How Vanta Builds with AI, with VP of Engineering Iccha Sethi

Vanta helps companies like Atlassian, Cursor, Notion, and GitHub automate security and compliance. We spoke to Iccha Sethi, their VP of Engineering, to learn how her team is deploying AI in their product and across their internal engineering workflows.

Here are the highlights from our conversation.

AI as a co-pilot, not a replacement

Vanta’s engineers use AI across multiple parts of the dev cycle - from test generation to writing RFCs and postmortems. But Iccha believes AI’s value today is in augmentation, not automation. 

Tools that do code generation can be a hit or miss. Real-world codebases have years of patterns baked in - and AI tools aren’t yet thinking about how a team wants to uplevel or evolve those patterns.

Instead of expecting AI to replace code gen, Iccha’s team treats it as a thought partner: helping them brainstorm, debug, and document faster. The near-term impact is felt in speed and clarity, not yet in fully automated coding.

Where AI drives real velocity gains: testing and communication

When we asked Iccha which part of the dev cycle AI is most helpful for, she mentioned testing and communication.

We use a third-party vendor to generate unit tests for older parts of our codebase to improve test coverage - and AI’s been great for that.

ChatGPT is also a great brainstorming partner. I can start with a few bullets, and it helps me flesh things out or spot gaps.

By focusing AI on repetitive, but essential tasks - like test generation and documentation - the team compounds small efficiency wins without compromising quality.

How AI has changed Vanta’s hiring process and priorities

AI is changing how Vanta hires and evaluates engineers. Given how fast the rate of change is, Iccha thinks engineering leaders should almost disregard whether a candidate knows the latest frameworks, and focus on how open they are to learning and experimenting.

The biggest thing I’m looking for in new hires is how willing they are to adopt and try new tools. Growth mindset has become more important than ever.

There are people who say, every new tool is a distraction. I need more open-mindedness. Let's try it, and if it doesn’t work, fine.

According to Iccha, the best engineers are curious enough to explore, but disciplined enough to not chase every shiny object.

Vanta has also changed their interview process to align with the times, given that every engineer now has AI copilots at their disposal. 

We’re discussing how to make our interviews reflect the real world - where engineers have access to AI tools. The idea is to let candidates use Cursor or Copilot during interviews and evaluate how they leverage them.

The company is also exploring ways to prioritize code review over code generation. 

Code review becomes a more important skill than ever with AI code generation.

As more code is written by machines, human judgment: deciding what’s good, safe, and maintainable becomes the differentiator.

AI helps engineering leaders get close to the codebase again

AI has brought engineering leaders like Iccha closer to the codebase by freeing up their time and taking care of mundane, repetitive tasks. 

“Cursor empowered me to be closer to the code and more self-sufficient. Over my winter break, I built a TypeScript program to analyze incident postmortems with LLMs - something I’d normally ask an engineer to do.”

AI gives leaders hands-on visibility into problems they’d otherwise have to delegate. That creates tighter feedback loops between management and execution.

Velocity depends on seniority

According to Iccha, AI’s impact varies based on experience level. Iccha thinks AI makes senior engineers faster, and could actually make junior engineers slower. 

The more senior the engineer, the more innovative they are about how to leverage AI to unblock themselves. For junior engineers, sometimes the opposite happens - they don’t yet know what ‘done’ or ‘good’ looks like, and so AI can actually slow them down.

She also mentioned how senior engineers in her team lead by example, documenting AI workflows, sharing prompt libraries, and mentoring juniors on how to verify rather than blindly trust AI output.

Takeaways for engineering leaders:

• Start narrow: Deploy AI where the ROI is obvious. In Vanta’s case, this was testing and documentation.
• Hire for curiosity: Growth mindset is a better predictor of success than AI tool fluency (which is bound to shift over time)
• Redesign interviews: Test for fundamental technical acumen, but also let candidates use AI and evaluate how they prompt and review. 
• Invest in review culture: Great engineers spend more time auditing code than writing it. This will become increasingly critical as machines write most of our code. 

Vanta’s approach shows what it looks like to integrate AI into an org with a strong engineering culture. Done right, AI can help engineering teams become thoughtful, fast, and self-sufficient.

We had a great time jamming with Iccha, and hope her playbook helps leaders in their organizations.

You can watch our full conversation with Iccha on YouTube. You can also check out previous conversations with product and engineering leaders at Intercom, Monday.com, Vercel, and more.