You have an AI compliance checklist.
It probably covers the obvious stuff: data privacy, model documentation, maybe some bias testing protocols.
But here’s what I see when I review these checklists: they’re built for yesterday’s regulatory environment, not tomorrow’s enforcement actions.
The EU AI Act is live. Colorado’s AI law has teeth. And state attorneys general are actively investigating AI systems right now.
If your compliance checklist was built before 2024, it’s already outdated. And if it’s missing these five requirements, you’re exposed.
1. Third-Party AI System Inventory (Not Just Your Own Models)
Most companies track the AI they build internally. Almost nobody tracks the AI they’re buying.
Here’s the problem: if you’re using a vendor’s AI system, you’re still liable for its outputs.
Your customer-facing chatbot runs on a third-party LLM? You’re responsible for what it says.
Does your HR team use an AI screening tool? You’re on the hook if it discriminates.
Did your marketing department buy an AI content generator? You own the compliance risk.
What regulators actually want to see:
- Complete inventory of ALL AI systems (built, bought, and embedded)
- Vendor due diligence documentation
- Contractual liability provisions
- Ongoing monitoring of third-party AI performance
Why most checklists miss this: They were created when companies only worried about their own AI development. Now AI is embedded in every SaaS tool your teams use.
2. Human Oversight Documentation (With Proof It Actually Happens)
Every AI policy says “human oversight required.”
Almost none of them prove it’s actually happening.
The EU AI Act and Colorado law both require meaningful human oversight for high-risk AI systems. Not just a checkbox. Not just a policy statement. Actual documented evidence that humans are reviewing AI decisions.
What regulators will ask for:
- Who reviews AI outputs before they affect people?
- How often do reviews happen?
- What authority do human reviewers have to override AI decisions?
- What training do reviewers receive?
- Can you show me the last 30 days of review records?
If your answer to any of these is “well, we have a policy that says…” you don’t have compliance. You have an aspiration.
What actually works:
- Named individuals with specific oversight responsibility
- Documented review schedules (and evidence they’re followed)
- Clear escalation procedures when AI outputs are questioned
- Regular audits of whether oversight is actually happening
3. Impact Assessments That Address Real-World Harms (Not Just Technical Risks)
I see a lot of AI risk assessments that focus on technical failure modes: model drift, accuracy degradation, and system downtime.
Those matters. But that’s not what gets you sued or investigated.
What actually triggers regulatory action:
- Discrimination in hiring, lending, and housing
- Privacy violations
- Unfair treatment of protected groups
- Lack of transparency about AI decision-making
- Inability to appeal or challenge AI decisions
Your impact assessment needs to answer: who could be harmed by this AI system, and how?
Not in theory. In practice.
Questions your assessment must answer:
- Does this system treat people differently based on protected characteristics?
- Can someone challenge or appeal an AI decision?
- Do we have processes to identify and fix bias?
- What happens when the AI gets it wrong?
- Who’s most likely to be negatively affected?
If your risk assessment reads like a technical manual instead of an ethical framework, you’re missing the point.
4. Ongoing Monitoring (Not Just Pre-Deployment Testing)
Here’s a conversation I have constantly:
Me: “How do you monitor your AI systems after deployment?”
Client: “We tested it before launch.”
Me: “That’s not what I asked.”
AI systems drift. Data changes. Edge cases emerge. What worked in testing might fail in production.
The regulatory expectation is continuous monitoring:
- Performance metrics tracked over time
- Bias testing on real-world outputs (not just training data)
- Regular audits of AI decisions
- Incident tracking when things go wrong
What regulators will want to see:
- Monitoring dashboards showing ongoing performance
- Evidence that you catch and fix problems
- Documentation of model updates and why they were made
- Records of how you respond to AI failures
If you can’t produce monitoring records from the last 90 days, you’re not monitoring. You’re hoping.
5. Explainability Mechanisms (That Non-Technical People Can Actually Understand)
Every company says its AI is “explainable.” Then they show me a technical white paper that even their own data scientists struggle to read.
Here’s the test: can your VP of HR explain how your hiring AI works?
Not the architecture. Not the algorithm. The actual decision-making process.
Because when someone files a discrimination complaint, your data science team isn’t going to be in that meeting. Your business leaders are. And they need to be able to explain:
- What factors does the AI consider
- What factors doesn’t it consider
- How it weighs different inputs
- Why was a specific decision
What actually meets explainability requirements:
- Plain-language explanations of how AI systems work
- Individual decision explanations (not just system-level documentation)
- Process for providing explanations to affected individuals
- Training for business teams on how to explain AI decisions
If your explainability documentation requires a PhD to understand, it doesn’t meet the standard.
Why These Requirements Aren’t On Your Current Checklist
Most AI compliance checklists were created by one of three groups:
- Legal teams that understand regulations but not AI operations
- Technical teams that understand AI but not regulatory expectations
- Consultants who copied frameworks from other domains (data privacy, cybersecurity) and slapped “AI” on them
None of these approaches work.
Real AI compliance requires three things together:
- Legal expertise (knowing what regulations require)
- Technical expertise (knowing how AI actually works)
- Operational expertise (knowing how to implement controls that don’t break everything)
At VisionAI+, that’s exactly what we built. Our team includes former AI company general counsel, certified ISO 42001 auditors, and compliance specialists who’ve implemented these requirements across dozens of organizations.
We don’t just tell you what compliance looks like. We build it with you.
How to Actually Fix Your Compliance Checklist
If you’re reading this and realizing your checklist has gaps, here’s how to address them:
Step 1: Audit what you actually have (not what your policies say you should have)
Pull your current AI inventory. Compare it to your vendor contracts. Check whether anyone’s actually monitoring your production systems. Look at your last 10 risk assessments and see if they address real-world harms.
Step 2: Identify the highest-risk gaps first
Not every AI system carries the same risk. Focus on:
- Customer-facing AI that makes decisions about people
- AI used in hiring, lending, or other regulated domains
- High-volume AI systems where small error rates affect many people
- Third-party AI where you don’t control the underlying system
Step 3: Build evidence, not just policies
Stop writing more policy documents. Start creating systems that generate evidence:
- Monitoring dashboards
- Review logs
- Impact assessments tied to specific systems
- Vendor due diligence files
- Training records
Step 4: Test whether it actually works
Run a tabletop exercise: regulators show up tomorrow asking for documentation. Can you produce it in 48 hours?
If the answer is “we’d need to pull it together from multiple sources” or “we’d need to ask the vendor,” your compliance program isn’t ready.
Stop Building Checklists, Start Building Programs
Most companies aren’t failing because they don’t know what compliance requires. They’re failing because they’re treating compliance like a checklist instead of a program.
Checklists are static. Programs are operational.
Checklists sit in documents. Programs run in systems.
Checklists make executives feel good. Programs survive regulatory scrutiny.
If your AI compliance strategy is “we have a checklist,” you don’t have compliance. You have exposure.
Schedule a 30-minute AI Compliance Gap Analysis and we’ll show you:
- The specific gaps in your current compliance program
- Which gaps carry the highest regulatory risk
- What it actually takes to move from checklist to program
- A roadmap for building compliance that holds up under scrutiny
No sales pitch. No generic framework. Just an honest assessment of where you stand and what it takes to get ready for enforcement.
Because regulators aren’t checking whether you have a checklist. They’re checking whether you can prove your AI systems are actually compliant.
The question is, can you?
Brian Alexander
Founding Partner, VisionAI+ Consulting Group
Former General Counsel, AI Engineering Firm
Certified ISO 42001 Lead Auditor
brian@visionaiconsult.com | 877-424-7853