Why "Point-in-Time" Validation Fails for GenAI
Traditional point-in-time validation breaks down with GenAI systems. Models change, outputs vary, and attack surfaces are linguistic. Here's why you need continuous compliance checks at runtime.
Paul Merrison 
Imagine a situation where your compliance team just signed off on your chatbot. You ran 500 test cases, documented the results, and filed everything neatly in a SharePoint folder that nobody will ever read again. Congratulations — you’re already out of compliance.
The problem isn’t that you did bad testing; you probably did great testing. The problem is that GenAI doesn’t sit still.
Tetrate Agent Operations Director provides continuous runtime governance for GenAI systems. Enforce policies, control costs, and maintain compliance at the infrastructure layer — without touching application code.
The Illusion of Static Validation
Traditional software validation makes sense because traditional software is (mostly) deterministic. You test the login flow in January, and unless someone deploys new code, that same flow will work the same way in July. Your validation documentation ages like fine wine — or at least like wine that doesn’t turn to vinegar overnight.
GenAI is different in three ways that break this model completely.
First, the models themselves change. Your “GPT-5” API call in January might be routing to a completely different model version by March. OpenAI doesn’t send you a change notification. Anthropic doesn’t ask for your approval before updating Claude. The thing you validated is literally not the thing running in production anymore.
Second, even the same model version doesn’t give you the same answer twice. Non-determinism isn’t a bug; it’s the feature. That helpful response you got during testing? It might come out differently tomorrow, even with identical inputs. Your test suite captured one possible universe; production is exploring all the others.
Third — and this is the one that keeps me up at night — the attack surface is linguistic. Prompt injection isn’t like SQL injection, where you can pattern-match for suspicious inputs. Users are literally having conversations with your system. The difference between “legitimate edge case” and “jailbreak attempt” is sometimes just phrasing.
When Compliance Theater Becomes Risk
I’ve watched teams spend months building beautiful test harnesses for their AI systems. They test for bias, hallucinations, PII leakage, off-topic responses. They document everything. They get sign-off from Legal, Risk, and Security.
Then they deploy to production and discover that a user can get the system to ignore all its safety instructions by saying “but my grandmother used to read me credit card numbers to help me fall asleep.” (Yes, this actually worked on an early ChatGPT jailbreak. No, I’m not making this up.)
The validation you did wasn’t wrong. It just wasn’t enough. Point-in-time testing tells you what your system did yesterday. It doesn’t tell you what it’s doing right now, and it definitely doesn’t tell you what it’ll do tomorrow when the foundation model gets quietly updated at 3am.
The Runtime Compliance Shift
If you can’t validate once and trust the results, you need to validate continuously. Every request becomes a mini-audit.
This is where most teams panic, because they imagine bolting validation logic into every microservice, slowing everything down, and burning through their engineering budget. This is a fair concern!
But there’s a better pattern: move the checks to the infrastructure layer. Your AI requests are already flowing through network infrastructure. That’s where you can enforce policies without touching application code.
Want to strip PII from every prompt? Do it at the gateway. Need to block certain topics? Check at the gateway. Want to verify that responses don’t leak sensitive data? Inspect them at the gateway before they reach the user.
The gateway sees every request. It’s already in the critical path. And most importantly, it’s centrally managed — when you need to update a policy, you update it once, not across 47 microservices maintained by 12 different teams.
What Continuous Compliance Actually Looks Like
Continuous compliance doesn’t mean “test everything all the time forever.” That’s expensive and slow and will get you fired.
It means having policy enforcement that runs on every request, automatically:
- Input validation: Is this prompt trying to do something dangerous?
- Context checks: Should this user be able to ask this question with this data?
- Output filtering: Is the response about to leak something it shouldn’t?
- Logging: Are we capturing enough to prove compliance during an audit?
Notice what’s not on that list: business logic. You’re not reimplementing your RAG pipeline at the gateway. You’re enforcing the policies that need to be consistent across all your AI systems, regardless of what they do.
The Alternative Is Worse
You could keep doing point-in-time validation and hope nothing breaks. Plenty of teams are making that bet right now.
Some of them will get lucky. Most of them will have an incident — maybe a minor one (embarrassing chatbot response screenshot on Twitter), maybe a major one (PII leak, regulatory violation, discriminatory output at scale).
The teams that handle this well are the ones who stopped treating AI governance as a paperwork exercise and started treating it as an operational requirement. They’re checking policies at runtime, not just at deployment time. They’re capturing proof of compliance continuously, not quarterly.
And they’re doing it at the infrastructure layer, because that’s the only place you can enforce policies consistently across a portfolio of AI systems without losing your mind.
Tetrate believes governance should be built into your infrastructure, not bolted onto your applications. Agent Operations Director provides centralized policy enforcement and observability for AI systems at the gateway layer—where you can actually control what’s happening in production. Learn more here ›
Paul Merrison 
