Why ChatGPT is Not EU AI Act Compliance Assessment — and Which Method to Use Instead

Why do companies turn to ChatGPT to "audit" AI compliance?

The pressure for speed in compliance analysis is real. Procurement, legal, and IT teams face queues of vendors to evaluate, and deadlines don't wait. In this scenario, it's tempting to copy a 40-page AI governance policy, paste it into ChatGPT, and ask: "Is this vendor compliant with the EU AI Act?"

The answer comes in seconds. It seems practical. But there's a critical problem: this answer has no methodology, no traceable evidence, and changes with each new question.

For corporate contexts — where decisions need to be defensible, reproducible, and auditable — this isn't enough. Compliance cannot depend on answers that vary by day or model version.

What makes an AI chat inadequate for corporate AI compliance analysis?

Absence of reproducibility: different results with each query

One of the most evident problems with AI chats is the lack of consistency. Ask the same question three times about the same document and you might receive three slightly different answers. This happens because language models work with probability, not deterministic logic.

In a corporate environment, this is unacceptable. If you need to defend a vendor choice before a committee, an audit, or a regulatory body, the analysis must be identical and replicable. Variations between answers create legal uncertainty and undermine confidence in the decision.

Lack of citable and dated evidence

When ChatGPT (or any AI chat) responds that "the vendor doesn't specify the legal basis for AI system deployment," where's the proof? Which section of the document was analyzed? What's the date of the version consulted? What's the official URL?

This information doesn't exist in a chat's standard response. And without it, you can't:

• Show the vendor exactly where the gap is
• Record the analysis for future reference
• Prove the evaluation was conducted rigorously
• Track changes over time

In internal or external audits, the absence of dated evidence and official sources can invalidate the entire analysis.

Versions and updates without control

AI governance policies change. Vendors update terms, include new AI systems, alter legal bases. If you conducted an analysis in March and the vendor changed the policy in June, how do you detect this change using an AI chat?

There's no history. No versioning. No alerts. You need to remember to redo the analysis manually — which rarely happens at corporate scale.

Inconsistency between evaluators

If three different analysts use ChatGPT to evaluate the same vendor, with slightly different prompts, conclusions may diverge. One might focus more on high-risk AI systems. Another might emphasize transparency obligations. The third might give more weight to conformity assessment procedures.

Without a standardized framework, each person extracts different conclusions from the same document. This generates unproductive internal debates, decision delays, and lack of alignment between areas.

Why "asking better questions" to the chat doesn't solve the problem?

Some argue that the problem lies in prompt quality, not the tool. "Just ask more specific questions," they say. But this solution ignores corporate reality.

Medium and large companies evaluate dozens or hundreds of vendors per year. Each needs to pass through the same criteria, be comparable with competitors, and generate auditable records. Depending on each analyst's individual skill in "asking the right question" isn't governance — it's improvisation.

Moreover, even well-crafted prompts don't solve:

• The absence of evidence with URL and date
• The lack of versioning and history
• Probabilistic variation between answers
• The impossibility of consistent benchmarking

What's the correct corporate method for evaluating vendor AI compliance?

Base exclusively on public and dated evidence

The first principle of a solid corporate method is working only with official and publicly available sources: AI governance policies, terms of use, help center pages, declarations on official websites.

Each evaluated criterion should point to:

• Official URL where information was found (or absence recorded)
• Date of evidence collection
• Document version when available

This ensures traceability. If an auditor questions a conclusion, you show exactly where it came from.

Use standardized and reproducible criteria

Analysis cannot depend on subjective interpretations. It's essential to have a fixed framework of criteria based on recognized regulations (EU AI Act, GDPR) and international standards (ISO/IEC 42001, ISO/IEC 23894 for AI).

Each criterion should have:

• Objective question (e.g., "Does the policy identify the AI Officer?")
• Binary or categorical answer (YES/NO, or maturity levels)
• Public evidence that proves the answer

This way, different analysts reach the same conclusion when evaluating the same vendor.

Record and version all analyses

Every evaluation should generate a permanent record with:

• Final score or index
• Breakdown by criterion groups (AI Officer, transparency, high-risk systems, conformity assessment, etc.)
• Evidence per criterion (URLs, dates, relevant excerpts)
• Analysis date
• Version of the evaluated policy

When the vendor updates their documentation, you can compare versions and identify if there was improvement, deterioration, or introduction of new risks.

Automate continuous monitoring

Policies change without prior notice. An effective corporate method includes automatic alerts when:

• The AI governance policy is updated
• New AI systems are included
• Data breach incidents are publicly reported
• Changes in AI practices are announced

This prevents your company from continuing to use a vendor whose risk profile has silently changed.

How does AITS (AI Trust Score) materialize this method?

AITS is an index that measures transparency of AI governance based exclusively on public information. It operates with 20 standardized criteria, aligned with EU AI Act, GDPR, CCPA, and ISO AI standards (ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 42005).

Adaptive structure: traditional privacy + AI governance

• Criteria 1-8: AI Governance
• Criteria 9-20: Traditional Privacy

Each criterion receives YES (clear information publicly available) or NO (absent or unclear). The result is a transparency score that indicates public communication maturity — not internal implementation, but documentary clarity.

Traceable public evidence

Every AITS evaluation records:

• Official URL of each analyzed document
• Analysis date
• Policy version (when available)
• Specific excerpts that prove each answer

This allows anyone — auditors, vendors, internal committees — to validate the analysis independently.

Reproducibility and consistency

Two analysts evaluating the same vendor with AITS reach the same result. The method eliminates interpretative variability because criteria are objective and evidence is public.

Comparability and benchmarking

Since all vendors are evaluated by the same criteria, you can:

• Compare competitors side by side
• Identify transparency leaders by category
• Evaluate if a vendor is above or below market average
• Detect common gaps and contractual negotiation opportunities

When to use AI to accelerate analyses (the right way)

This doesn't mean AI is useless for compliance analysis. On the contrary: AI is essential for processing documents at scale, as long as it's used within a structured method.

Trust This uses specialized AI pipeline (Gemini, Claude, DeepSeek) to:

• Process AI governance policies and identify information about the 86 criteria
• Detect AI use in evaluated software
• Extract relevant textual evidence for each criterion
• Validate consistency between multiple sources

But the difference lies in the method: AI is a tool, not a substitute. Each AI-generated response is cross-referenced with public, dated, and auditable evidence. The final result isn't a "model opinion," but an analysis based on verifiable facts.

What are the risks of continuing to use AI chats as "compliance assessment"?

Regulatory risk: weak justifications in audits

Regulatory bodies (European Data Protection Board, national AI authorities) may question your vendor choices. If you base decisions on inconsistent responses without evidence from a chat, your defense will be weak.

Operational risk: decisions that don't survive committees

When you present a vendor recommendation to an executive committee, someone will ask: "How did you reach this conclusion?" If the answer is "I asked ChatGPT," confidence plummets.

Rework risk: analyses that need to be redone

Without a standardized method, analyses done by different people at different times may contradict each other. This generates unproductive debates, delays, and constant rework needs.

Reputational risk: poorly founded choices

If you contract a vendor based on weak analysis and they cause an AI compliance incident, the investigation will question: "How was the evaluation conducted?" Improvised methods don't protect your company — nor your professional reputation.

What changes when adopting a public evidence-based method?

Speed with governance

You can evaluate vendors in minutes — but with auditable records, standardized criteria, and versioned history. You don't need to choose between speed and rigor.

Defensible decisions

When someone questions your choice, you point to public evidence, dates, URLs, and objective scores. The analysis defends itself.

Real comparability

You can benchmark competitors, identify transparency leaders, and use objective data to break ties between "similar" vendors.

Continuous monitoring

Policies change, incidents happen. With automatic alerts, you act proactively instead of discovering problems too late.

Why Trust This never calls its analyses "audits"?

Because audit implies access to internal systems, technical controls, operational processes. AITS evaluates transparency of public communication, not actual implementation.

This distinction is fundamental. AITS doesn't replace technical audits, security assessments, or in-depth contractual due diligence. It's an initial screening tool — fast, objective, and scalable — that prepares the ground for deeper analyses when necessary.

But unlike an AI chat, AITS generates results that:

• Are reproducible
• Have traceable evidence
• Allow comparison between vendors
• Record change history
• Survive audits and committees

How to start using evidence-based method in your company?

Define objective criteria for your organization

List the AI compliance and governance aspects that are critical for your company. Use recognized frameworks (EU AI Act, GDPR, ISO/IEC 42001) as a base, adapting to your regulatory and sectoral context.

Record all analyses with public evidence

Create an internal repository where each vendor evaluation contains: score, evaluated criteria, evidence (URLs, dates), analyzed policy version. This builds history and enables comparisons over time.

Automate change monitoring

Set up alerts to detect updates in AI governance policies, new public incidents, and changes in AI practices of your critical vendors. Don't wait to discover changes by chance.

Train teams with method, not just tools

Train procurement, legal, IT, and compliance teams in the method: how to search for public evidence, how to interpret objective criteria, how to record analyses auditably. The method should be independent of specific tools.

Conclusion

AI compliance decisions cannot depend on answers that change with each question. In corporate contexts, you need method, traceable evidence, reproducibility, and governance. AI chats are powerful — but only work when inserted into structured frameworks, not as method substitutes.

Trust This offers ready AI compliance analyses with AITS, allowing your company to evaluate vendors in minutes with public evidence, standardized scores, and versioned history. Want to see how it works? Explore the AITS analysis catalog and compare vendors with solid corporate methodology.

SUGGESTED IMAGES FOR CONTENT:

Comparative infographic: Side-by-side table showing "AI Chat" vs "AITS Method" in criteria like reproducibility, traceable evidence, versioning, benchmarking, analysis time. Use green check and red X icons.

Method flowchart: Illustration of evidence-based analysis pipeline: (1) Public document collection → (2) Standardized criteria evaluation → (3) Evidence recording with URL/date → (4) Score and breakdown → (5) Continuous monitoring.

Traceable evidence example: Simulated screenshot showing how AITS evidence is recorded: "Criterion 15: Legal basis for AI deployment | Answer: YES | Evidence: [URL] | Date: 15/10/2024 | Excerpt: 'Our legal basis is legitimate interest...'"