Rolling Out AI Coding Assistants: How Drata Did It

AI has raised the bar

With AI accelerating across every stack within Drata, CTO, Daniel Marashlian, saw the stakes clearly: every engineering org now has to move faster — without eroding quality or trust.

But AI wasn’t seen as a replacement for engineers. Instead, Daniel framed it as a new programming model: LLMs are the runtime; prompting is the syntax.

The real prize? Enabling 200+ engineers to ship faster, without compromising compliance or quality. That meant evaluating vendors on concrete use-case wins, not hype.

What follows is the playbook Drata used to hit that productivity-plus-security target—and how your engineering org can adapt it.

Why it matters

Executive urgency is real and measurable.
Engineering leaders who wait will inherit unmanaged shadow-AI usage.
A structured rollout beats a scatter-shot “just go get a tool” approach every time.

Snapshot: Inside Drata’s Engineering Org

Drata is a fast-growing AI-native Trust Management platform with 200+ engineers across three regions.

Key context:

Security DNA. As a cybersecurity company, every new tool must clear stringent ISO 42001-mapped controls.
Exploding surface area. Thirty frameworks, six product lines, and hundreds of customer requirements drive complexity.
Talent mix. From AI skeptics to AI engineers, skills and natural interest vary on the team.

Call-out: Legacy is a privilege. Maintaining a code base that serves 8,000+ customers while shipping new features meant Drata needed tooling that adds leverage, not just novelty.

Five Filters Drata Applied Before Buying

Criterion	What Drata Looked For	Field Note
Accuracy & context-depth	Does the model nail edge-case paths?	“The main theme … was the accuracy of code results.”
IDE fit & workflow friction	Runs where engineers already work.	Some engineers rejected solutions that forced a new IDE.
Security/compliance posture	ISO 42001 alignment, data boundaries, AI architecture transparency.	Deep questionnaires probed multi-tenant vs. isolated models.
Partner support & onboarding rigor	Slack-native customer success and co-development mindset.	“Customer success culture” was a decisive tie-breaker.
Agent-level capabilities	Beyond autocomplete: test generation, planning, refactoring.	Unit-test coverage “improved instantaneously” with agent mode.

The Seven-Vendor Bake-off

Drata refused to guess its way to a winner. Instead, it orchestrated a 30-day, seven-vendor sprint:

Define use-cases. Boilerplate generation, complex unit tests, cross-service refactors.
Select pilot cohort. Two pods (5-10 volunteers) per vendor — “if you’re in, you’re all in.”
Mandate daily usage. Engineers had to lean on the assistant for every eligible task.
Pressure-test with questions. Cohorts peppered vendors to see how the relationship would fare post decision.
Security review in parallel. Each tool ran the full ISO-aligned questionnaire.
Consolidate findings. Accuracy scores and partner engagement determined the finalist.

Pro tip: Drata’s bake-off ran in parallel to compress calendar time yet preserved an apples-to-apples cohort test.

Driving Adoption: Three Change-Management Levers

1. Hard-wired OKRs AI adoption became an explicit engineering objective. Success metric = every engineer touches the assistant at least once per week during the first quarter.

2. Champion Channel A public Slack channel called #wins-ai showcases screenshots of successful prompts and performance gains. Roughly half the posts now come from engineers using the coding assistant.

3. Education Push Engineers self-select Coursera, internal workshops, or vendor-led sessions, then log completed learning in a central tracker — “AI literacy is non-negotiable.”

Early Wins That Moved the Needle

Unit-test turbo-boost. Complicated mocking suites that once took hours now materialize in minutes, lifting coverage of neglected paths.
Boilerplate acceleration. Agents scaffold services fast enough to “reduce the majority of setup time by a huge factor”.
Slack brag file. Dozens of #wins-ai posts document 5-10× task speed-ups each week — priceless for momentum.

“At some point I realized: this is going to transform our engineering process.” — Tony Bentley, Staff Engineer

What Drata Would Repeat — Checklist for Your Team

Scope first, shop second. Tie vendor demos to your real use-cases.
Run a multi-vendor cohort test with mandated daily use.
Log accuracy, not just adoption. False positives erode trust faster than no suggestions.
Pull security in on day one; the questionnaire will only grow.
Demand tight customer success loops. Async Slack > weekly QBRs.
Elevate AI literacy. Treat prompting like a new programming language.
Celebrate wins publicly to normalize usage and surface best practices.

Closing POV: AI Literacy Is the New Git Literacy

Coding assistants are no silver bullet, but they raise the baseline for productivity and quality. Drata’s experience shows that world-class teams treat AI like any critical runtime: they evaluate rigorously, adopt intentionally, and teach continuously.

“LLMs are just another language; prompting is the syntax. Master it or fall behind.” — Jono Stiansen, AI Engineer, Drata

Ready to start? Draft your evaluation plan, pick a pilot cohort, and schedule your first vendor gauntlet. Treat AI like learning a new skill or tool, not a magic wand. And don’t forget to celebrate your wins.