Over the years vendor security reviews have exploded. Fueled by GDPR, then remote work, now AI. Today, Enterprise companies aren’t buying your software without a pretty intense security review.
On the other hand, the Information Security teams at software companies that manage these reviews aren’t growing at all. If anything, they now have more responsibilities.
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The solution is AI. AI can scale the manual knowledge work of responding to questionnaires. Well in theory at least. When it comes to security information and answers it’s a risky bet. Most AI indexes too much, guesses when it shouldn’t, and doesn't show its work. Not great if you’re facing an audit.
It can be done. The Security Assurance team at Sprout Social uses AI to automate 98% of security questions. The first pass rate of the AI is almost 90% accurate. This has eliminated 200 hours a year of human manual security question processing
How can you safely and effectively use AI to respond to vendor security reviews? Let’s break the problem down.
Applying AI to this workflow isn’t a single problem. It's three:
- The quality of the knowledge
- How it's retrieved
- The context of who's asking
Most companies only tackle one and the result is a system that fails. Let’s look at a real example.
The problems in the problem
Sprout Social is a powerhouse enterprise software company. Four product lines with over 40 integrations, a global customer base, and security and compliance expectations that are anything but simple. They were experiencing all three of these problems at once.
As a result when it came to answering security questions, sales just did things themselves. This caused issues. Sales reps kept local copies of old answers to respond to customers. Others found information from an old wiki. There was no single source of truth, no version control, and no visibility into what customers saw.
Bad knowledge, poorly retrieved, no context of what it’s being used for.
In this world at least 25% of customer security questions made their way back to the GRC desk. Even with a team of 30 solutions engineers working on security questionnaires. With Conveyor, that numbrer came down to 2%. Here’s how they attacked the problem.
For automation to work, three things must be true:
1. Knowledge is curated, not collected
Security knowledge must be curated, not accumulated. Patrick, the Security Assurance Manager at Sprout built a knowledge library with Conveyor. A sort of GRC brain that uses AI to stay automatically updated. This new security knowledge library now draws from approved Q&As, help center documentation, and past questionnaires across all four product lines. Since these are living sources, knowledge gets updated as the product evolves. Without ongoing curation, the AI reflects the decay. Bad inputs equal bad output that erodes trust with the teams using it.
2. Precision at retrieval
A well-maintained library fails if the AI using it “roams freely” through it. Conveyor is purpose built for security use cases. Patrick is in control of what the AI pulls from, how it’s used and can monitor the result. His team controls AI access down to specific Confluence spaces, scoped by product line. That precision prevents bad answers that aren't grounded in Sprout's actual security posture. As a result their first pass answer rate is almost 90%, a number the team at Sprout is thrilled with given the complexity of their organization. Conveyor tells them the confidence level of answers making it easy to flag issues and improve the system over time.
3. CRM powered context
Without CRM context, customer trust AI solutions and trust centers are basically just a document library. Using Conveyor’s out of the box Salesforce integration Patrick integrates CRM data into the system.
When a user hits the trust center they are prompted to request access. Conveyor then checks their status and responds accordingly. NDAs are handled automatically for known contacts. Unknown visitors trigger a Slack alert to sales. GRC is now able to surface sales signals from the security workflow.
When GRC stops being the help desk
Before Conveyor, Sprout's GRC team was a de facto help desk. Fielding escalated questions and verifying information for solutions engineers. Reactive work that’s invisible to the business.
Now they're working on EU regulatory changes and supply chain risk. Work that actually makes the company more secure, not just more responsive.
"Our team can get one-off answers on their own in seconds," Patrick says. "Conveyor's AI search pulls together related resources and delivers an accurate answer to any question we throw at it."
The SE team made a similar shift. They spend less time drafting and more time talking to customers. That's what automation is supposed to look like. Not a faster queue. No queue.
Where to start
If you’re ready to use AI to scale your vendor security review program, great. But keep in mind what the team at Sprout Social faced. Generic AI isn’t going to cut it here. You need something purpose built for you, a Customer Trust Platform that can deliver an accurate, connected, AI solution.
Before evaluating platforms, map what you actually have. Which docs are current? What product lines have solid Q&A coverage? Knowledge is the foundation for AI. That audit will tell you more about your readiness than any vendor demo.
Fix the knowledge problem first, then look for a solution.
If your security team is ready to make that shift, we'd like to show you what it looks like in practice. Talk to us →
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