI is a good tool for general work. On its own, it is not a security questionnaire system or a TPRM system, any more than it is a system of record for your contracts. Weigh the choice on defensibility, not on perceived sticker price. A drafting shortcut that can't prove its own answers becomes a liability the first time one gets challenged in an audit or a deal.
Winning With Trust
Drata brings questionnaire response and third-party risk into one system built for the accountability trust work demands: answers and assessments grounded in your documented posture, structured and traceable outcomes, an audit-ready trail, and human judgment on every final call. Your team reviews and decides. The platform does the repeatable work and keeps the proof.
See how Drata helps you run security reviews and vendor risk without building the plumbing yourself. Request a demo.