For SaaS and software companies

Your team is already using AI. The only question is whether you're managing it.

AI is already inside your codebase: in the PRs it drafted, the tests it generated, the customer data an engineer pasted into it to debug faster. Tienta helps SaaS and engineering teams get in front of it: understand where it's already being used, close the exposure, and put it to work deliberately instead of accidentally.

The short version

Most engineering teams already have unmanaged AI use inside them: an engineer running customer data through a public chatbot to debug faster, or shipping AI-generated code nobody has a documented process for reviewing. The obligations already cover it, just not from a licensing board: SOC2 trust-service commitments, customer data processing agreements, and ordinary trade-secret protection over source code all already govern how that data and code can be handled, whether or not engineering has written a policy. Tienta closes the gap between those obligations and what's actually happening in two phases: an AI Risk & Readiness Audit that documents actual use and produces a team-specific policy, then ongoing implementation of the tools that make the team faster.

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This isn't hypothetical. It's already happening on your team.

Somewhere in your codebase right now, an AI tool has probably touched a PR, a test, or a customer support ticket, whether or not you've sanctioned it. That's not a guess; it's the predictable result of powerful, free tools meeting sprint-deadline pressure. The only real question is whether it's happening with guardrails or without them.

And the gap between concern and readiness is already measured. A survey of 307 senior technology leaders, split roughly across CISOs, CTOs, and CIOs, found 93 percent at least somewhat concerned about AI-generated ('vibe-coded') code reaching production without governance, while only 8 percent describe their organization's internal AI tool governance as strong. The concern is nearly universal. The governance closing that gap isn't.

Source: State of AI Governance Report 2026, Retool, June 2026.

The obligations already exist. They just don't come from a licensing board. Software companies don't answer to a single professional-conduct body the way law firms or CPAs do, but the obligations are just as real: the SOC2 trust-service commitments your customers already rely on, the data processing agreements that specify exactly how customer data can be handled, and the ordinary duty to protect your own source code and trade secrets. An engineer pasting customer data or proprietary code into an ungoverned AI tool can violate all three at once, whether or not anyone wrote a policy telling them not to.

In plain terms: your SOC2 commitments don't pause because an engineer pasted a customer's data into a public chatbot to debug faster. Your DPA's data-handling terms don't pause because nobody told engineering which tools are actually approved. The obligations already apply. What's usually missing is a policy that tells your people how to meet them.

What's actually at risk

Three things, in order of how often they actually bite engineering teams:

Source code and customer data leaving your walls

Public AI tools can retain, train on, or expose whatever gets typed into them. In 2023, Samsung engineers pasted proprietary source code and internal meeting notes into ChatGPT on three separate occasions, information the company had no way to retrieve afterward. Multiply that by every engineer who's found their own AI workaround, and you have an invisible, unmanaged data-exposure surface with no record of what left the building or where it went.

Unreviewed AI-generated code reaching production

AI coding tools produce plausible, confident code that's sometimes wrong in ways that don't show up on a quick read, including real security vulnerabilities. As more of a codebase gets AI-assisted, “who actually reviewed this, and how” stops being obvious from the commit history unless a team has decided that in advance.

No record that you did anything about it

If a data-exposure or security issue does surface, the difference between “isolated engineer mistake” and “systemic failure of oversight” is often a written policy, an approved-tools list, and a documented review process. Teams with nothing on paper carry the exposure of the whole company on every individual's shoulders.

Get in front of it, then get the benefit of it.

Most teams respond to this one of two ways: ban AI coding tools outright, or ignore it and hope. Neither works. A ban doesn't stop use, it just pushes it further out of sight. Ignoring it leaves you exposed with no idea how exposed you actually are. Tienta's approach has two phases, and the second doesn't happen without the first.

Phase 1

AI Risk & Readiness Audit

A focused, bounded engagement to answer the question you can't currently answer: what's actually happening with AI on your team right now, and where's the exposure?

Confidential interviews with engineers and leadership to surface actual (not assumed) AI tool use across the team, from coding assistants to internal chat tools
A gap assessment against your existing SOC2 trust-service commitments, customer DPAs, and IP/trade-secret obligations
A plain-language AI Use Policy and SOP, built for your team, not a generic template, covering what tools are approved, what customer data and source code can and can't be entered into them, and review requirements before AI-assisted code ships
A prioritized readiness score so leadership can see, in one page, where the team stands and what to fix first

Phase 2

Implementation & Ongoing Partnership

Once the exposure is closed, the same relationship turns toward the upside: the code review overhead, the on-call grind, and the technical debt that eats engineering hours become solvable.

The code review overhead, the on-call triage, and the technical debt that eats engineering hours, addressed as engineering problems, not policy ones
Solutions built and maintained on retainer, as your trusted technical partner rather than a one-time vendor
Everything runs on your own team's systems and accounts, so customer data and source code never leave your control

Why Tienta

We work inside your systems, not ours.

Solutions are built on accounts and infrastructure you already control. Customer data and source code never live in a third-party consultant's environment.

We speak both languages.

Twenty-plus years building and scaling technology, paired with a plain-language approach that doesn't require your leadership to become AI specialists to understand the exposure.

We're local, with a national practice.

Based in Idaho Falls, working with engineering teams here and remotely across the country. The audit runs the same either way.

This isn't a one-time audit that gets filed and forgotten.

The goal is an ongoing relationship: we close the gap, then we keep finding and solving the next thing.

Common questions

Do software companies have AI-specific compliance rules to follow?

Not a separate rulebook. There's no licensing board for software engineering the way there is for law or accounting, but the obligations are just as real: your existing SOC2 trust-service commitments, your customer data processing agreements, and ordinary trade-secret protection over source code all already govern how AI tools can be used, whether or not the team has written a policy of its own.

Does using AI coding tools put us at risk of violating our SOC2 commitments or customer DPAs?

It can, and often without anyone realizing it in the moment. SOC2's trust-service criteria and most customer DPAs specify how data may be processed and by whom; pasting customer data into a public AI tool that retains or trains on it can breach both, even when the engineer's intent was just to move faster. That's exactly the exposure the AI Risk & Readiness Audit is built to find before an auditor or a customer does.

Our team hasn't approved any AI coding tools. Does this still apply to us?

Almost certainly yes. Unapproved use is the common case, not the exception: free, capable tools meeting sprint-deadline pressure produce AI use whether or not it has been sanctioned. A team with no approved tools and no written policy typically has AI use it cannot see and no record that it did anything about it, which is the exposure the audit is designed to surface.

What does the AI Risk & Readiness Audit actually produce?

Four things: confidential interviews with engineers and leadership that document actual rather than assumed AI use, a gap assessment against your SOC2 commitments, customer DPAs, and IP obligations, a plain-language AI Use Policy and SOP written for your team rather than a generic template, and a prioritized readiness score that shows leadership in one page where the team stands and what to fix first.

Will our source code or customer data end up with a third-party consultant?

No. Solutions are built and run on accounts and infrastructure your team already controls, so source code and customer data stay inside your environment rather than living in a consultant's systems.

Isn't it safer to just ban AI coding tools at the company?

A ban doesn't stop use, it pushes it further out of sight, which removes your ability to supervise it while leaving the underlying exposure in place. The workable third option is deliberate adoption: approved tools, clear rules about what data can be entered, a defined review step before AI-assisted code ships, and clear sign-off responsibility.

Find out what's already happening on your team, before a customer, an auditor, or a breach does.

A 30-minute conversation is enough to know whether there's a gap worth closing. No cost, no obligation.

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