Snyk Innovation & Technology Culture

How does your team use AI in the engineering design process, and what benefits have you observed? How does Snyk support and encourage you to use AI safely?

My team works with our product management and engineering teams to design new features, improve existing ones and write technical documentation for Snyk products. 

Snyk encourages the safe and effective use of AI. We have a dedicated AI governance team that guides us on risks and opportunities. We use AI to make our workflows faster and more efficient. During brainstorming sessions, AI helps us organize and evaluate ideas. It also acts as a creative partner during ideation, suggesting alternative solutions that keep us focused on customer needs. 

On the documentation side, we are piloting AI to generate the first draft. We want to better understand if we can accelerate creation of high-quality and accurate documentation of all Snyk product features. 

Finally, we’re using it for some of the most impactful solutions, such as fixing security. 

Snyk’s balanced approach to AI, which encourages engagement and experimentation with the technology while maintaining appropriate governance and education, is key to its continued adoption in the company.

What challenges have you encountered when implementing AI technologies in engineering design, and how have you addressed these issues?

Like any new technology, AI brings excitement and uncertainty. On my team, there are varying levels of fluency with AI tools. Externally, the hype around AI often exaggerates what it can achieve. 

One challenge we faced was identifying meaningful use cases and trusting AI-generated results. For example, AI can hallucinate, leading to inaccurate conclusions about user needs. To address this, we leveraged our internal large language model for insights on select interview data that had already been manually analyzed. The LLM captured broad themes accurately but fabricated some customer quotes. To fix this, we refined the prompts and now use the LLM only for identifying high-level themes. Qualified team members still review and validate all insights. 

Another challenge was using AI for design solutions. While generative AI can produce numerous iterations from a single prompt, it often misses the mark on creating specific, usable designs. For example, I couldn’t arrive at the intended design idea solely through AI prompts. We’ve learned that AI works best for freeform brainstorming, but designers still refine these ideas into solutions that align with the product’s look and behavior. AI is a valuable tool, but human expertise remains essential to ensure quality and alignment. 

What excites you most about your team’s future when it comes to leveraging AI in innovative ways?

AI’s ability to retain context and process vast data across domains allows us to make faster, more informed decisions. As the technology evolves, I see it enabling a more human-centric and efficient way to interact with systems. For instance, instead of writing complex queries to understand security vulnerabilities, customers could simply use prompts to get faster, more precise results. 

AI will transform our internal workflows. It can detect bottlenecks early on and optimize team processes. It can ensure that design and technical documentation is accurate, high-quality and compliant with organizational standards. By flagging potential communication gaps or problems early on, AI will help us address challenges proactively. 

With proper governance, AI can enhance efficiency while safeguarding against risks. I see AI as a partner, not a replacement, for our teams. I am excited to see how empowered teams will leverage AI responsibly to accelerate innovation at Snyk and across the industry.

What’s your rule for fast, safe releases and what KPI proves it works?

The key to fast, safe releases is frequency — which is also a useful KPI. The more frequent your releases, the less risk there is in each one and you exercise your release process and infrastructure end-to-end each time as well. This also improves your speed (latency to production).

I’ve found that infrequent releases, even with a highly disciplined engineering team, are very difficult to consistently land for systems of any real complexity.

Forcing yourself to do frequent releases (for back-end systems, weekly is what I’ve found to be ideal) will force investment in all other parts of your release process to make them high-quality, automated and reliable. If you’re able to consistently stick to those releases, and aren’t frequently having incidents in production, that’s a strong signal that everything is working well. We care deeply about developer trust — a release process that causes stress isn’t sustainable.

In addition to release frequency, we also look at change failure rate and time to restore service, because they tell you whether you’re shipping fast and safely.

A key requirement for frequent, predictable releases is rollback safety; releases should always be rollback-safe, ideally for more than one version. This requires a modest amount of engineering discipline, but I’ve found that if you’re thinking about change management and APIs correctly, rollback safety becomes a natural outcome.

Ultimately, it only works if it’s a team sport — product, platform, security and engineering all aligned around the same goal: shipping quickly while maintaining trust.

What standard or metric defines “quality” in your stack?

Quality, for me, boils down to three key metrics. We define quality through outcomes, not opinions.

Any engineering project needs a balanced diet of these, though it will often be correct to trade off one for another based on business needs. For example, by trading reliability for velocity on an early exploratory PoC; or the reverse on a core infrastructure component that is seldom changed and must be stable.

One of the best ways to drive quality is to have this conversation explicitly. Engineering is not about building the best thing — it’s about building the optimal thing, with constraints like cost and time-to-market as critical levers. Be explicit with your quality goals up front, what you’re optimising for and don’t be afraid of changing those goals over time.

Name one AI/automation that shipped recently and its impact on your team or the business.

One of the most exciting parts of my job right now is seeing the experimentation with different workflows as the tools evolve day to day. AI tools are incredibly powerful, but we’re all still figuring out how to use them safely and effectively — and we think that’s exactly where the industry needs to be: moving fast, but responsibly.

We launched Snyk Studio last fall to address this gap with AI-driven development. Snyk Studio helps ensure code is secured the moment it’s generated, before a human even needs to review it, no matter what tool your developers use.

We’ve rolled out Snyk Studio to all our engineers internally and received great feedback from the team on where it has improved their workflows and how to integrate it seamlessly. Getting that early internal feedback was crucial to the launch, and it means our developers are actively contributing to Snyk’s AI security future each time they write code.

The goal isn’t AI for AI’s sake — it’s helping teams ship faster without compromising security, and making secure development feel natural in day-to-day work.