Stats and Global Laws for SaaS Teams

In 2024, an enforcement case over facial-recognition data resulted in a €30.5M fine for Clearview AI. For context, that is roughly equivalent to the annual cost of employing about 400 senior engineers in San Francisco. Now imagine losing that much overnight, not because of real business, but because you were not compliant enough as your AI evidence trail breaks down, and just like that, suddenly, in 2025, the possibility of “regulatory risk” stops being hypothetical.

This shift has increased demand for AI governance software, particularly among enterprise-focused SaaS vendors.  Meanwhile, AI adoption is racing ahead, as in 2025, nearly 79% companies prioritize AI capabilities in their software selection. But the AI governance structures? Lagging badly behind. The result: longer deal closures, product launch delays, and nervous legal teams blocking features.

In this guide, we’ve compiled the regulations shaping 2026, the proof buyers consistently request, and the steps your SaaS company can use to keep launches and deals moving.

Delinea’s 2025 report adds that 44% of organizations have business units deploying AI without involving security teams. These tools may be helpful internally, but if an unsanctioned AI tool mishandles customer data, you won’t know until a buyer’s security audit surfaces it—or worse, until there’s an incident. At that point, “we didn’t know” wouldn’t be a good defense. It’s a disqualifier.

How to fix:  Run an internal AI inventor. Start with IT and security logs, then survey the department heads on what tools their teams actually use. Decide whether to bring each tool under governance or phase it out. You can’t answer buyer questions confidently if you don’t know what’s running.

3.  Overlooking third-party AI risk

SaaS third-party vendors are part of your stack, which means their risk is your risk.
 ACA Group’s 2025 AI Benchmarking Survey found that only 24% of firms have policies governing the use of third-party AI, and just 43% perform enhanced due diligence on AI vendors. If a third-party AI vendor you rely on has a data breach, bias incident, or compliance failure, you’re on the hook — not them. Buyers wouldn’t care where the AI came from. They’ll see your product, your name, and your liability. 

How to fix: Add AI-specific questions to your vendor assessments. Ask about governance frameworks, data handling practices, and certifications like ISO 42001. If you can answer these questions about your own vendors, you’ll be better positioned when your buyers ask them about you.

4.  Letting documentation fall behind

Model cards, data lineage records, and training documentation will be requirements under the EU AI Act. But many teams haven’t prioritized them yet. A Nature Machine Intelligence study analyzing 32,000+ AI model cards found that even when documentation exists, sections covering limitations and evaluation had the lowest completion rates, the exact areas buyers and regulators scrutinize most.

How to fix:  Require model cards to pass review before any release goes live. Include training data sources, known limitations, and bias test results—the exact fields buyers ask for in security questionnaires.

Organizations that assign clear AI governance ownership move faster, not slower. IBM’s 2025 research across 1,000 senior leaders found that 27% of AI efficiency gains come directly from strong governance — and companies with mature oversight are 81% more likely to have CEO-level involvement driving accountability. The pattern is clear: when someone owns AI decisions, teams ship with confidence instead of stalling for approvals.

Start lean. Publish a short AI policy that names specific owners across product, legal, and security, not a committee, but individuals with authority to act. Review quarterly as regulations evolve, and build in a clear escalation path for edge cases. The goal isn’t bureaucracy; it’s removing the friction that comes when nobody knows who’s responsible.

2. Build a living AI inventory and risk register

Organizations that centralize their AI data and track use cases move pilots to production four times faster. Cisco’s 2025 AI Readiness Index found that 76% of top-performing companies (“Pacesetters”) have fully centralized data infrastructure, compared to just 19% overall— and 95% of them actively track the impact of every AI investment. That visibility is what lets them scale while others stall.

Create a shared inventory tracking every AI use case: product features, third-party APIs, and internal automation. Map each to a risk tier using EU AI Act categories as your baseline (minimal, limited, high, unacceptable). Update it with every sprint, and don’t do it just quarterly. The companies pulling ahead treat this as a living document, not an occasional compliance check.

3. Adopt a management system that customers recognize

Adopting a management system here means grounding your AI governance in a standard that customers already know how to evaluate. ISO/IEC 42001 (published December 2023) is the first AI-specific management system standard designed for that purpose.

Using ISO/IEC 42001 as the reference will let you answer AI governance questions by pointing to defined controls instead of custom explanations. Reviewers can see how ownership, risk management, monitoring, and documentation are handled without follow-up calls or extra evidence requests. 

4. Fix data readiness before it stalls features

43% of organizations identify data quality and readiness as their top obstacle to AI success, and 87% of AI projects never reach production with poor data quality as the primary culprit. Failed projects trace back to missing lineage, unclear consent records, or training sources you can’t verify when buyers ask.

How to fix it: Define minimum data standards (source documentation, user consent, retention policy, full lineage) and make them release blockers in CI/CD. If the data story isn’t clean, the feature doesn’t ship. This prevents expensive rework during security reviews when you can’t answer basic provenance questions.

5. Add product gates that prevent expensive work

You often discover AI compliance gaps after your team has already committed engineering resources. Features move into production, then slow down during security reviews, procurement questionnaires, or internal risk checks when governance evidence is missing. Pacific AI’s 2025 AI Governance Survey explains why this continues to happen: 45% of organizations prioritize speed to market over governance. When oversight gets deferred, you absorb the cost later through rework, retroactive controls, delayed launches, and blocked deals.

The impact shows up in longer release cycles, stalled approvals, and slower expansion motions.

How to fix it: Add a compliance gate to releases: bias test results, audit logs, human oversight mechanisms, and rollback plans required before launch. Ship once, not twice.