The Meltdown Nobody's Connecting to Their Vendor Stack
Private credit defaults just hit a record 9.2%. Fund managers are slamming gates on billions in redemptions. Fortune called it a "$265 billion meltdown." And buried in the wreckage is a detail that should concern every technology leader: analysts estimate that 15 to 25 percent of the affected portfolios are concentrated in mid-market software companies.
The companies in those portfolios are the SaaS platforms your teams use every day. Your CRM. Your HR system. Your analytics stack. The tools that process your customer data, influence your hiring decisions, and power the AI features your own products depend on.
The question nobody seems to be asking: if your vendors are under financial stress because AI is eroding their competitive moats, what does that mean for the systems you've built on top of them?
Why SaaS Companies Are Defaulting
HR platforms. Vertical analytics tools. Mid-market CRMs. Project management software. Financial reporting systems. The Bank for International Settlements reports that outstanding private credit loans to software companies grew from $8 billion in 2015 to over $500 billion by end of 2025, roughly 19% of all direct lending. UBS estimates that 25 to 35 percent of private credit portfolios now carry "elevated AI disruption risk."
These companies took on significant debt during the low-rate era, betting that recurring revenue and high switching costs would protect their margins. That bet held for years. Then generative AI arrived, and the economics shifted. Capabilities that justified six-figure annual contracts started appearing as features inside foundation models or lightweight open-source tools. Not overnight, and not for every category. But enough to compress margins, slow expansion revenue, and make lenders nervous.
Software stocks dropped nearly 30% between October 2025 and February 2026. Business development companies (the funds that hold this debt) with high software exposure underperformed by five percentage points. Some borrowers are deferring interest payments rather than paying cash (a sign of severe financial stress). Others are missing their loan covenants entirely. Morgan Stanley is meeting less than half of investor redemption requests. In a worst-case scenario, UBS projects the default rate could reach 13%, roughly triple the stress level projected for traditional high-yield bonds.
This is a repricing of an entire asset class, driven in part by AI's effect on software economics.
Where Governance Enters the Picture
If this were purely a financial story, it would belong on the finance page. It isn't.
S&P Global Ratings said in March that AI's impact on software credit quality will be felt "on a case-by-case basis," not as a sector-wide downgrade. Some companies will weather this. Others won't. The question is what separates them.
The data points toward governance as an increasingly important factor in that separation. Cleary Gottlieb's 2026 Private Credit Outlook reports that lenders are tightening due diligence requirements, expecting "more detailed information requests, stricter covenant packages" for credits perceived as carrying execution risk. When lenders look harder at borrowers, what they're asking about is operational maturity: can this company document how its AI works, demonstrate compliance, and prove its business model is defensible? SAS, in its 2026 predictions, was more direct: "AI governance will separate winners from losers."
Gartner projects that the AI governance platform market will reach $492 million in 2026 and exceed $1 billion by 2030, driven by the regulatory wave bearing down on every company deploying AI. The companies investing in this infrastructure now aren't doing it for abstract reasons. They're doing it because governance is becoming a gating factor for procurement (ISO 42001, which SAP is certified against and Microsoft is auditing), for legal defensibility (the Mobley v. Workday ruling made AI vendors directly liable for algorithmic discrimination), and increasingly, for access to capital.
This affects you directly. If your HR platform uses AI for screening and that vendor can't demonstrate compliance with Colorado's AI Act (effective June 30, 2026), you inherit that risk. If your analytics tool processes customer data through AI models and that vendor can't produce an audit trail, your own compliance posture has a hole in it. And if your vendor's lenders are tightening the screws on operational maturity, the vendor's ability to keep investing in the product you depend on is tied to the same governance questions.
Seven Questions to Ask Your SaaS Vendors This Quarter
Most vendor security reviews cover SOC 2 and data residency. Almost none cover AI governance. That gap is closing fast. These questions will tell you where your vendors stand and, more importantly, where your exposure is.
1. "Can you produce a complete inventory of AI systems operating within your product?"
This is the baseline. A vendor that can't tell you which AI models are running, where, and on what data has no foundation for any other governance claim. You're looking for a specific, maintained list: model names, versions, deployment locations, data flows. If the answer is vague ("we use AI across our platform to enhance the experience"), that's a red flag.
What a good answer sounds like: "We maintain an AI system registry updated quarterly. Here's the current inventory with model versions, training data categories, and deployment contexts."
What silence means: They don't know. Which means they can't govern what they can't see.
2. "If a regulator asked how a specific AI-driven decision was made in your product last Tuesday, could you reconstruct it?"
Traceability is the core requirement of every major AI regulation. The EU AI Act (Article 12) requires automated, structured event logging with metadata. Colorado's AI Act requires documentation sufficient to support impact assessments. This question tests whether your vendor has the infrastructure to comply.
What a good answer sounds like: "Yes. We log every inference with input, output, model version, timestamp, and decision context. Retention is 12 months minimum."
What a concerning answer sounds like: "We log errors and exceptions. We don't retain routine inference data."
3. "What human oversight mechanisms exist for AI features that influence consequential decisions?"
If your vendor's product uses AI to affect hiring, credit, insurance, pricing, or service eligibility (for you or your customers), human oversight isn't optional. It's required under both the EU AI Act and Colorado's AI Act. The question is whether oversight is built into the system or exists only as a policy document.
What a good answer sounds like: "We have dashboards, alert systems, and override controls that product and compliance teams can use directly." Intervention mechanisms need to be usable by non-engineers. If the only people who can intervene in an AI decision are the ML engineers who built it, the oversight doesn't meet the regulatory standard.
What a concerning answer sounds like: "Our data science team monitors model performance." That's model management, not human oversight in the regulatory sense.
4. "How do you detect and respond to model drift or degraded performance in production?"
AI systems change behavior over time. Training data goes stale, input distributions shift, model performance degrades. A vendor with monitoring catches this before it becomes a compliance event. A vendor without it discovers the problem when a customer complains or a regulator investigates.
What a good answer sounds like: "We track accuracy, fairness metrics, and output distribution on a continuous basis. Automated alerts trigger review when metrics deviate beyond defined thresholds."
What to watch for: Vendors who rely on periodic manual review rather than continuous automated monitoring. Quarterly model reviews don't catch drift that happens in week two.
5. "What happens to our data when it enters your AI pipeline?"
This question has layers. Where is the data processed? Is it used to train or fine-tune models? Is it shared with third-party model providers? Is it retained after inference? Can it be deleted on request?
Many SaaS companies route customer data through third-party AI APIs (OpenAI, Google, Anthropic) without clearly disclosing this in their data processing agreements. If your data leaves your vendor's infrastructure and enters a model provider's system, your compliance surface just expanded to include that provider's practices.
What a good answer sounds like: "AI processing happens within our infrastructure. Customer data is never used for model training. Here's our data flow diagram for AI features."
What requires follow-up: "We use [model provider] for AI features." That's not necessarily a problem, but you need to know the terms, the data flow, and whether your own agreements with the vendor account for this.
6. "Can you provide documentation of your AI risk management practices?"
The EU AI Act requires a risk management system that's "established, documented, implemented, and maintained" (Article 9). ISO 42001 certification demonstrates this. Even without certification, a vendor should be able to produce documentation that covers risk identification, mitigation measures, testing protocols, and incident response.
What you're evaluating: Not whether the document exists, but whether it reflects how the system actually operates. A risk management document that was written for a compliance audit and never updated is worse than no document, because it creates false confidence.
7. "Have you assessed your product against the EU AI Act's risk classification and Colorado's AI Act requirements?"
This is the forward-looking question. The EU AI Act's high-risk obligations take effect August 2, 2026. Colorado goes live June 30. Seventy-three AI laws passed across 27 US states in 2025. A vendor that hasn't evaluated their product against these frameworks is going to be scrambling in Q3, and that scramble will affect your operations.
What a mature answer sounds like: "We've completed our risk classification. Here's our assessment, our compliance roadmap, and our timeline for meeting the August deadline."
What an honest but concerning answer sounds like: "We're aware of the deadlines but haven't started our assessment." Honest is better than evasive, but the clock is running.
What to Do With the Answers
These questions will sort your vendor ecosystem into three categories:
Governed. The vendor has AI governance infrastructure, can demonstrate compliance, and has a roadmap for upcoming regulatory deadlines. Low risk. These are the vendors positioned to survive the current repricing.
In Progress. The vendor is aware of the requirements and working toward them, but isn't there yet. Moderate risk. Worth monitoring quarterly and requesting a compliance update before the August deadline.
Ungoverned. The vendor can't answer these questions, or the answers reveal fundamental gaps in traceability, oversight, monitoring, or documentation. High risk. This is where your exposure lives. These vendors are both a compliance liability and, if the private credit data is any indication, a business continuity risk.
For vendors in the third category, the calculus is straightforward: either they close the governance gap on a timeline that works for you, or you start evaluating alternatives now while you have time to migrate deliberately rather than under crisis pressure.
This Isn't Just About Your Vendors
If you're reading these questions and realizing they apply to your own product too, you're not alone. Gartner estimates the AI governance market at $492 million in 2026 precisely because the gap is so wide. Only 29% of organizations have comprehensive AI governance plans. The other 71% are where most of the market is: aware of the problem, not yet sure where to start.
We've been building software in regulated industries for over a decade, and shipping agentic AI systems with governance built in since the current wave of regulation began. The pattern we see consistently is that teams overestimate the cost of building governance in from the start and underestimate the cost of retrofitting it under deadline pressure. The infrastructure is familiar engineering work: logging, versioning, oversight workflows, monitoring, documentation. Built into your architecture, it's a rounding error on development cost. Added in Q3 under regulatory pressure, it's a program.
We put together a readiness guide that covers the regulatory landscape, a self-assessment framework, and practical next steps. It takes about 15 minutes to work through.
Get the AI Governance Readiness Guide →
If you'd rather talk it through, we offer a 30-minute governance review. We'll walk through your current architecture, identify gaps, and give you a clear picture of where you stand. No pitch, no pressure.